SVM Classification Model -Large Data SET.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "95455eec",
   "metadata": {},
   "source": [
    "# Support Vector Machine - Extended Data Set\n",
    "\n",
    "- Looking at the Learning curves for SVM on 240 30 seconds and 480 15 second clips , looked like cv accuracy was going up with data set size, suggests more DATA will help the model to generalize better\n",
    "\n",
    "- DATA SOURCE : KAGGLE [MusicNet Midi FILES](https://www.kaggle.com/datasets/rohitsingh0210/musicnet-midi-files?select=musicnet_midis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a2b49b4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm, datasets\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe13964d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Range</th>\n",
       "      <th>PitchVariety</th>\n",
       "      <th>NumberOfCommonPitches</th>\n",
       "      <th>NoteDensity</th>\n",
       "      <th>AmountOfArpeggiation</th>\n",
       "      <th>IntervalBetweenStrongestPitches</th>\n",
       "      <th>MostCommonPitchClassPrevalence</th>\n",
       "      <th>MostCommonPitchPrevalence</th>\n",
       "      <th>RelativeStrengthOfTopPitchClasses</th>\n",
       "      <th>RepeatedNotes</th>\n",
       "      <th>MelodicOctaves</th>\n",
       "      <th>SizeOfMelodicArcs</th>\n",
       "      <th>VariabilityOfNoteDuration</th>\n",
       "      <th>MelodicTritones</th>\n",
       "      <th>PitchClassVariety</th>\n",
       "      <th>Composer_name</th>\n",
       "      <th>Composer_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.7500</td>\n",
       "      <td>0.221198</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.145455</td>\n",
       "      <td>0.104545</td>\n",
       "      <td>0.968750</td>\n",
       "      <td>0.013825</td>\n",
       "      <td>0.004608</td>\n",
       "      <td>5.340206</td>\n",
       "      <td>0.171883</td>\n",
       "      <td>0.018433</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Bach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.3125</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.173709</td>\n",
       "      <td>0.079812</td>\n",
       "      <td>0.945946</td>\n",
       "      <td>0.009524</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>5.764045</td>\n",
       "      <td>0.136698</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Bach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.7500</td>\n",
       "      <td>0.221198</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.145455</td>\n",
       "      <td>0.104545</td>\n",
       "      <td>0.968750</td>\n",
       "      <td>0.013825</td>\n",
       "      <td>0.004608</td>\n",
       "      <td>5.340206</td>\n",
       "      <td>0.171883</td>\n",
       "      <td>0.018433</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Bach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.6875</td>\n",
       "      <td>0.189815</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.164384</td>\n",
       "      <td>0.086758</td>\n",
       "      <td>0.972222</td>\n",
       "      <td>0.009259</td>\n",
       "      <td>0.009259</td>\n",
       "      <td>5.069307</td>\n",
       "      <td>0.176578</td>\n",
       "      <td>0.004630</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Bach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.4375</td>\n",
       "      <td>0.231132</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.176744</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.921053</td>\n",
       "      <td>0.009434</td>\n",
       "      <td>0.004717</td>\n",
       "      <td>5.684211</td>\n",
       "      <td>0.141548</td>\n",
       "      <td>0.014151</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Bach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Range  PitchVariety  NumberOfCommonPitches  NoteDensity  \\\n",
       "0   39.0          30.0                    3.0      13.7500   \n",
       "1   39.0          32.0                    0.0      13.3125   \n",
       "2   39.0          30.0                    3.0      13.7500   \n",
       "3   36.0          27.0                    0.0      13.6875   \n",
       "4   39.0          32.0                    0.0      13.4375   \n",
       "\n",
       "   AmountOfArpeggiation  IntervalBetweenStrongestPitches  \\\n",
       "0              0.221198                              2.0   \n",
       "1              0.214286                             17.0   \n",
       "2              0.221198                              2.0   \n",
       "3              0.189815                              1.0   \n",
       "4              0.231132                             17.0   \n",
       "\n",
       "   MostCommonPitchClassPrevalence  MostCommonPitchPrevalence  \\\n",
       "0                        0.145455                   0.104545   \n",
       "1                        0.173709                   0.079812   \n",
       "2                        0.145455                   0.104545   \n",
       "3                        0.164384                   0.086758   \n",
       "4                        0.176744                   0.079070   \n",
       "\n",
       "   RelativeStrengthOfTopPitchClasses  RepeatedNotes  MelodicOctaves  \\\n",
       "0                           0.968750       0.013825        0.004608   \n",
       "1                           0.945946       0.009524        0.004762   \n",
       "2                           0.968750       0.013825        0.004608   \n",
       "3                           0.972222       0.009259        0.009259   \n",
       "4                           0.921053       0.009434        0.004717   \n",
       "\n",
       "   SizeOfMelodicArcs  VariabilityOfNoteDuration  MelodicTritones  \\\n",
       "0           5.340206                   0.171883         0.018433   \n",
       "1           5.764045                   0.136698         0.004762   \n",
       "2           5.340206                   0.171883         0.018433   \n",
       "3           5.069307                   0.176578         0.004630   \n",
       "4           5.684211                   0.141548         0.014151   \n",
       "\n",
       "   PitchClassVariety Composer_name  Composer_label  \n",
       "0               11.0          Bach             0.0  \n",
       "1               11.0          Bach             0.0  \n",
       "2               11.0          Bach             0.0  \n",
       "3               10.0          Bach             0.0  \n",
       "4               11.0          Bach             0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ADDITIONAL DATA SET: \n",
    "\n",
    "data_dir = './DataStreamFeatures/DataFrames/'\n",
    "\n",
    "df_best_features = pd.read_csv(data_dir + 'BestMusicFeatures_15seconds_MusicNet_Kaggle_313.csv')\n",
    "\n",
    "df_best_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1da6a90d",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Range</th>\n",
       "      <th>PitchVariety</th>\n",
       "      <th>NumberOfCommonPitches</th>\n",
       "      <th>NoteDensity</th>\n",
       "      <th>AmountOfArpeggiation</th>\n",
       "      <th>IntervalBetweenStrongestPitches</th>\n",
       "      <th>MostCommonPitchClassPrevalence</th>\n",
       "      <th>MostCommonPitchPrevalence</th>\n",
       "      <th>RelativeStrengthOfTopPitchClasses</th>\n",
       "      <th>RepeatedNotes</th>\n",
       "      <th>MelodicOctaves</th>\n",
       "      <th>SizeOfMelodicArcs</th>\n",
       "      <th>VariabilityOfNoteDuration</th>\n",
       "      <th>MelodicTritones</th>\n",
       "      <th>PitchClassVariety</th>\n",
       "      <th>Composer_name</th>\n",
       "      <th>Composer_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.238095</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.348214</td>\n",
       "      <td>0.008929</td>\n",
       "      <td>6.216216</td>\n",
       "      <td>0.089537</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Bach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.269841</td>\n",
       "      <td>0.304878</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.204819</td>\n",
       "      <td>0.144578</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>0.048780</td>\n",
       "      <td>8.261905</td>\n",
       "      <td>0.070927</td>\n",
       "      <td>0.012195</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Bach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.730159</td>\n",
       "      <td>0.432692</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.264151</td>\n",
       "      <td>0.188679</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.096154</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>7.583333</td>\n",
       "      <td>0.091070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Bach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.365079</td>\n",
       "      <td>0.508772</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.336207</td>\n",
       "      <td>0.301724</td>\n",
       "      <td>0.717949</td>\n",
       "      <td>0.412281</td>\n",
       "      <td>0.008772</td>\n",
       "      <td>5.437500</td>\n",
       "      <td>0.083497</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Bach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.047619</td>\n",
       "      <td>0.513761</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.315315</td>\n",
       "      <td>0.252252</td>\n",
       "      <td>0.657143</td>\n",
       "      <td>0.284404</td>\n",
       "      <td>0.009174</td>\n",
       "      <td>6.944444</td>\n",
       "      <td>0.089949</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Bach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Range  PitchVariety  NumberOfCommonPitches  NoteDensity  \\\n",
       "0   24.0          18.0                    3.0     7.238095   \n",
       "1   22.0          15.0                    4.0     5.269841   \n",
       "2   31.0          21.0                    2.0     6.730159   \n",
       "3   24.0          17.0                    2.0     7.365079   \n",
       "4   24.0          18.0                    3.0     7.047619   \n",
       "\n",
       "   AmountOfArpeggiation  IntervalBetweenStrongestPitches  \\\n",
       "0              0.562500                              7.0   \n",
       "1              0.304878                              1.0   \n",
       "2              0.432692                              7.0   \n",
       "3              0.508772                              7.0   \n",
       "4              0.513761                              7.0   \n",
       "\n",
       "   MostCommonPitchClassPrevalence  MostCommonPitchPrevalence  \\\n",
       "0                        0.315789                   0.263158   \n",
       "1                        0.204819                   0.144578   \n",
       "2                        0.264151                   0.188679   \n",
       "3                        0.336207                   0.301724   \n",
       "4                        0.315315                   0.252252   \n",
       "\n",
       "   RelativeStrengthOfTopPitchClasses  RepeatedNotes  MelodicOctaves  \\\n",
       "0                           0.777778       0.348214        0.008929   \n",
       "1                           0.882353       0.024390        0.048780   \n",
       "2                           0.535714       0.096154        0.009615   \n",
       "3                           0.717949       0.412281        0.008772   \n",
       "4                           0.657143       0.284404        0.009174   \n",
       "\n",
       "   SizeOfMelodicArcs  VariabilityOfNoteDuration  MelodicTritones  \\\n",
       "0           6.216216                   0.089537         0.000000   \n",
       "1           8.261905                   0.070927         0.012195   \n",
       "2           7.583333                   0.091070         0.000000   \n",
       "3           5.437500                   0.083497         0.000000   \n",
       "4           6.944444                   0.089949         0.000000   \n",
       "\n",
       "   PitchClassVariety Composer_name  Composer_label  \n",
       "0               11.0          Bach             0.0  \n",
       "1                8.0          Bach             0.0  \n",
       "2               12.0          Bach             0.0  \n",
       "3               10.0          Bach             0.0  \n",
       "4               11.0          Bach             0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = './DataStreamFeatures/DataFrames/'\n",
    "\n",
    "df_best_features = pd.concat([pd.read_csv(data_dir + 'BestMusicFeatures_15seconds.csv'),df_best_features])\n",
    "\n",
    "df_best_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0334e9b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "793 samples with 15 features each <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "X = df_best_features.drop(['Composer_name', 'Composer_label'], axis=1)\n",
    "\n",
    "training_features_list = list(X.columns)\n",
    "\n",
    "X = X.to_numpy()\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "print(f'{m} samples with {n} features each',type(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed75210b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(793,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df_best_features['Composer_label'].to_numpy()\n",
    "\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e5f9b3c",
   "metadata": {},
   "source": [
    "# First Trial, take only two features, can then make cool Scatter plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7059bd05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "793 samples with 15 features each <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "X = df_best_features.drop(['Composer_name', 'Composer_label'], axis=1)\n",
    "\n",
    "training_features_list = list(X.columns)\n",
    "\n",
    "X = X.to_numpy()\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "print(f'{m} samples with {n} features each',type(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5fe9bb8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(793, 15)\n",
      "(793, 2)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "\n",
    "X = X[:,:2]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.85, random_state = 0)\n",
    "\n",
    "# Scaling the Train and Test feature set \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "X_scaled = scaler.transform(X)\n",
    "\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33df8e30",
   "metadata": {},
   "source": [
    "Test different kernals some statring parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d570fb71",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear = svm.SVC(kernel='linear', C=1000, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "rbf = svm.SVC(kernel='rbf', gamma=0.1, C=1000, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "poly = svm.SVC(kernel='poly', degree=3, C=1000, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "sig = svm.SVC(kernel='sigmoid', C=1000, decision_function_shape='ovo').fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9cf649d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#stepsize in the mesh, it alters the accuracy of the plotprint\n",
    "#to better understand it, just play with the value, change it and print it\n",
    "h = .05\n",
    "#create the mesh\n",
    "x_min, x_max = X_scaled[:, 0].min() - 1, X_scaled[:, 0].max() + 1\n",
    "y_min, y_max = X_scaled[:, 1].min() - 1, X_scaled[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),np.arange(y_min, y_max, h))\n",
    "# create the title that will be shown on the plot\n",
    "titles = ['Linear kernel','RBF kernel','Polynomial kernel','Sigmoid kernel']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d08aae10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, clf in enumerate((linear, rbf, poly, sig)):\n",
    "    #defines how many plots: 2 rows, 2columns=> leading to 4 plots\n",
    "    plt.subplot(2, 2, i + 1) #i+1 is the index\n",
    "    #space between plots\n",
    "    plt.subplots_adjust(wspace=0.4, hspace=0.4) \n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    # Put the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.PuBuGn, alpha=0.7)\n",
    "    # Plot also the training points\n",
    "    plt.scatter(X_scaled[::3, 0], X_scaled[::3, 1], c=y[::3], cmap=plt.cm.PuBuGn,     edgecolors='grey')\n",
    "    plt.xlabel(training_features_list[0])\n",
    "    plt.ylabel(training_features_list[1])\n",
    "    plt.xlim(xx.min(), xx.max())\n",
    "    plt.ylim(yy.min(), yy.max())\n",
    "    plt.xticks(())\n",
    "    plt.yticks(())\n",
    "    plt.title(titles[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8285e87f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Linear Kernel: 0.42016806722689076\n",
      "Accuracy Polynomial Kernel: 0.3949579831932773\n",
      "Accuracy Radial Basis Kernel: 0.5126050420168067\n",
      "Accuracy Sigmoid Kernel: 0.36134453781512604\n"
     ]
    }
   ],
   "source": [
    "# retrieve the accuracy and print it for all 4 kernel functions\n",
    "accuracy_lin = linear.score(X_test, y_test)\n",
    "accuracy_poly = poly.score(X_test, y_test)\n",
    "accuracy_rbf = rbf.score(X_test, y_test)\n",
    "accuracy_sig = sig.score(X_test, y_test)\n",
    "print('Accuracy Linear Kernel:', accuracy_lin)\n",
    "print('Accuracy Polynomial Kernel:', accuracy_poly)\n",
    "print('Accuracy Radial Basis Kernel:', accuracy_rbf)\n",
    "print('Accuracy Sigmoid Kernel:', accuracy_sig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d2d9b3",
   "metadata": {},
   "source": [
    "# Try with Full list of Features, same parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92515a6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "793 samples with 15 features each <class 'numpy.ndarray'>\n",
      "(793, 15)\n"
     ]
    }
   ],
   "source": [
    "X = df_best_features.drop(['Composer_name', 'Composer_label'], axis=1)\n",
    "\n",
    "training_features_list = list(X.columns)\n",
    "\n",
    "X = X.to_numpy()\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "print(f'{m} samples with {n} features each',type(X))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.85, random_state = 0)\n",
    "\n",
    "# Scaling the Train and Test feature set \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "X_scaled = scaler.transform(X)\n",
    "\n",
    "print(X_scaled.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ad4ec8a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_all = svm.SVC(kernel='linear', C=1000, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "rbf_all = svm.SVC(kernel='rbf', gamma=0.1, C=1000, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "poly_all = svm.SVC(kernel='poly', degree=3, C=1000, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "sig_all = svm.SVC(kernel='sigmoid', C=1000, decision_function_shape='ovo').fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "475e2e35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Linear Kernel: 0.5546218487394958\n",
      "Accuracy Polynomial Kernel: 0.6050420168067226\n",
      "Accuracy Radial Basis Kernel: 0.7142857142857143\n",
      "Accuracy Sigmoid Kernel: 0.5042016806722689\n"
     ]
    }
   ],
   "source": [
    "# retrieve the accuracy and print it for all 4 kernel functions\n",
    "accuracy_lin_all = linear_all.score(X_test, y_test)\n",
    "accuracy_poly_all = poly_all.score(X_test, y_test)\n",
    "accuracy_rbf_all = rbf_all.score(X_test, y_test)\n",
    "accuracy_sig_all = sig_all.score(X_test, y_test)\n",
    "print('Accuracy Linear Kernel:', accuracy_lin_all)\n",
    "print('Accuracy Polynomial Kernel:', accuracy_poly_all)\n",
    "print('Accuracy Radial Basis Kernel:', accuracy_rbf_all)\n",
    "print('Accuracy Sigmoid Kernel:', accuracy_sig_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b8a964f",
   "metadata": {},
   "source": [
    "# Try Grid Search for Linear and Polynomial Kernels , which seem to work best\n",
    "\n",
    "- based on grid search example in scikit learn documentation [Grid Search SV](https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_stats.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6ca5fa35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "793 samples with 15 features each <class 'numpy.ndarray'>\n",
      "(793, 15)\n"
     ]
    }
   ],
   "source": [
    "## will use cross validation for the grid search... \n",
    "\n",
    "X = df_best_features.drop(['Composer_name', 'Composer_label'], axis=1)\n",
    "\n",
    "training_features_list = list(X.columns)\n",
    "\n",
    "X = X.to_numpy()\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "print(f'{m} samples with {n} features each',type(X))\n",
    "\n",
    "\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.85, random_state = 0)\n",
    "\n",
    "# Scaling the Train and Test feature set \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "#X_train = scaler.fit_transform(X_train)\n",
    "#X_test = scaler.transform(X_test)\n",
    "\n",
    "#Scale X and take all X and y\n",
    "\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e51bb382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{&#x27;C&#x27;: [100],\n",
       "                          &#x27;gamma&#x27;: [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7, 1.0, 3.0],\n",
       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{&#x27;C&#x27;: [100],\n",
       "                          &#x27;gamma&#x27;: [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7, 1.0, 3.0],\n",
       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{'C': [100],\n",
       "                          'gamma': [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7, 1.0, 3.0],\n",
       "                          'kernel': ['rbf']}])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV, RepeatedStratifiedKFold\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "param_grid = [{'kernel': ['rbf'], 'gamma': [1e-4,1e-3,3e-3,1e-2,3e-2,7e-2,1e-1,2e-1,3e-1,7e-1,1e0,3e0],\n",
    "                     'C': [100]}]\n",
    "\n",
    "# not sure why, grid search didn't like the polynomial kernel...\n",
    "\n",
    "svc = SVC(random_state=0)\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=0)\n",
    "\n",
    "search = GridSearchCV(estimator=svc, param_grid=param_grid, cv=cv)\n",
    "search.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bfff8374",
   "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>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kernel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100_0.2_rbf</th>\n",
       "      <td>{'C': 100, 'gamma': 0.2, 'kernel': 'rbf'}</td>\n",
       "      <td>1</td>\n",
       "      <td>0.728388</td>\n",
       "      <td>0.023317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_0.3_rbf</th>\n",
       "      <td>{'C': 100, 'gamma': 0.3, 'kernel': 'rbf'}</td>\n",
       "      <td>2</td>\n",
       "      <td>0.724867</td>\n",
       "      <td>0.023659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_0.03_rbf</th>\n",
       "      <td>{'C': 100, 'gamma': 0.03, 'kernel': 'rbf'}</td>\n",
       "      <td>3</td>\n",
       "      <td>0.724600</td>\n",
       "      <td>0.024758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_0.1_rbf</th>\n",
       "      <td>{'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}</td>\n",
       "      <td>4</td>\n",
       "      <td>0.723358</td>\n",
       "      <td>0.028158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_0.07_rbf</th>\n",
       "      <td>{'C': 100, 'gamma': 0.07, 'kernel': 'rbf'}</td>\n",
       "      <td>5</td>\n",
       "      <td>0.720591</td>\n",
       "      <td>0.032881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_0.01_rbf</th>\n",
       "      <td>{'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}</td>\n",
       "      <td>6</td>\n",
       "      <td>0.695867</td>\n",
       "      <td>0.028608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_0.7_rbf</th>\n",
       "      <td>{'C': 100, 'gamma': 0.7, 'kernel': 'rbf'}</td>\n",
       "      <td>7</td>\n",
       "      <td>0.675171</td>\n",
       "      <td>0.027752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_0.003_rbf</th>\n",
       "      <td>{'C': 100, 'gamma': 0.003, 'kernel': 'rbf'}</td>\n",
       "      <td>8</td>\n",
       "      <td>0.670873</td>\n",
       "      <td>0.014806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_0.001_rbf</th>\n",
       "      <td>{'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}</td>\n",
       "      <td>9</td>\n",
       "      <td>0.625460</td>\n",
       "      <td>0.020713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_1.0_rbf</th>\n",
       "      <td>{'C': 100, 'gamma': 1.0, 'kernel': 'rbf'}</td>\n",
       "      <td>10</td>\n",
       "      <td>0.623982</td>\n",
       "      <td>0.027211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_0.0001_rbf</th>\n",
       "      <td>{'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}</td>\n",
       "      <td>11</td>\n",
       "      <td>0.606299</td>\n",
       "      <td>0.025659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_3.0_rbf</th>\n",
       "      <td>{'C': 100, 'gamma': 3.0, 'kernel': 'rbf'}</td>\n",
       "      <td>12</td>\n",
       "      <td>0.466847</td>\n",
       "      <td>0.021433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      params  rank_test_score  \\\n",
       "kernel                                                                          \n",
       "100_0.2_rbf        {'C': 100, 'gamma': 0.2, 'kernel': 'rbf'}                1   \n",
       "100_0.3_rbf        {'C': 100, 'gamma': 0.3, 'kernel': 'rbf'}                2   \n",
       "100_0.03_rbf      {'C': 100, 'gamma': 0.03, 'kernel': 'rbf'}                3   \n",
       "100_0.1_rbf        {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}                4   \n",
       "100_0.07_rbf      {'C': 100, 'gamma': 0.07, 'kernel': 'rbf'}                5   \n",
       "100_0.01_rbf      {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}                6   \n",
       "100_0.7_rbf        {'C': 100, 'gamma': 0.7, 'kernel': 'rbf'}                7   \n",
       "100_0.003_rbf    {'C': 100, 'gamma': 0.003, 'kernel': 'rbf'}                8   \n",
       "100_0.001_rbf    {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}                9   \n",
       "100_1.0_rbf        {'C': 100, 'gamma': 1.0, 'kernel': 'rbf'}               10   \n",
       "100_0.0001_rbf  {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}               11   \n",
       "100_3.0_rbf        {'C': 100, 'gamma': 3.0, 'kernel': 'rbf'}               12   \n",
       "\n",
       "                mean_test_score  std_test_score  \n",
       "kernel                                           \n",
       "100_0.2_rbf            0.728388        0.023317  \n",
       "100_0.3_rbf            0.724867        0.023659  \n",
       "100_0.03_rbf           0.724600        0.024758  \n",
       "100_0.1_rbf            0.723358        0.028158  \n",
       "100_0.07_rbf           0.720591        0.032881  \n",
       "100_0.01_rbf           0.695867        0.028608  \n",
       "100_0.7_rbf            0.675171        0.027752  \n",
       "100_0.003_rbf          0.670873        0.014806  \n",
       "100_0.001_rbf          0.625460        0.020713  \n",
       "100_1.0_rbf            0.623982        0.027211  \n",
       "100_0.0001_rbf         0.606299        0.025659  \n",
       "100_3.0_rbf            0.466847        0.021433  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.DataFrame(search.cv_results_)\n",
    "\n",
    "results_df['C']=[p['C'] for p in results_df['params'].values]\n",
    "results_df['log_10[C]']=[np.log10(p['C']) for p in results_df['params'].values]\n",
    "\n",
    "results_df = results_df.sort_values(by=[\"rank_test_score\"])\n",
    "results_df = results_df.set_index(\n",
    "    results_df[\"params\"].apply(lambda x: \"_\".join(str(val) for val in x.values()))\n",
    ").rename_axis(\"kernel\")\n",
    "results_df[[\"params\", \"rank_test_score\", \"mean_test_score\", \"std_test_score\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ab10f410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='log_10[gamma]', ylabel='mean_test_score'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_df = pd.DataFrame(search.cv_results_)\n",
    "\n",
    "results_df['log_10[gamma]']=[np.log10(p['gamma']) for p in results_df['params'].values]\n",
    "\n",
    "results_df['C']=[p['C'] for p in results_df['params'].values]\n",
    "\n",
    "results_df = results_df.sort_values(by=[\"log_10[gamma]\"])\n",
    "\n",
    "sns.lineplot(data=results_df, x=\"log_10[gamma]\", y=\"mean_test_score\",hue='C',marker='o')\n",
    "#results_df.plot(x='log_10[gamma]',y='mean_test_score',color='C',marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9ffded83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{&#x27;C&#x27;: [1000],\n",
       "                          &#x27;gamma&#x27;: [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7, 1.0],\n",
       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{&#x27;C&#x27;: [1000],\n",
       "                          &#x27;gamma&#x27;: [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7, 1.0],\n",
       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{'C': [1000],\n",
       "                          'gamma': [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7, 1.0],\n",
       "                          'kernel': ['rbf']}])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = [{'kernel': ['rbf'], 'gamma': [1e-4,1e-3,3e-3,1e-2,3e-2,7e-2,1e-1,2e-1,3e-1,7e-1,1e0],\n",
    "                     'C': [1000]}]\n",
    "\n",
    "# not sure why, grid search didn't like the polynomial kernel...\n",
    "\n",
    "svc = SVC(random_state=0)\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=0)\n",
    "\n",
    "search_C2 = GridSearchCV(estimator=svc, param_grid=param_grid, cv=cv)\n",
    "search_C2.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a5e6ea1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df_C2 = pd.DataFrame(search_C2.cv_results_)\n",
    "\n",
    "results_df_C2['log_10[gamma]']=[np.log10(p['gamma']) for p in results_df_C2['params'].values]\n",
    "\n",
    "results_df_C2['C']=[p['C'] for p in results_df_C2['params'].values]\n",
    "\n",
    "results_df = pd.concat([results_df,results_df_C2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5c41bed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='log_10[gamma]', ylabel='mean_test_score'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_df['log_10[gamma]']=[np.log10(p['gamma']) for p in results_df['params'].values]\n",
    "\n",
    "sns.lineplot(data=results_df, x=\"log_10[gamma]\", y=\"mean_test_score\",hue='C',marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd4404e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{&#x27;C&#x27;: [10000.0],\n",
       "                          &#x27;gamma&#x27;: [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7],\n",
       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{&#x27;C&#x27;: [10000.0],\n",
       "                          &#x27;gamma&#x27;: [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7],\n",
       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{'C': [10000.0],\n",
       "                          'gamma': [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7],\n",
       "                          'kernel': ['rbf']}])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = [{'kernel': ['rbf'], 'gamma': [1e-4,1e-3,3e-3,1e-2,3e-2,7e-2,1e-1,2e-1,3e-1,7e-1],\n",
    "                     'C': [1e4]}]\n",
    "\n",
    "# not sure why, grid search didn't like the polynomial kernel...\n",
    "\n",
    "svc = SVC(random_state=0)\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=0)\n",
    "\n",
    "search_C3 = GridSearchCV(estimator=svc, param_grid=param_grid, cv=cv)\n",
    "search_C3.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7531e923",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a4ab3266",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df_C3 = pd.DataFrame(search_C3.cv_results_)\n",
    "\n",
    "results_df_C3['log_10[gamma]']=[np.log10(p['gamma']) for p in results_df_C3['params'].values]\n",
    "\n",
    "results_df_C3['C']=[p['C'] for p in results_df_C3['params'].values]\n",
    "\n",
    "results_df = pd.concat([results_df,results_df_C3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "30b64d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='log_10[gamma]', ylabel='mean_test_score'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_df['log_10[gamma]']=[np.log10(p['gamma']) for p in results_df['params'].values]\n",
    "\n",
    "sns.lineplot(data=results_df, x=\"log_10[gamma]\", y=\"mean_test_score\",hue='C',marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ba2b4e5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score for training data: 0.7283878672080247 \n",
      "\n",
      "Best C: 1000 \n",
      "\n",
      "Best Gamma: 0.2 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# View the accuracy score\n",
    "print('Best score for training data:', search.best_score_,\"\\n\") \n",
    "\n",
    "# View the best parameters for the model found using grid search\n",
    "\n",
    "Best_C = search_C2.best_estimator_.C\n",
    "\n",
    "Best_GAMMA = search_C2.best_estimator_.gamma\n",
    "\n",
    "print('Best C:',Best_C,\"\\n\") \n",
    "\n",
    "print('Best Gamma:',Best_GAMMA,\"\\n\")\n",
    "\n",
    "final_model = search_C2.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "164653c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.45272\n",
      "213 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.48828\n",
      "236 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.52358\n",
      "260 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.55232\n",
      "283 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.56216\n",
      "306 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.56769\n",
      "330 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.59190\n",
      "353 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.60451\n",
      "377 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.60755\n",
      "400 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.64615\n",
      "423 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.65120\n",
      "447 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.66129\n",
      "470 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.66986\n",
      "493 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.67616\n",
      "517 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.67868\n",
      "540 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.69004\n",
      "563 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.69660\n",
      "587 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.70367\n",
      "610 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.71451\n",
      "634 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.72864\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "svm_best_params = svm.SVC(kernel='rbf', gamma=Best_GAMMA, C=Best_C, decision_function_shape='ovo')\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=0)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "\n",
    "train_size_abs, train_scores, test_scores = learning_curve(\n",
    "                                                            svm_best_params, X, y, cv=cv,train_sizes=np.linspace(0.3,1,20)\n",
    ")\n",
    "\n",
    "\n",
    "size_list = []\n",
    "\n",
    "cv_train_list = []\n",
    "\n",
    "cv_test_list = []\n",
    "\n",
    "for train_size, cv_train_scores, cv_test_scores in zip(\n",
    "    train_size_abs, train_scores, test_scores\n",
    "):\n",
    "    size_list.append(train_size)\n",
    "    cv_train_list.append(cv_train_scores.mean())\n",
    "    cv_test_list.append(cv_test_scores.mean())\n",
    "    \n",
    "    \n",
    "    print(f\"{train_size} samples were used to train the model\")\n",
    "    print(f\"The average train accuracy is {cv_train_scores.mean():.5f}\")\n",
    "    print(f\"The average test accuracy is {cv_test_scores.mean():.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "99dc4fe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learing_results_df = pd.DataFrame([])\n",
    "\n",
    "learning_df = pd.DataFrame(list(zip(cv_train_list, cv_test_list)),\n",
    "               columns =['train','test'],index=size_list)\n",
    "\n",
    "learning_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cdf3ae68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "793 samples with 15 features each <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "X = df_best_features.drop(['Composer_name', 'Composer_label'], axis=1)\n",
    "\n",
    "training_features_list = list(X.columns)\n",
    "\n",
    "X = X.to_numpy()\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "print(f'{m} samples with {n} features each',type(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "13053fa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "793 samples with 15 features each <class 'numpy.ndarray'>\n",
      "(793, 15)\n"
     ]
    }
   ],
   "source": [
    "X = df_best_features.drop(['Composer_name', 'Composer_label'], axis=1)\n",
    "\n",
    "training_features_list = list(X.columns)\n",
    "\n",
    "X = X.to_numpy()\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "print(f'{m} samples with {n} features each',type(X))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.85, random_state = 0)\n",
    "\n",
    "# Scaling the Train and Test feature set \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "X_scaled = scaler.transform(X)\n",
    "\n",
    "print(X_scaled.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dd11e1a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "rbf_BEST_FINAL_MODEL = svm.SVC(kernel='rbf', gamma=Best_GAMMA, C=Best_C, decision_function_shape='ovo',probability=True).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9b7e8aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "TEST_PROBS_DF = pd.DataFrame(rbf_BEST_FINAL_MODEL.predict_proba(X_test),columns=['Bach','Beethoven','Brahms','Schubert'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fd082e70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9H26DXbt2lXU3lQsGFCIiomJcunQJCxcuRFhYGLy9vdXpo0aNwgsvvIDU1FR07NgRV69eRXR0NFatWoX//ve/eP755/HUU09hx44dFutbsGABnJ2dsWPHDkyePBnjx4/H+vXrLeZ58803ER8fj5SUFISHh6NPnz4YMGAAEhIS1OAxZMgQdf4nnngCNWvWxP/93/8hKSkJzw4aBgcHhzLslfLDe1CIyCYNFjSw6/o+UWWwatUquLi4AAAuX76MgIAArFq1Cjrd//6eHzZsGB555BGL5V5++WX130OHDsXatWuxZMkSNG3aVJ0eGRmJcePGAQDq1KmDDz/8EBs2bECHDh3UeZ555hn07NkTwM0gFBMTg9deew0dO3YEALz44ot45pln1PnT0tIwcuRIhIeHAwBiq1VHZE2P0uiKCsczKERERP9o27YtUlJSkJKSgh07diA2NhZxcXE4fvy4Ok/jxo0tlsnLy8Nbb72FyMhIeHt7w8XFBT/++CPS0tIs5it8o21AQAAyMzNLnMfPzw8A0KBBA4tpV69eRXZ2NgBgxIgR6N+/Px566CFMmjQJJ44dvYNPry0MKERERP9wdnZGWFgYwsLC8MADD2DOnDm4fPkyZs+ebTFPQVOnTsV7772HV155BRs3bkRKSgo6duyIa9euWcxX+NKLoijIz88vcR5FUUqcZl4uMTER+/fvR+fOnbFx40b8u30zLFu2zN6PrykMKERERCVQFAU6nQ5XrlwpcZ4tW7agW7duePLJJ9GwYUPUrl0bhw4dKrc21q1bF8OHD8ePP/6I9p26YN68eeVWd1liQCEiIvpHbm4uMjIykJGRgdTUVAwdOhSXLl1C165dS1wmLCwM69evx/bt25GamooBAwYgIyOjzNt65coVDBkyBJs2bcLx48exbds27N+TjIiIiDKvuzzwJlkiIioX9t5cvffkhXK78XPt2rUICAgAALi6uiI8PBxLlixBmzZtcOzYsWKXee2113D06FF07NgRTk5OeP7559G9e3dkZWWVaVv1ej3Onj2L+Ph4nD59Gj4+PmgV2xlvvPFGmdZbXhhQiIiIAMyfPx/z588v8f2QkBCISJHpXl5eWL58+S3XvWnTpiLTCi9TeN3F1demTRuLaYXHW9l78gJMJtMt21JZ8BIPERERaQ4DChEREWkOAwoRERFpDgMKERERaQ4DChEREWkOAwoRERFpDgMKERERaQ4DChEREWkOAwoRERFpDgMKERHRHZo/fz48PDzueD0hISGYPn36Ha+nKuBQ90REVC5Sw+37ETsHAKk2LhPxu61LAJmZmXjttdfwww8/4PTp0/D09ETDhg2RmJiImJgYm9enNW3atEFUVFSlCUAMKERERAAeffRRXL9+HQsWLEDt2rVx+vRpbNiwAefOnavopt2R69evw8HBoaKbYTNe4iEiorvehQsXsHXrVrzzzjto27YtgoOD8cADDyAhIQGdO3dW53n++efh5+cHk8mE+vXrY9WqVRbrWbduHSIiIuDi4oJOnTohPT1dfa9NmzYYNmyYxfzdu3dH3759LaZdvHgRffr0gYuLCwIDA/HBBx9YvJ+VlYXnn38evr6+cHNzQ7t27bBnzx71/cTERERFRWHu3LmoXbs2jEYjnn76afzyyy94//33oSgKFEUp8deZtYIBhYiI7nouLi5wcXHB8uXLkZubW+T9/Px8xMXFYfv27fjyyy9x4MABTJo0CXq9Xp0nJycH7777Lr744gts3rwZaWlpePnll21uy5QpUxAZGYndu3cjISEBw4cPx/r16wHc/MXjzp07IyMjA2vWrEFSUhIaNWqE9u3bW5zp+fPPP/HNN99g6dKlSElJwYwZMxATE4PnnnsO6enpSE9PR1BQkB09VX54iYeIiO56BoMB8+fPx3PPPYePP/4YjRo1QuvWrdG7d29ERkbip59+ws6dO5Gamoq6desCAGrXrm2xjuvXr+Pjjz/GPffcAwAYMmQIxo8fb3NbWrRogdGjRwMA6tati23btuG9995Dhw4d8PPPP2Pfvn3IzMyE0WgEALz77rtYvnw5vv32WzR7uCcA4Nq1a/jiiy9QvXp1db2Ojo5wcnKCv7+/7R1UAXgGhYiICDfvQTl16hRWrFiBjh07YtOmTWjUqBHmz5+PlJQU1KxZUw0nxXFyclLDCQAEBAQgMzPT5nYUviE3JiYGqak3b/pNSkrCpUuX4O3trZ71cXFxwdGjR3H48GF1meDgYItwUhnxDAoREdE/TCYTOnTogA4dOuD1119H//79MW7cOKsu1RS+EVVRFIiIWtbpdBZl4OZZF2soigLg5qWmgIAAbNq0qcg8Hh4eOHX15r+dnZ2tWq+W2XUGZebMmQgNDYXJZEJ0dDS2bNlyy/kXLlyIhg0bwsnJCQEBAXjmmWdw9uxZuxpMRERUXurVq4fLly8jMjISJ0+exMGDB+1eV/Xq1S1ums3Ly8N///vfIvP99ttvRcrh4eEAgEaNGiEjIwMGgwFhYWEWLx8fn1vW7+joiLy8PLvbX95sDiiLFy/GsGHDMHbsWCQnJ6Nly5aIi4tDWlpasfNv3boV8fHx6NevH/bv348lS5bg//7v/9C/f/87bnx5abCgQUU3gYiIytDZs2fRrl07fPnll9i7dy+OHj2KJUuWYPLkyejWrRtat26NVq1a4dFHH8X69etx9OhR/PDDD1i7dq3VdbRr1w6rV6/G6tWr8fvvv2PQoEG4cOFCkfm2bduGyZMn4+DBg/joo4+wZMkSvPjiiwCAhx56CDExMejevTvWrVuHY8eOYfv27Xj11Vexa9euW9YfEhKCHTt24NixYzhz5gzy8/Nt6qPyZvMlnmnTpqFfv35qwJg+fTrWrVuHWbNmYeLEiUXm/+233xASEoIXXngBABAaGooBAwZg8uTJd9h0IqLKrcGCBtj39L6Kbka5sWfwNADYe/ICImt6lG5jCnFxcUHTpk3x3nvv4fDhw7h+/TqCgoLw3HPPYcyYMQCApUuX4uWXX8bjjz+Oy5cvIywsDJMmTbK6jmeffRZ79uxBfHw8DAYDhg8fjrZt2xaZ76WXXkJSUhLeeOMNuLq6YurUqejYsSOAm5d61qxZg7Fjx+LZZ5/F33//DX9/f7Rq1Qp+fn44f4v6X375ZTz99NOoV68erly5gqNHjyIkJMSWbipXNgWUa9euISkpSb272Cw2Nhbbt28vdpnmzZtj7NixWLNmDeLi4pCZmYlvv/1Wfa68OLm5uRaPeWVnZ9vSTCIiIpsYjUZMnDix2D+0zby8vDB37txi3+vbt2+R8Uy6d+9ucc+Jg4MDZs6ciZkzZ5ZYhzVjk7i6umLGjBmYMWNGkffOn7yAxMREJCYmFnmvbt26+PXXX2+7fq2w6RLPmTNnkJeXBz8/P4vpfn5+yMjIKHaZ5s2bY+HChejVqxccHR3h7+8PDw+PIgPPFDRx4kS4u7urL60/q01ERESly66bZM13E5uJSJFpZgcOHMALL7yA119/HUlJSVi7di2OHj2KgQMHlrj+hIQEZGVlqa8TJ07Y00wiIiKqpGy6xOPj4wO9Xl/kbElmZmaRsypmEydORIsWLTBy5EgAQGRkJJydndGyZUtMmDABAQEBRZYxGo3qADTlITU8wu5ro0RERFT6bDqD4ujoiOjoaHXIXbP169ejefPmxS6Tk5MDnc6yGvPQwIWfByciIiIC7LjEM2LECHz22WeYO3cuUlNTMXz4cKSlpamXbBISEhAfH6/O37VrV3z33XeYNWsWjhw5gm3btuGFF17AAw88gMDAwNL7JEREpBll/gfoqeSyXT9Zray+a5sfM+7VqxfOnj2L8ePHIz09HfXr18eaNWsQHBwMAEhPT7cYE6Vv3764ePEiPvzwQ7z00kvw8PBAu3bt8M4775TepyAiIk0wj6aak5ODatWqVXBrqDzk5OQAKDqS7p2ya6j7QYMGYdCgQcW+N3/+/CLThg4diqFDh9pTFRERVSJ6vR4eHh7qb9A4OTmV+BCFteTGNVy9etVy4g0BCk+703VWAWXxuUpap4ggJycHmZmZ8PDwsPhl59LA3+IhIqJSZf61XHt+KK84meevwPFKobMxF/4GLh8t3XVWAWXxuW63Tg8PjzL5hWQGFCIiKlWKoiAgIAC+vr5W/xjerfT/bhM2vNTGcuKHPYAhtx7a3eZ1VgFl8blutU4HB4dSP3NixoBCRERlQq/Xl8rB66+LeTCZTJYTL50ACk+703VWAWXxuSqqr+waqI2IiIioLDGgEBERkeYwoBAREZHmMKAQERGR5jCgEBERkeYwoBAREZHmMKAQEdFdocGCBhXdBLIBAwoRERFpDgMKERERaQ4DCpEdeKqYiKhsMaAQERGR5jCgEBERkeYwoBAREZUTXh62HgMKERERaQ4DChEREWkOA8odChm9uqKbQEREVOUwoBAREZHmMKAQERGR5jCgEBERkeYwoBAREZHmMKAQERGR5jCgEBERkeYwoBAREZHmMKAQERGR5jCgEBERkeYwoBAREZHmMKAQERGR5jCgEBERkeYwoBAREZHmMKAQEREBaLCgQUU3gQpgQCEiIiLNYUAholtKDY+o6CYQ0V2IAYWIiIg0hwGFiIiINIcBhYiIiDSHAYWIiIg0hwGFiIiINIcBhYiIiDSHAYVswkdOiYioPDCgEBERkeYwoBAREZHmMKAQERGVEf6+j/0YUIiIiEhzGFCIiIhIcxhQiIiISHMYUIiIiEhzGFCIiIhIcxhQiIiISHMYUIiIiEhzGFCIiIhIcxhQiIiISHMYUIiIiEhzGFCIiIhIcxhQiIiISHMYUIiIiEhzGFCIiIhIcxhQiIiISHPsCigzZ85EaGgoTCYToqOjsWXLllvOn5ubi7FjxyI4OBhGoxH33HMP5s6da1eDiYiIqOoz2LrA4sWLMWzYMMycORMtWrTAJ598gri4OBw4cAC1atUqdpmePXvi9OnTmDNnDsLCwpCZmYkbN27cceOJ6M6lhkcg4vfUim4GEZEFmwPKtGnT0K9fP/Tv3x8AMH36dKxbtw6zZs3CxIkTi8y/du1a/PLLLzhy5Ai8vLwAACEhIbesIzc3F7m5uWo5Ozvb1mYSEf1PojuQmFXRrSAiG9h0iefatWtISkpCbGysxfTY2Fhs37692GVWrFiBxo0bY/LkyahRowbq1q2Ll19+GVeuXCmxnokTJ8Ld3V19BQUF2dJMIiIiquRsOoNy5swZ5OXlwc/Pz2K6n58fMjIyil3myJEj2Lp1K0wmE5YtW4YzZ85g0KBBOHfuXIn3oSQkJGDEiBFqOTs7myGFiIjoLmLzJR4AUBTFoiwiRaaZ5efnQ1EULFy4EO7u7gBuXiZ67LHH8NFHH6FatWpFljEajTAajfY0jYiIiKoAmy7x+Pj4QK/XFzlbkpmZWeSsillAQABq1KihhhMAiIiIgIjg5MmTdjSZiIiIqjqbAoqjoyOio6Oxfv16i+nr169H8+bNi12mRYsWOHXqFC5duqROO3jwIHQ6HWrWrGlHk4mIiKiqs3kclBEjRuCzzz7D3LlzkZqaiuHDhyMtLQ0DBw4EcPP+kfj4eHX+Pn36wNvbG8888wwOHDiAzZs3Y+TIkXj22WeLvbxDRKWnwYIGFd0EIiK72HwPSq9evXD27FmMHz8e6enpqF+/PtasWYPg4GAAQHp6OtLS0tT5XVxcsH79egwdOhSNGzeGt7c3evbsiQkTJpTepyAiIqIqxa6bZAcNGoRBgwYV+978+fOLTAsPDy9yWYiIiIioJPwtHiIiItIcBhQiIiLSHAYUIiKqehLdbz8PaRoDChEREWkOAwoRERFpDgMKERERaQ4DChEREWkOAwoRERFpDgMKERGVLT5RQ3ZgQCEiIiLNYUAhIiKiO1MGZ8kYUIiIiEhzGFCIiIhIcxhQiIiISHMYUIiIiEhzGFCoSkoNj6joJlB54mOsRFUOAwoRERFpDgMKURnhWRw78WwIEeEuDSg8cBARUWXVYEGDim5CubgrAwoRERFpGwMKERERaQ4DChEREWkOA8pd5G65bklERJUfAwoRERFpDgMKERERaQ4DChEREWkOAwoRERFpDgNKZcXRNomIqApjQCEiIiLNYUAhIiIizWFAISIiIs1hQCEiIiLNYUAhIiIizWFAoTuSGh5R0U0gIqIqiAGFiKiM8PeviOzHgEJERESaw4BCRBWOlwqJtEULZ/8YUKjc8WBERES3w4BCRESljn+I0J1iQCEiIiLNYUAhqkK0cN2YiKg0MKCQigc3IiLSCgYUIiIi0hwGFCIiItIcBhQiIiLSHAYUIiIi0hwGFCIiItIcBhQiIiLSHAYUIiIi0hwGFCIiItIcBhQiIiLSHAYUIiIi0hwGFCIiItIcBhQiIiLSHAYUIiIi0hwGFCIiItIcBhQiIiLSHAYUIiIi0hwGFCIiItIcuwLKzJkzERoaCpPJhOjoaGzZssWq5bZt2waDwYCoqCh7qiUiIqK7hM0BZfHixRg2bBjGjh2L5ORktGzZEnFxcUhLS7vlcllZWYiPj0f79u3tbiwRERHdHWwOKNOmTUO/fv3Qv39/REREYPr06QgKCsKsWbNuudyAAQPQp08fxMTE2N1YIiIiujvYFFCuXbuGpKQkxMbGWkyPjY3F9u3bS1xu3rx5OHz4MMaNG2dVPbm5ucjOzrZ4ERER0d3DpoBy5swZ5OXlwc/Pz2K6n58fMjIyil3m0KFDGD16NBYuXAiDwWBVPRMnToS7u7v6CgoKsqWZRKRlie4V3QIiqgTsuklWURSLsogUmQYAeXl56NOnD9544w3UrVvX6vUnJCQgKytLfZ04ccKeZhIREZUdhu0yZd0pjX/4+PhAr9cXOVuSmZlZ5KwKAFy8eBG7du1CcnIyhgwZAgDIz8+HiMBgMODHH39Eu3btiixnNBphNBptaRoRERFVITadQXF0dER0dDTWr19vMX39+vVo3rx5kfnd3Nywb98+pKSkqK+BAwfi3nvvRUpKCpo2bXpnrSciIqIqyaYzKAAwYsQIPPXUU2jcuDFiYmLw6aefIi0tDQMHDgRw8/LMX3/9hc8//xw6nQ7169e3WN7X1xcmk6nIdCIiIiIzmwNKr169cPbsWYwfPx7p6emoX78+1qxZg+DgYABAenr6bcdEISIiIroVmwMKAAwaNAiDBg0q9r358+ffctnExEQkJibaUy0RERHdJfhbPERERKQ5DChEGtFgQYOKbgIRkWYwoFC5CRm9uqKbQERElQQDChEREWkOAwoRERFpDgMKERFRJVZV719jQCEiIiLNYUAhIiIizWFAISIiIs1hQKnEUsMjKroJRFSWEt0rugVEFabKB5SqevMQERFRVVblAwoRERFVPgwoREREpDkMKERERKQ5DCikbbxJkIjorsSAQkRERJrDgEJERESaw4BCREREmsOAQkRERJrDgEJERESaw4BCREREmsOAQkRERJrDgFJB+EN/REREJWNAISIiIs1hQCEiIiLNYUAhIiIizWFAISIqJw0WNKjoJhBVGgwoREREpDkMKERERHTHSvvpVAYUIiIi0hwGFCIiItIcBhQiIiLSHAYUO/BOfKKyxf9jRMSAQlQMHiCJiCoWAwoRkTUS3Su6BXQr/H6qHAYUIqK7GQ/spFEMKERERKQ5DChERJUFz3bQXYQBhYiIbMKbyKu2kNGrK7oJABhQiIiISIMYUIiI7nKl/RsqRKWBAYWIiIg0hwGFrMcb9IiIqJwwoBAREZHmMKAQERGR5jCgEFUVvARHRFUIAwoRERFpDgMK3ZX4WCURkbYxoNzFOBpkOeLlFyIimzCgEBERkeYwoBAREZHmMKAQERGR5jCgEBERkeYwoBAREZHmMKAQlRM+NUVEZD0GFCpZVX00tqp+LiKiKoQBhYiIiDSHAYWIiIg0p9IHFF7XJyIiqnoqfUChqo+/m0NEdPexK6DMnDkToaGhMJlMiI6OxpYtW0qc97vvvkOHDh1QvXp1uLm5ISYmBuvWrbO7wURERFT12RxQFi9ejGHDhmHs2LFITk5Gy5YtERcXh7S0tGLn37x5Mzp06IA1a9YgKSkJbdu2RdeuXZGcnHzHjSeiKoBPVRFRMQy2LjBt2jT069cP/fv3BwBMnz4d69atw6xZszBx4sQi80+fPt2i/Pbbb+P777/HypUrcf/99xdbR25uLnJzc9Vydna2rc0kIiKiSsymMyjXrl1DUlISYmNjLabHxsZi+/btVq0jPz8fFy9ehJeXV4nzTJw4Ee7u7uorKCjIlmYSERFRJWdTQDlz5gzy8vLg5+dnMd3Pzw8ZGRlWrWPq1Km4fPkyevbsWeI8CQkJyMrKUl8nTpywpZlERERUydl8iQcAFEWxKItIkWnFWbRoERITE/H999/D19e3xPmMRiOMRqM9TSMiIqIqwKaA4uPjA71eX+RsSWZmZpGzKoUtXrwY/fr1w5IlS/DQQw/Z3lIiIiK6a9h0icfR0RHR0dFYv369xfT169ejefPmJS63aNEi9O3bF1999RU6d+5sX0upXHDMESIi0gKbL/GMGDECTz31FBo3boyYmBh8+umnSEtLw8CBAwHcvH/kr7/+wueffw7gZjiJj4/H+++/j2bNmqlnX6pVqwZ3dz5eSEREREXZPA5Kr169MH36dIwfPx5RUVHYvHkz1qxZg+DgYABAenq6xZgon3zyCW7cuIHBgwcjICBAfb344oul9ymI/hEyenVFN4GIiEqBXTfJDho0CIMGDSr2vfnz51uUN23aZE8VVBkkugMIrOhWEJEtEt2BxKyKbkWlkRoegYjfUyu6GXcl/hYPERFVOP7wKxXGgEJERESaw4BCRHcFPqFGVLkwoFCVw1PFRLfGsFZ62JdlhwGF6C7C8Fb5VcQBkdsNVQQGFCIiItIcBhQiIiLSHAYUIivwFHfFYv8T3X0YUIiIiEhzGFA0in8xkiYk8veyiKhiMKAQERGR5jCgEBERkeYwoBAREVUgDvZWPAaUisDr+kRUlXEfR6WAAYWIiIg0hwGFNIenO4mIypcW97sMKERERKQ5d19A4bVRKg/czoiI7sjdF1CIiIhI8xhQiKjcafF6NxFpCwMKERERaQ4DChERlTmeNSNbMaDwZkYiIqooPAaViAGFiIiINIcBhYiISCt4RkXFgEJERESaw4BCpFENFjSo6CYQEVUYBhQiIiLSHAYUIiIi0hwGFCKqEjjOBlHVwoBCREREmsOAUknwhkkqgo8jElEVxoBClR7DWzliKCK664WMXl0u9TCgkLbwAEhEdir1+5C4P6pQDChERESkOQwoRKWBf2kREZUqBhQqV7xfhIiIrMGAYqPyujlIKzi2BBERVQQGFCK6IzwrVsXwciVpBAMKEZGVeEaRboeBvfQwoBAREZHmMKAUVllPb1bWdlcCd9t9R0RljvsrsgIDClEhDCRElQMvuVVtDChERESkOQwoRBp2u78Q+RckEVVVDChERESkOQwoREQaxrNkdLdiQKlCuCMrH7yJlqzB7YTsVR778spwvGBAISIiC1o4eHHAM2JAISK78SwBEZUVBhQiDaiwAz0HzKr0eKaBKkJ5bHdVLqBUxH9Wa+rUwilTIiKiyqLKBRSqZBLdK214q6ztLmv8i15b+H3cxMuRlQ8DChERUSVRKkGrklzardIBhYmZyhPPqNDdgPvVKkijgaVKBxQqGXcyVR+/YyKqzCp1QCmPHTB38kQVi/8HyV48q1m5VeqAQncn3vRX+fBAQbdSVUNo4c9VVT9nWWFAISKqonhALGUavVejqrIroMycOROhoaEwmUyIjo7Gli1bbjn/L7/8gujoaJhMJtSuXRsff/yxXY3FxJr2LUe3xR1Z1cfv2JItZ+LYd7d2N53VrIizgbeqs7hts6p8HzYHlMWLF2PYsGEYO3YskpOT0bJlS8TFxSEtLa3Y+Y8ePYqHH34YLVu2RHJyMsaMGYMXXngBS5cuvePGl5UqcTq6Eo8vUur4V0+VVmx4qATfeWmEnsocnLS2f6rMfVlV2RxQpk2bhn79+qF///6IiIjA9OnTERQUhFmzZhU7/8cff4xatWph+vTpiIiIQP/+/fHss8/i3XfftavBWtuoS8utEi//49y5qrrdlLqyCLZlEBZKe/RmLf0fu9P+ryp/PVdGZbIdVYKwXaxS2JcYbJn52rVrSEpKwujRoy2mx8bGYvv27cUu8+uvvyI2NtZiWseOHTFnzhxcv34dDg4ORZbJzc1Fbm6uWs7KygIAZOcKLuXlITs7GwCQn5uDvCv/K9cftw4u95b8/s2VW67jduXC67hdGQAwsSb++NYf9ybtsquOsvgct1unuWzLOov7HPU+roff+vxWYrk06rCmr2yqY2JNNAuuWaSdpd1XhddRXN8U/lyF23m77cqWbbX+uHX4r8m2/r9dHcV9H9lK6dZRXNnWOqxZZ+Hvp0gdhb+PQmVb/1/n5+YgO8ENl/L8y3b/VEb7jtLczxZXZ8HvozTquN3/wdKqo6z7ytpjEhJOWl1nWfSViMBmYoO//vpLAMi2bdsspr/11ltSt27dYpepU6eOvPXWWxbTtm3bJgDk1KlTxS4zbtw4AcAXX3zxxRdffFWB14kTJ2yJGyIiYtdNsoqiWJRFpMi0281f3HSzhIQEZGVlqa/z58/j8OHD6n0uJ06cQFZWFk6cOGFT2Z5lWAfrqKp1VNZ2sw7WwToqTx1paWk4ceIEAgMDYSubLvH4+PhAr9cjIyPDYnpmZib8/PyKXcbf37/Y+Q0GA7y9vYtdxmg0wmg0Wkzz8PBQTxW5ubnBzc1Nfc/Wcmmsg3WwjqpSR2VtN+tgHaxD+3W4u7sXmcdaNp1BcXR0RHR0NNavX28xff369WjevHmxy8TExBSZ/8cff0Tjxo2Lvf+EiIiIyOZLPCNGjMBnn32GuXPnIjU1FcOHD0daWhoGDhwI4Oblmfj4eHX+gQMH4vjx4xgxYgRSU1Mxd+5czJkzBy+//HLpfQoiIiKqUmy6xAMAvXr1wtmzZzF+/Hikp6ejfv36WLNmDYKDgwEA6enpFmOihIaGYs2aNRg+fDg++ugjBAYGYsaMGXj00UdtbqzRaMS4cePUyz+2lktjHayDdVSVOipru1kH62Adla8Oeygi9jz7Q0RERFR2+Fs8REREpDkMKERERKQ5DChERESkOQwoREREpDl3XUDhPcFERETaZ/NjxuXp5MmTmDVrFrZv346MjAwoigI/Pz80b94cAwcORFBQkM3rNBqN2LNnDyIi7PuVxfT0dMyaNQtbt25Feno69Ho9QkND0b17d/Tt2xd6vd6u9RIREdH/aPYx461btyIuLg5BQUGIjY2Fn58fRASZmZlYv349Tpw4geXLl8PR0RFeXl6oV68eNm7ciHHjxmH27NmYOXMmdu/ejYsXLyIvLw/h4eGoVasW3n//fTz55JPw9vbGiRMnEB4ejnPnziEgIAA//vgjzp8/jwsXLiA4OBhDhgzB33//jV27dqFz586oXbs2Wrdujby8PCiKgtzcXPTp0wc3btzAunXrEBERgXXr1sHV1bWiu6+Iy5cv46uvvioS9lq0aIHHH38czs7OAG6GwmvXruHLL7/E4MGDsXfvXjRs2BCurq744YcfkJqaitzcXPTo0QMRERGoXbs21q1bB4PBgC1btuD06dMICAjA5s2bkZmZibNnzyI4OBiDBw9GTEwMVq5ciV27dqFTp06IiYnBDz/8gFdeeQXnz5+Hh4cHQkJC1MDXvn37Cu614tnSlx4eHrh8+TLee+89dOzY0a6+9PX1xejRo6HX63HvvfdWqb4ErOvPgn35ySefqNtmvXr18Mcff6BevXqYM2eO3dvmmTNnLPpy48aNeOedd/DXX3+pQ3UX/GNEq/1ZWbbNmjVr4tq1a3B0dISiKLf9zq1pI4A7+s4r6/+fqkyzAaVJkyZ48MEH8d577xX7ft++fbFo0SJcv34diqLgvvvuw++//47r16/DZDIhNzcXIgJPT0+ICLKysqDX63Hjxg00adIEOp0OO3fuhIeHBxo3boxdu3bh/PnzUBQF1atXR506dbBz507o9Xp07twZ27Ztg9FoRGZmJsaOHQudToeJEyfCzc0Nf/zxB2bPno0333wTrq6uqFevnqYO/pmZmTh37hwAICgoCPXq1UNgYCAyMzPxyy+/wNnZGQsXLsTIkSORlJQERVGQl5cHd3d3ZGdnw8PDA71798asWbNQvXp1iAjOnj2LwYMHY9asWRgyZAhmzpyJvLw83HPPPTh27Jj6vdSqVQu+vr5ITk7G888/j08//RQNGzbEoUOH8Prrr2PkyJFwdHSEXq9HTk4OwsPDkZubi2PHjsHPzw9RUVHw9/fXzMHKwcEBqampyMvLs7ovmzVrhm3btkFRFLv7Erj545o+Pj44d+4c+vfvjzlz5txxX5q3y4o6WJlMJpw8efNn4KOjo1G/fn31D5FffvkFJpMJPj4+OHDgABRFQVxcHFatWqVum+7u7rhw4QK8vLyg1+vt2jbN31NUVBQOHTqEmTNnYsCAAVAUBTdu3MDVq1ehKApiY2Nx8OBBq7dNW/qyTp06OHr06B0dVG/Xl1raNg0GA0QEeXl5qF27Ntq2bYszZ84U+50XbuPXX3+Njh07qm3MyspC9erVMXToULz66qt2fefFtbFevXpwcXHBrl27EB4ejgcffBB6vV7dt7dv3179wdv8/HxkZWVh5cqVeOqpp3Ds2DEEBQVBp9PhyJEjSEpKQm5uLh5++GH4+PigXbt2mDdvHvz9/bF3714cO3YMAQEBSElJQVpaGtLT0xEcHIx+/fohNDQUe/bswe7du9GmTRuEhoZi//79SEhIQHp6Ovz8/BAcHIwbN27AaDSqn7VwO/Pz86HT6XD+/HmsWLECrVq1umUbAdjUzuzs7CJt/Oijj5Cfn49///vf6Nixo+1BwObfPy4nJpNJfv/9d4tp33//vfpq2LChKIoiX3zxhXz88cfi7OwsJpNJFEWRRYsWiclkEgCyYcMGEREZM2aMABC9Xi/79++X3r17i06nU+uIiooSANKyZUt58cUXxcfHRwCIyWSSlStXyu7duwWATJs2TW3Pt99+KwDE399fPDw8JCYmRpycnOS5556Tbt26iYeHh9SoUUM2bdokTZo0EZ1OJ3q9XgCIu7u7KIoinp6e8p///EcASPXq1cXHx0cURZEhQ4aIXq+XF198URwcHESn00mdOnXEwcFBAIiiKBIcHCxNmjQRg8EggwYNEoPBINHR0eLm5ibvvvuuKIoiRqNRdDqdAJCIiAhp2rSp6HQ66dChgyQlJcmuXbukU6dO4u3tLZGRkfLVV1/J2LFjBYD4+PjI8ePHZcqUKepPZoeEhEhISIgAkGrVqomiKOLk5CQAJCAgQEREoqOj1b7s1q2bODg4iLOzs+j1ehk3bpyIiGzcuFF0Op08+OCDkpeXJyIisbGxoiiKeHh4SPv27cXFxUUaNWqk9qW/v7/Ur19f7ccuXbpY9KWHh4cAEC8vL7v70rxucz9++eWX4uzsLC4uLuo2BUDi4uKkadOmotfrb9mXn3zyiVSrVk0A2N2XtWrVEgAyb9486datm7oNtW/fXvbt22dXXxa3XbZo0ULtD3u3S/Py1atXF71eLwMGDChxuzR/Vjc3N2nSpIlFX+7Zs0d27dolgYGB4uzsrPZl7dq1BYA89dRTkp2dLa+//roAEGdnZ7u3TUVRxMXFRe1Lk8kk9erVkwEDBkheXp7MmzdPPD09xWg0Wr1t3q4vHRwcJDAwUHQ6nSQkJMhbb70lDg4OYjAYrN42b9eXPXr0sOhLrWyb5m0jNzdXDh48KIGBgTJw4MASv/PCbQwMDBQAUrNmTQkJCREXFxcBIEFBQXZ/54Xb+Nhjj0m1atUkKipK9Hq9ODg4yD333CMdOnSQhg0bil6vl0aNGklqaqr06NFDTCaTeHl5CQAJDg4WnU4nYWFhap8ajUZxdHQUg8Eg7733nuj1epk6daradkdHR7XvdTqd1K5dW+rWrStOTk7y1ltviV6vF29vb3F1dZUlS5ao/awoSpHlQ0JCLNrZsGFD6dKli5hMJvH19ZWePXuq9YSFhcmOHTss2ujq6iqNGzeWRYsWWd1OR0dHizb+9NNP4uHhIQ899JB07NhR9Hq9LFy40OYcoNmAEhoaKnPnzrWYpiiK6HQ69Usxf0EFywDk0KFDYjAYBIDUrVtXXnrpJTVgmANKaGiouLm5ya5du0RExNfXVxRFUTfqa9euqRuyXq9X/1NMmTJFbc/OnTsFgPTo0UNyc3Pl6NGj4ujoKHv27Clxp1ARB/9q1apJbGysODs7q/1YUv8V7N927dpJVFSUZGRkqO8fOHBAXYe/v78YDAYJCQkRZ2dn2bNnj0VfBgUFiYjI6dOn5ZVXXlHra9KkiXz66acCQFatWqX25wMPPCAA5NSpUyIiMn36dAkMDNTUwcrR0VE8PT0lLi5OREQOHjxo0Y+F+9YcDgHY3ZefffaZAJDTp0+LiKg7lHvuuUd0Op00adJEAMg333xTYl8uX77coi+1crDS6/VSrVo1m/syPDxczp49KydOnFD/n9u7bZr3Fea+1Ov1YjKZ5ODBgyIicvToUfX/hbXb5u360rzDByA1atRQyzt37rR627xdX5q/M3PbtbJtmoP+mTNnLPZH1rbRPC0qKkoOHDig7tvN36U933nhNv7xxx9q/586dUqWL18utWrVkqysLMnKypI//vhDWrVqJSEhIRIWFiYLFixQ93F+fn6SlJQkw4YNK3E/W/iYJSLSuHFjASAxMTFy//33q0Hf0dFRxowZIyIiixYtEgcHB4mIiJDz589LTk6OeHp6isFgkFOnTsmGDRvE09NTJk2apLbTvG0uWLBAZsyYIUajUQCobaxbt26Rfb+t7TQYDOLp6SlnzpyRRYsWiYeHh4wfP17tz3fffVeioqLEVpoNKB999JE4OjrK4MGDZfny5fLrr79K9erVZfTo0TJ48GABIK+//ro6v5ubm3z//fdqsjbvSC9evCjx8fESHh4uAMRgMMj+/fslMDBQunTpIv369RMRkR49eqgJ0czT01NCQkLk+PHjMmTIEPVL+uGHH2Tjxo0SGRkpAGT//v0iIrJ27doiOwQtHPwDAwPlo48+UncKPj4+MmTIEKlZs6YcO3ZMPvnkE1EURX7++Wc5duyYrF69Wg163bt3l8jISPWAFBQUJB988IH6l5vBYBBvb29p27atjB49WkREOnbsWKQvZ8+eLQaDQT744AN5+umn1R34+++/r85jTufZ2dlqP1iz4yrPg5Wfn584OjqKg4ODuoN1c3MTb2/vYvvy2LFj6g7Z3r4cN26cxUHA3JebN2+WzZs3q9uug4NDiX155MgRTR6sfH19LQ5WBfvy2LFjYjQaxcvLSy2bt80OHTpIZGSk7N27VwDIrFmz7N42PT09pWbNmhZ9ad6Bi4hs2rRJ7S9bt82S+rLwH0x169a944Nq4b5cvny56HQ6mTNnjqa2TfOZBnNfenl5icFgKPE7L66NiqLIsmXLJCgoSN544w0BIJmZmXf0nRds48qVKwWAbNu2rcj/n4Kvgt+5eTuoX7++xMbGytmzZ9Xv3dw/iqJIjRo1xGAwSFhYmBiNRklNTVX3LYqiSGhoqIiI7Nq1S/r06aO2vUePHrJu3ToBIN99953aVvO+NCsry2LbLNjG4srmNprn79y5s5w+fVq2b99uEfisaad52zQajfLYY4+JwWCQvXv3qm08fPiwuLi4iK00G1BERL7++mtp2rSpGAwGix1C06ZN5Z577pHPP/9cnTcyMlI++ugjURRFBg8eLK6urqIoivq++T8ScPNSh4uLi3z22WcSEhIirVq1khEjRqgb6XPPPSetWrUSnU4n7u7u0r9/fwkNDZURI0aol0wURRFHR0dxcnKS5cuXi4jIunXrxNXVVd0haOXgP27cOHF2dlb/w7Vp00YGDBggDg4OMmXKFPH09BQ/Pz/59ttvRUQkJSVFgJuXx65fvy7du3eX6tWri6IocvLkSWnXrp26MSqKIm5ubvLxxx+Lt7e3xMfHy5tvvqm+/9Zbb0l8fLwYjUZp166d1KlTRyZMmCDR0dFSvXp1qVatmnz66acyf/58URRFXF1d1c/l7u4unp6emjpYPf300+q2uG3bNklPT5dmzZqJTqcrti9Fbp4NBGB3X5q3j4SEhGL78oEHHpDQ0FBxcHAosS83bdqkyYPV448/LgBkwoQJkpKSIs2aNRO9Xi8pKSkyZcoU0ev10qtXL3V+87b5448/Svfu3aVWrVqiKIrk5OTYvW3qdDrx8/NT+7JPnz5iMpmkQYMGMnv2bLn33ntFr9erO2Rrts3b9eXx48ctAoqvr+8dH1QL9+WOHTvU/aGWtk3z+qZMmSIpKSkSFRUlrq6uJX7nJbVRROTkyZPqX/Tt27e3+zsv3EbzZeONGzeq/38URZF33nlHNm3aJJs2bZJp06YJAFm0aJFs2rRJZs+erf7BGhMTI+3atVP3u7Vq1ZKVK1eqxw3zGYemTZvK5MmTRUSkefPm6v8xs2+//VZ0Op288cYb0qZNG3XfM2/ePIttEYCcPXtWRERcXFxEr9er7TRftilYBmDRRkVRZNq0aVKrVi2ZO3euReCzpp0eHh7i5+cnn3/+uXp508/PT31/586d4u/vL7bSdEAxu3btmpw6dUqWLVsmK1euFBGRt99+Wz3NLiIya9Ys+fbbb2XTpk0iIvKf//zHIqCMGTNG6tevL71795YxY8ZIYmKirF27Vs6fPy+jRo2SevXqqQEoODhY+vTpI7/99ptMmDBBunTpIpMmTRKRm6fYatasKV5eXtK3b19JSEgQd3d39T9bmzZtZNSoUep/Nq0c/M335xQ+sxMQECDvvPOOvPLKKxIbGysiIufOnZPu3bvLokWLRETk+vXr8q9//Ut0Op2IiOTn54u/v78a1Lp37y7ffPON/Pnnn9K7d29xdXVVD+wODg7SvHlzWbZsmVy6dEn69+8v9evXl4EDB8rJkyfVywLmnbWzs7Pal/fdd5+0b99ecwcr8ynVwn85FdeXIiKjRo2SgQMH3nFfGgwGu/vy008/lTp16mjuYOXu7i5OTk4SEBBgsV0qys3Lra1bt7boy4LbZuG+tHfbXLRokUVfXrt2TcaNG2dxZsTNzU1cXFys3jZv15fNmjVTP+dDDz0knp6epXJQLdiXBf9a1tK2aTKZpH///mo7C559Ku47v1UbRUQSEhLEw8NDPWtesJ29evWy6jsvro3dunWToKAgWbJkicyfP1/CwsLknXfekQsXLsiSJUukVq1a4unpKatXr7bYH23ZskUuXrwoMTExUqNGDVEURVJSUtRjjLmd3t7eMmPGDHF3d5dx48bJBx98oNa9cOFCef3118XDw0Puv/9+adq0qXz55ZfSrl07cXJyEi8vL0lKSpJ9+/apAWPJkiVy4cIFadSokbi4uKjtNBgM8q9//Uvtr6CgIAFQpI3mz2C+dN6vXz+r2+ng4CBBQUHy5ZdfSteuXaVVq1ZSo0YNSU1Nld9//11at24tjz32mNiqUgQUrZs0aZLFDtb8suXgb95AyurgbzKZ5KeffpIjR47I9u3bZcKECeo9K+Z2mE8RFufGjRty7NgxtXz58mXZvn27TJ8+Xc6dO2cxb35+vhw+fFiOHDki165du23/7du3T3777Te5fv36LfuyNA9W1u64ijtYBQUFye7duy36suDlRnNf5ufnF+mXgn1pLpv7ctq0aRZ9mZ+fb9GXubm5RfqucB179uwpti+1GqRMJpOsX79eRG6eRp8wYYIMHz5cjhw5YtGXJSm8XRbszzvdNq9cuSK7d++Wffv22bxt3q4v27RpY9GXffv2tTlIlfR/vKS+tKc/y+r/uZn5/9D27dvt/s4vX74sV69elaSkpDtup7mNN27ckNzcXBk4cKA4OjqKTqcTg8EgBoNBdDqdODo6yn/+8x8ZNGiQeuDNyMiQ6OhomT17toiIZGdnqw8liIjk5OSIq6urGhybNGkin332mWzfvl2aNWtWJKzVqFFDpk+fLhkZGfLQQw+Ji4uLxMXFSUpKinrWxDxv27Zt1Xbq9Xr1co6jo6M0aNBAHnnkEfUzxsfHS9euXYtto4jI+PHjxWg0qmfirWnnm2++adHGrKwsGTJkiLrvqVOnjvz555+37PviaPYx48ro6NGjyMjIAAD4+/sjNDQUAHDjxg3k5OTAzc2t2OXy8vJw8uRJBAcHAwBycnKwZ88e7Ny5E/Hx8fD09FTnFREcPXoUiqKgZs2acHBwuGWb/vvf/+Ly5cuIjo6GwaDpcfksFNeXtvYjUHp9efXqVaSmpsLBwQHh4eFF+tLR0bHIAICFp5V22dplVq9eDRcXF4u+BOzbLvfu3YsdO3bctdslUDrb5q22S8C2/ty3bx9ycnIqVV8WHvAyPz8fIqIOdHm7sl6vR0BAAEwmEy5duoSMjAyb12FNHaGhoejYsSPuvfdenDlzBsDN7zw6Ohpubm44f/48Tp06hfvuu6/Yz3np0iUkJSWhdevWAG5uO7/88gv27NmDhIQE+Pr6qvP+/fffWLp0Kfz9/REVFYWQkJAS+y8nJwdLlizBpUuX8Oijj8Lf3x/Z2dlISkqy2Dajo6ORl5dncxtNJhP+7//+Dz///PMdtfPIkSPqkAf2bJsMKGXsxIkTGDduHObOnVts2Zp57rQMAIcOHcKwYcMwZcoU1KtXz6Jcu3ZtfPPNN+jRoweSkpLg7OyMffv2qWUvLy/Url0bX375JcLDw+0uW1PH9OnT1dGCg4ODMWbMGCQnJ8PT0xNDhgzBlStX8O6776rlwMBAJCYm4q+//kK/fv3Qt29frF692mIe8zJeXl4YPHhwieWS5i9Yh7OzM+rVq4dz584hOTkZeXl5yM3NxZ9//omIiAhUq1YNJ0+eRM2aNbF7925ERETg7NmzAIDMzEy7yuZ1Fi7fqo5q1aoBAFJSUvDkk0/C2dkZBw4cwH333YfAwEDEx8fDxcUFCxYswKFDhxAQEIBu3bphw4YNarl79+746aefSnz/VmVr6ggMDETdunWxd+9edbDErKwsbNy4EZmZmQgODkZgYCAOHz6slhs2bIitW7ciMzMT99xzD4YMGYI///wTy5YtK3EZW8vF1TF37lykp6ergzg6Ozvj1VdfRcuWLQEAQ4cORc+ePa0u27OMNev84IMP1IEle/bsiS+++AIjR47ElStX0K5dOyxZsgTPPPMMVq5cCScnJ/Tt2xdpaWlYtWqV3eU6deogISHB6jo6duyIxYsXIz8/H4qi4OrVq3BwcICzs7M6sObtyrGxsfj555+Rn5+PatWq4fLlyzAYDDat43bl3r17Q0Q0PwhnlWbzOReySUpKisXps8Jla+a50/Iff/whAQEBAvzvCQA/Pz+1HBMTI8DN5/cLXruuWbOmWjaPE2Nv2Zo6zE9aeXl5iclkklGjRqmnuAteojCXdTqdev3VfBd53759b7mMreXi6ggICBAHBwfx9PRUx14BIK6urmrZ/Nio+VS9+ZFbe8r21GG+T0RRFImMjFRvjGvdurXUrFlTPdXs6+srHTp0EH9/f7Xv7S2bv0db63Bzc5MOHTqIp6enADdvep41a5Y89NBDAkDuv/9+i7Krq6sMGzZMhg0bpt4T8+9///uWy9haLlyHeVv29fW1GOOkTp06MmnSJItT2daU09PTbV7mduWRI0eKq6urPProo+Lv7y+TJk2SatWqidFolPvuu08dt0JRFLnvvvvE1dVVHW/F3rKzs7MYDAab6jBfLpkwYYK8/fbbotfr1Ru6z507J0ajUR0SobhyrVq1xMHBQRITE+XcuXMSFRWlju1j7TqsqcPPz08uXbok7733nnh5eUlwcLB06NBBmjdvLs2bN5cOHTpIXFycPPHEE/LUU0/Jk08+KZ06dSoyz52WrakjLi5O+vbtK7Nnz5ZLly5ZHF8yMjLkjTfesLtcGusobp3WYEC5QwUHj/v+++9lzJgxFq9+/foJgBLLY8aMUXeK9pZvV0fdunXVG6M+/vhj8fDwUHcahw4dktjYWAFu3gX/999/y6+//qrezJWcnCyHDh1SH2G0t2xNHebrqsePH1cHCTJn6EWLFomiKOLu7q6WTSaTGI1GGTt2rIiIdOvWTQBI8+bNS1zG1nLhOiIiIgSA9OnTR90G2rRpIwBkzpw5InLzBu7Q0FDR6XSyf//+Oy6b12lLHYqiqI8JxsXFqU8AnD59Wq5evaqO63P06FER+d9j9uYbz+0pe3t7CwCb6zDfuxEVFSWKosjDDz8sIiL333+/OnCZuWy+ce/48eMiIhISEmJRLm4ZW8vF1QFAfvrppyKDOP7rX/9SyzExMVaXzf//JkyYIC+88IJd6yiu3LhxY3VgSb1eL76+vrJ06VIREZk6daoAkBdffFFE/ndTp3l7t6dsvufB1jq8vb3V/z/m8JyRkSEiogbCksrm4Rx27NghIiI//vijADfvfbJ2HdbWUXAQTpPJJE5OTuqAek5OThIbG6s+gGAymaRjx44W85hMJjGZTHaXranDyclJHnvsMYtBGM37jYLfk73l0lhHceu0BgPKHSp4A2LBm4e0+ircxsOHD6vjNPj7+1uUe/XqJbVq1ZLDhw+rO0B7y9bUYT4TUKtWLTl06JDaRhGRvLw8ix1dXl6e+iTNoUOHROTmDaIA1Me+i1vG1nJJdQQHB8tLL70k165dk3379glwcxAu8zTzIH59+/YtlbLIzYG4rK2jYECpWbOmbNiwQZ0mImp4MJdDQ0PVGz7tLX/44YcW67SmDmdnZ/H19RWRm+N4uLi4WJRdXV3Vxxl9fX3V8SnMAyyaQ1HBARcLL2NruaQ6zJ/j2rVr6v93vV6vnp00jyxsTbl79+4C3DyDGRgYKKNGjRJFUaRNmzZ2r1Ov16tj95hHqTUajWrQOnbsmABQb0oWEQFg8biorWVz0LK1joKPT5s/S05OjlVl8/g65lHCjx49qu4rrF2HtXUUHIRTp9NJ7969JTc3V3Jzc+Xxxx8XDw8P6d27t1y8eNGibJ7H19dXfH197S5bU0enTp2kcePG6qCBLVq0kHr16smSJUtkyZIlMnz4cAFQYnny5Mm3LFuzjuLWqSiKOmjp4sWLGVAqQmBgoCxbtqzEcnJysgBQpxUui4jFc/32lG9Xh6urqyxdulTdQMxlAOq4CsDNswIFywcOHFAHdDMPjmRv2Zo6zJdSzOWCAUXk5o6t4LgP5jEGzOHBvHM077iKW8bWckl17N27V+Lj4yUyMlL9a2vnzp3qtL1796qP95VW2cHBweo6AMimTZvEwcFBfH195b///a8oiiKZmZkiIuolPnM5MDBQFEVRDxz2lM2DSNlSR/v27dXtskePHnLvvfdalP38/MTT01Mtt23bVjw8PNQBFuvXry9Go9FiwMXCy9haLq6OggFF5OYfJhEREXL8+HF1/BfzWRdrysHBwQLcHF+nYPn06dN2r9McposbWFJE1AEczaOSmkfuNQdye8rmy6C21qHX69UBL80/o2Bt2TyEe/369eWHH36Qd955R32s3t51FlcHYDkIp6IoFmcmzH+YmKcVLouIegbW3rI1dZj/6NTiH8kF/4BnQKkAXbt2lddee63EsvmUpnla4bKISOvWrS0OxLaWb1dHkyZNZMKECWqoKVgePHiwek/D559/XqQsIjJ48GDR6/Xq8vaUranDvCEXLBf8nEFBQepf1yIiCxYsEAcHB3XnuGXLFjEYDBbD5xdextZycXXUqFFDfVRy0aJF6siY5p3GokWLxM/PT73cUtpla+ooeM+Mk5OTfPfdd6IoijRo0EDuv/9+9RFCc9nFxUVdxt6yeewOW+oYOXKk6PV6dbBE8+l+f39/qVGjhhoYC5br1q0rLi4u4u7urj5KaTQab7mMreXCdQCQBx54QB3EEYDFb4soiiKLFy+2upyfn29xRqtw2Z51jhkzxmJgydGjR4uTk5O4uLhIixYt1DMtOp1OWrRoIT4+PuLo6CiKothddnV1FUdHR5vqcHFxUR/XVRRFHBwcJDQ0VA1Y4eHh0qhRoxLL9erVk6lTp0rPnj3VaXXq1JHOnTtbvQ5r6nB3d7cYhNPT01Mti4gsW7ZMdDqdOq1wWeTmKLnm8GtP2Zo63NzcxMPDQx0k0MXFRVxdXWXr1q2ydetW9V61ksrz5s0TADJ58uRiy9aso7h1KopiMaimPQGlcjyTpmEjR47E5cuXSyyHhYVhxowZqFOnTrFlABgzZgwefvhhu8u3q+Pf//43Nm3ahJ9//rlIuXXr1sjPz8esWbOwaNEirFmzxqL81FNP4cMPP8SuXbuwY8cOALCrbE0d+/btw+bNm9VlCpYB4L777kNeXp5a/uOPP9CkSRN12g8//IAHHnjA4vspvIyt5eLq6NSpk/rIXO/evbFt2zb8/vvv6iOkvXv3xoMPPoikpCQEBwejXr16pVq2po5x48YhOzsbp06dQu3ateHk5IRx48apn6tatWr466+/8Oijj6rTFi9eDL1ej27dutlVTklJQf369dV1WlPHvn370K1bN9SpUwcrV65Un+zIycmBoigwGo3Iz8+3KJt/TdjV1RVXrlxBeno68vLybrmMreXCdRw7dgzJyck4ffo0WrRogcDAQItfZw0ODkb79u2tLiuKguDgYPWR1sJle9Y5fvx4ODk54bfffsOAAQMwatQoNGjQAIMGDcLOnTsRFRWFjRs34rnnnsOKFSug0+nQu3dvXLp0CWvWrLGrbH6KaMSIEVbX0bNnT7Ru3RqvvfYacnJy8K9//Qsffvih+mvz5kfir169esuyNfPcSTkxMRFPP/00Xn31VXTo0AHPPvss4uPj0a9fPwDA3Llz0axZMzz55JNo3bo1Nm/ejKZNm1rMYz4WjBgxwq6yNXXk5OSgWbNmuHDhAtavX4+rV6+iVatWaNGiBQAgMjIS27dvL7Fs/uy5ublo0aJFkbI16yhpneb91YULFyD2PDBsc6QhIiK6C5Q02CEKXL4AYHEZo/A8d1q+XR0FlwsICJCnnnpKvvjiC/UzfP755/L888+XWD537pwMHTpUXaZw2Zp1FLfO+fPnl1i2FsdBISIiuoXCg/MBKDJY3+3mudOyNXWYB2GsMmyONERERHe5tLQ0eeaZZ0osWzPPnZYrax3WYkAhIiKykRYG4aysdViLN8kSEREVsmLFCouy+aZ/s9OnTyM/Px9jx44ttgwAO3futJhma7my1nH69GmIiNqHR44csb7jC+A9KERERIXodDooiqI+fcJDpe0URbH4d8EnJK2hK+0GERERVXYBAQFYunQp8vPzkZ+fj8DAQCxbtgxy89YIJCcnA4A6rXBZRFC9enU15NhTrqx1JCcnQ6fTqX23e/duu74DBhQiIqJCoqOjLQ6shcvmswPmaYXLAFCvXj2LMy+2litrHQXPPBVXthbvQSEiIiqkMgzCqdU6wsLC1IFBiytbi/egEBERkebwEg8RERFpDgMKERERaQ4DChEREWkOAwoRERFpDgMKERERaQ4DChEREWkOAwoRERFpzv8DnYSxsBKk5MsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TEST_PROBS_DF.iloc[:,:].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "38a670e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Save the Best model, to use on the TEST data set PS2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "444cce5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "filename = \"./Saved_Models/RBF_SVM_C_1000_Gamma_02.pickle\"\n",
    "\n",
    "# save model\n",
    "pickle.dump(rbf_BEST_FINAL_MODEL, open(filename, \"wb\"))\n",
    "\n",
    "# load model\n",
    "#loaded_model = pickle.load(open(filename, \"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19bf410a",
   "metadata": {},
   "source": [
    "### Confusion Matrix on Test Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e713edd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test = rbf_BEST_FINAL_MODEL.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "87a37c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "Composers = ['Bach','Beethoven','Brahms','Schubert']\n",
    "\n",
    "c_matrix = pd.DataFrame(confusion_matrix(y_test,y_pred_test,normalize='true'))\n",
    "\n",
    "c_matrix.index = Composers\n",
    "c_matrix.columns = Composers\n",
    "\n",
    "sns.heatmap(data=c_matrix,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "935ecc35",
   "metadata": {},
   "source": [
    "## Can see that Bach is easiest to classify\n",
    "\n",
    "* Brahms is the next easiest, also Brahms has second highest probabilities in the plot above \n",
    "\n",
    "* "
   ]
  }
 ],
 "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}