SVM Classification Model -Repeat for 15 Second Audio Clips .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "95455eec",
   "metadata": {},
   "source": [
    "# Support Vector Machine Classifiers for audio files , features extracted from MIDI   \n",
    "\n",
    "Repeating earlier analysis, now on a larger data set of 15 second clips "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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": {
      "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>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",
       "</div>"
      ],
      "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = './DataStreamFeatures/DataFrames/'\n",
    "\n",
    "df_best_features = pd.read_csv(data_dir + 'BestMusicFeatures_15seconds.csv')\n",
    "\n",
    "df_best_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0334e9b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "480 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": 4,
   "id": "ed75210b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df_best_features['Composer_label'].to_numpy()\n",
    "\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "926ed373",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 480 samples instead of 240 for 30 seconds data set "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd1bf5d",
   "metadata": {},
   "source": [
    "# Scale Features using Standard Scalar"
   ]
  },
  {
   "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": [
      "480 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": [
      "(480, 15)\n",
      "(480, 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=10, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "rbf = svm.SVC(kernel='rbf', gamma=1, C=10, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "poly = svm.SVC(kernel='poly', degree=3, C=10, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "sig = svm.SVC(kernel='sigmoid', C=10, 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: 0.4305555555555556\n",
      "Accuracy Polynomial Kernel: 0.4027777777777778\n",
      "Accuracy Radial Basis Kernel: 0.4305555555555556\n",
      "Accuracy Sigmoid Kernel: 0.3194444444444444\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": "ff02819b",
   "metadata": {},
   "source": [
    "# TRY NEXT TWO FEATURES "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "954fa672",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "480 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": 13,
   "id": "97e8ac96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(480, 15)\n",
      "(480, 2)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "\n",
    "X = X[:,2:4]\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": "code",
   "execution_count": 14,
   "id": "687f7c07",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear = svm.SVC(kernel='linear', C=10, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "rbf = svm.SVC(kernel='rbf', gamma=1, C=10, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "poly = svm.SVC(kernel='poly', degree=3, C=10, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "sig = svm.SVC(kernel='sigmoid', C=10, decision_function_shape='ovo').fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aca5d265",
   "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": 16,
   "id": "db66001d",
   "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=0.3, hspace=0.3) \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[2])\n",
    "    plt.ylabel(training_features_list[3])\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": 17,
   "id": "b1b07bbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Linear Kernel: 0.3611111111111111\n",
      "Accuracy Polynomial Kernel: 0.3611111111111111\n",
      "Accuracy Radial Basis Kernel: 0.4166666666666667\n",
      "Accuracy Sigmoid Kernel: 0.3055555555555556\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": 18,
   "id": "92515a6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "480 samples with 15 features each <class 'numpy.ndarray'>\n",
      "(480, 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": 19,
   "id": "ad4ec8a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_all = svm.SVC(kernel='linear', C=1, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "rbf_all = svm.SVC(kernel='rbf', gamma=1, C=1, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "poly_all = svm.SVC(kernel='poly', degree=3, C=1, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "sig_all = svm.SVC(kernel='sigmoid', C=1, decision_function_shape='ovo').fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "475e2e35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Linear Kernel: 0.6111111111111112\n",
      "Accuracy Polynomial Kernel: 0.5138888888888888\n",
      "Accuracy Radial Basis Kernel: 0.625\n",
      "Accuracy Sigmoid Kernel: 0.5694444444444444\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": 21,
   "id": "6ca5fa35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "480 samples with 15 features each <class 'numpy.ndarray'>\n",
      "(480, 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": 45,
   "id": "e51bb382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" 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;: [1, 10, 100, 1000, 10000.0, 100000.0, 1000000.0,\n",
       "                                10000000.0, 100000000.0],\n",
       "                          &#x27;gamma&#x27;: [0.001], &#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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" 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;: [1, 10, 100, 1000, 10000.0, 100000.0, 1000000.0,\n",
       "                                10000000.0, 100000000.0],\n",
       "                          &#x27;gamma&#x27;: [0.001], &#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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" 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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" 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': [1, 10, 100, 1000, 10000.0, 100000.0, 1000000.0,\n",
       "                                10000000.0, 100000000.0],\n",
       "                          'gamma': [0.001], 'kernel': ['rbf']}])"
      ]
     },
     "execution_count": 45,
     "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-3],\n",
    "                     'C': [1,10,100,1000,1e4,1e5,1e6,1e7,1e8]}]\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": 46,
   "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>10000.0_0.001_rbf</th>\n",
       "      <td>{'C': 10000.0, 'gamma': 0.001, 'kernel': 'rbf'}</td>\n",
       "      <td>1</td>\n",
       "      <td>0.647917</td>\n",
       "      <td>0.048591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100000.0_0.001_rbf</th>\n",
       "      <td>{'C': 100000.0, 'gamma': 0.001, 'kernel': 'rbf'}</td>\n",
       "      <td>2</td>\n",
       "      <td>0.647083</td>\n",
       "      <td>0.040752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000_0.001_rbf</th>\n",
       "      <td>{'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}</td>\n",
       "      <td>3</td>\n",
       "      <td>0.637500</td>\n",
       "      <td>0.038302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000000.0_0.001_rbf</th>\n",
       "      <td>{'C': 1000000.0, 'gamma': 0.001, 'kernel': 'rbf'}</td>\n",
       "      <td>4</td>\n",
       "      <td>0.637083</td>\n",
       "      <td>0.040363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000000.0_0.001_rbf</th>\n",
       "      <td>{'C': 10000000.0, 'gamma': 0.001, 'kernel': 'r...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.637083</td>\n",
       "      <td>0.040363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100000000.0_0.001_rbf</th>\n",
       "      <td>{'C': 100000000.0, 'gamma': 0.001, 'kernel': '...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.637083</td>\n",
       "      <td>0.040363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_0.001_rbf</th>\n",
       "      <td>{'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}</td>\n",
       "      <td>7</td>\n",
       "      <td>0.602917</td>\n",
       "      <td>0.041596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10_0.001_rbf</th>\n",
       "      <td>{'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}</td>\n",
       "      <td>8</td>\n",
       "      <td>0.577083</td>\n",
       "      <td>0.041978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1_0.001_rbf</th>\n",
       "      <td>{'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}</td>\n",
       "      <td>9</td>\n",
       "      <td>0.522917</td>\n",
       "      <td>0.038752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  params  \\\n",
       "kernel                                                                     \n",
       "10000.0_0.001_rbf        {'C': 10000.0, 'gamma': 0.001, 'kernel': 'rbf'}   \n",
       "100000.0_0.001_rbf      {'C': 100000.0, 'gamma': 0.001, 'kernel': 'rbf'}   \n",
       "1000_0.001_rbf              {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}   \n",
       "1000000.0_0.001_rbf    {'C': 1000000.0, 'gamma': 0.001, 'kernel': 'rbf'}   \n",
       "10000000.0_0.001_rbf   {'C': 10000000.0, 'gamma': 0.001, 'kernel': 'r...   \n",
       "100000000.0_0.001_rbf  {'C': 100000000.0, 'gamma': 0.001, 'kernel': '...   \n",
       "100_0.001_rbf                {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}   \n",
       "10_0.001_rbf                  {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}   \n",
       "1_0.001_rbf                    {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}   \n",
       "\n",
       "                       rank_test_score  mean_test_score  std_test_score  \n",
       "kernel                                                                   \n",
       "10000.0_0.001_rbf                    1         0.647917        0.048591  \n",
       "100000.0_0.001_rbf                   2         0.647083        0.040752  \n",
       "1000_0.001_rbf                       3         0.637500        0.038302  \n",
       "1000000.0_0.001_rbf                  4         0.637083        0.040363  \n",
       "10000000.0_0.001_rbf                 4         0.637083        0.040363  \n",
       "100000000.0_0.001_rbf                4         0.637083        0.040363  \n",
       "100_0.001_rbf                        7         0.602917        0.041596  \n",
       "10_0.001_rbf                         8         0.577083        0.041978  \n",
       "1_0.001_rbf                          9         0.522917        0.038752  "
      ]
     },
     "execution_count": 46,
     "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": 47,
   "id": "109aab36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='log_10[C]'>"
      ]
     },
     "execution_count": 47,
     "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 = results_df.sort_values(by=[\"C\"])\n",
    "\n",
    "results_df.plot(x='log_10[C]',y='mean_test_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "459b9a76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score for training data: 0.6479166666666667 \n",
      "\n",
      "Best C: 10000.0 \n",
      "\n",
      "Best Kernel: rbf \n",
      "\n",
      "Best Gamma: 0.001 \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.best_estimator_.C\n",
    "\n",
    "print('Best C:',Best_C,\"\\n\") \n",
    "print('Best Kernel:',search.best_estimator_.kernel,\"\\n\")\n",
    "print('Best Gamma:',search.best_estimator_.gamma,\"\\n\")\n",
    "\n",
    "final_model = search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "46bc50e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 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-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 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-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 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-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 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-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" 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, 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-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" 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, 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-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" 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-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" 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, 1.0, 3.0],\n",
       "                          'kernel': ['rbf']}])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take best Value for C, and search gamma space now...\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': [Best_C]}]\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_gamma = GridSearchCV(estimator=svc, param_grid=param_grid, cv=cv)\n",
    "search_gamma.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "6c019070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='log_10[gamma]', ylabel='mean_test_score'>"
      ]
     },
     "execution_count": 98,
     "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_gamma.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": 99,
   "id": "6c4f9355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" 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;: [100.0],\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, 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-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" 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;: [100.0],\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, 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-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" 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-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" 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': [100.0],\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": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Repeat for smaller C, poor mans way to sweep gamma C space \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': [0.01*Best_C]}]\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_gamma_smallerC = GridSearchCV(estimator=svc, param_grid=param_grid, cv=cv)\n",
    "search_gamma_smallerC.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "e3a4bfe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GOING TO TAKE THIS VALUE OF C AS BEST MODEL, see results below, value of C not too important ! \n",
    "\n",
    "results_df_smallerC = pd.DataFrame(search_gamma_smallerC.cv_results_)\n",
    "\n",
    "results_df_smallerC['log_10[gamma]']=[np.log10(p['gamma']) for p in results_df_smallerC['params'].values]\n",
    "\n",
    "results_df_smallerC['C']=[p['C'] for p in results_df_smallerC['params'].values]\n",
    "\n",
    "results_df = pd.concat([results_df,results_df_smallerC])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "19003dca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='log_10[gamma]', ylabel='mean_test_score'>"
      ]
     },
     "execution_count": 101,
     "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": 102,
   "id": "08ba4146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 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-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 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-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 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-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 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-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" 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;: [10.0],\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, 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-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" 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;: [10.0],\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, 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-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" 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-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" 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': [10.0],\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": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Repeat for smaller C, poor mans way to sweep gamma C space \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': [0.001*Best_C]}]\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_gamma_smallerC_2 = GridSearchCV(estimator=svc, param_grid=param_grid, cv=cv)\n",
    "search_gamma_smallerC_2.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "1276c86a",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df_smallerC_2 = pd.DataFrame(search_gamma_smallerC_2.cv_results_)\n",
    "\n",
    "results_df_smallerC_2['log_10[gamma]']=[np.log10(p['gamma']) for p in results_df_smallerC_2['params'].values]\n",
    "\n",
    "results_df_smallerC_2['C']=[p['C'] for p in results_df_smallerC_2['params'].values]\n",
    "\n",
    "results_df = pd.concat([results_df,results_df_smallerC_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "036b9255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='log_10[gamma]', ylabel='mean_test_score'>"
      ]
     },
     "execution_count": 106,
     "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": 107,
   "id": "467b2ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 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-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 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-15 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-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 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-15 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-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 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-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 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-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 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-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" 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;: [1000000.0],\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, 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-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" 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;: [1000000.0],\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, 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-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" 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-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" 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': [1000000.0],\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": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Repeat for smaller C, poor mans way to sweep gamma C space \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*Best_C]}]\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_gamma_largerC = GridSearchCV(estimator=svc, param_grid=param_grid, cv=cv)\n",
    "search_gamma_largerC.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c4bf838c",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df_largerC = pd.DataFrame(search_gamma_largerC.cv_results_)\n",
    "\n",
    "results_df_largerC['log_10[gamma]']=[np.log10(p['gamma']) for p in results_df_largerC['params'].values]\n",
    "\n",
    "results_df_largerC['C']=[p['C'] for p in results_df_largerC['params'].values]\n",
    "\n",
    "results_df = pd.concat([results_df,results_df_largerC])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "8ddb034b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='log_10[gamma]', ylabel='mean_test_score'>"
      ]
     },
     "execution_count": 109,
     "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": 110,
   "id": "5ded7bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 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-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 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-16 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-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 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-16 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-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 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-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 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-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 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-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" 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;: [100000000.0],\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, 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-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" 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;: [100000000.0],\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, 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-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" 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-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" 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': [100000000.0],\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": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Repeat for smaller C, poor mans way to sweep gamma C space \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': [10000*Best_C]}]\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_gamma_largerC_2 = GridSearchCV(estimator=svc, param_grid=param_grid, cv=cv)\n",
    "search_gamma_largerC_2.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b3f27071",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df_largerC_2 = pd.DataFrame(search_gamma_largerC_2.cv_results_)\n",
    "\n",
    "results_df_largerC_2['log_10[gamma]']=[np.log10(p['gamma']) for p in results_df_largerC_2['params'].values]\n",
    "\n",
    "results_df_largerC_2['C']=[p['C'] for p in results_df_largerC_2['params'].values]\n",
    "\n",
    "results_df = pd.concat([results_df,results_df_largerC_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "9016be61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='log_10[gamma]', ylabel='mean_test_score'>"
      ]
     },
     "execution_count": 112,
     "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": "markdown",
   "id": "414f23dd",
   "metadata": {},
   "source": [
    "# Conclusions : Gamma and C search : \n",
    "\n",
    "* after searching appears that tuning the value of gamma is most important , optimal = gamma = 0.2 = 2e-1 \n",
    "\n",
    "* for optimal gamma value , C doesn't make much difference \n",
    "\n",
    "* all things being equal, makes sense to take moderate value of C , so will take C = 100 \n",
    "\n",
    "* see [RBF SVM Parameters](https://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html) guide which explains meaning and trades for different choices of C and Gamma\n",
    "\n",
    "* results above have more dependence on C, not sure why gamma is so much more dominant for our music data ???? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "ec22d44d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score for training data: 0.6987499999999999 \n",
      "\n",
      "Best C: 100.0 \n",
      "\n",
      "Best Kernel: rbf \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_gamma_smallerC.best_estimator_.C\n",
    "\n",
    "Best_GAMMA = search_gamma_smallerC.best_estimator_.gamma\n",
    "\n",
    "print('Best C:',Best_C,\"\\n\") \n",
    "print('Best Kernel:',search_gamma_smallerC.best_estimator_.kernel,\"\\n\")\n",
    "print('Best Gamma:',Best_GAMMA,\"\\n\")\n",
    "\n",
    "final_model = search_gamma_smallerC.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bdcb705",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TO PREDICT \n",
    "\n",
    "# need to apply standardScalar to the test data set with 22 features... then call predict method on model\n",
    "\n",
    "#Y_pred = final_model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "2e6e5b6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "480 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": 130,
   "id": "2cc79625",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.25042\n",
      "129 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.25708\n",
      "143 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.27333\n",
      "157 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.34125\n",
      "171 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.45250\n",
      "185 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.43625\n",
      "200 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.43292\n",
      "214 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.43625\n",
      "228 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.43625\n",
      "242 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.44208\n",
      "256 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.45083\n",
      "270 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.46875\n",
      "284 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.51125\n",
      "299 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.51917\n",
      "313 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.52000\n",
      "327 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.52667\n",
      "341 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.53792\n",
      "355 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.54500\n",
      "369 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.55500\n",
      "384 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.57583\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",
    "\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": 132,
   "id": "52cfd6a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 132,
     "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": [
    "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": null,
   "id": "dd4404e3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}