SVM Classification Model .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "95455eec",
   "metadata": {},
   "source": [
    "# Support Vector Machine Classifiers for audio files , features extracted from MIDI    30 second data set analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a2b49b4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm, datasets\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe13964d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>IntervalBetweenStrongestPitches</th>\n",
       "      <th>MostCommonPitchPrevalence</th>\n",
       "      <th>NoteDensity</th>\n",
       "      <th>AmountOfArpeggiation</th>\n",
       "      <th>MinimumNoteDuration</th>\n",
       "      <th>StepwiseMotion</th>\n",
       "      <th>IntervalBetweenStrongestPitchClasses</th>\n",
       "      <th>...</th>\n",
       "      <th>VariabilityOfNoteDuration</th>\n",
       "      <th>MelodicOctaves</th>\n",
       "      <th>PitchClassVariety</th>\n",
       "      <th>DistanceBetweenMostCommonMelodicIntervals</th>\n",
       "      <th>MostCommonPitchClass</th>\n",
       "      <th>MelodicTritones</th>\n",
       "      <th>RelativeStrengthOfTopPitchClasses</th>\n",
       "      <th>DirectionOfMotion</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>29.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.126263</td>\n",
       "      <td>6.439024</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.078750</td>\n",
       "      <td>0.015385</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.005128</td>\n",
       "      <td>0.906250</td>\n",
       "      <td>0.521212</td>\n",
       "      <td>Bach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.229592</td>\n",
       "      <td>6.373984</td>\n",
       "      <td>0.463918</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.427835</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082307</td>\n",
       "      <td>0.020619</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.005155</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>Bach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.247475</td>\n",
       "      <td>6.439024</td>\n",
       "      <td>0.474490</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.433673</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084916</td>\n",
       "      <td>0.025510</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.513889</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>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.242268</td>\n",
       "      <td>6.308943</td>\n",
       "      <td>0.484375</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.442708</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082494</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.005208</td>\n",
       "      <td>0.627119</td>\n",
       "      <td>0.503597</td>\n",
       "      <td>Bach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.099631</td>\n",
       "      <td>15.875000</td>\n",
       "      <td>0.252066</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.634298</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120471</td>\n",
       "      <td>0.037190</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.020661</td>\n",
       "      <td>0.941860</td>\n",
       "      <td>0.381148</td>\n",
       "      <td>Bach</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Range  PitchVariety  NumberOfCommonPitches  \\\n",
       "0   29.0          22.0                    5.0   \n",
       "1   24.0          20.0                    2.0   \n",
       "2   24.0          19.0                    2.0   \n",
       "3   24.0          20.0                    2.0   \n",
       "4   48.0          46.0                    2.0   \n",
       "\n",
       "   IntervalBetweenStrongestPitches  MostCommonPitchPrevalence  NoteDensity  \\\n",
       "0                              5.0                   0.126263     6.439024   \n",
       "1                              7.0                   0.229592     6.373984   \n",
       "2                              7.0                   0.247475     6.439024   \n",
       "3                              7.0                   0.242268     6.308943   \n",
       "4                              2.0                   0.099631    15.875000   \n",
       "\n",
       "   AmountOfArpeggiation  MinimumNoteDuration  StepwiseMotion  \\\n",
       "0              0.384615                0.125        0.538462   \n",
       "1              0.463918                0.125        0.427835   \n",
       "2              0.474490                0.125        0.433673   \n",
       "3              0.484375                0.125        0.442708   \n",
       "4              0.252066                0.125        0.634298   \n",
       "\n",
       "   IntervalBetweenStrongestPitchClasses  ...  VariabilityOfNoteDuration  \\\n",
       "0                                   2.0  ...                   0.078750   \n",
       "1                                   5.0  ...                   0.082307   \n",
       "2                                   5.0  ...                   0.084916   \n",
       "3                                   5.0  ...                   0.082494   \n",
       "4                                   2.0  ...                   0.120471   \n",
       "\n",
       "   MelodicOctaves  PitchClassVariety  \\\n",
       "0        0.015385               11.0   \n",
       "1        0.020619               11.0   \n",
       "2        0.025510               11.0   \n",
       "3        0.020833               11.0   \n",
       "4        0.037190               12.0   \n",
       "\n",
       "   DistanceBetweenMostCommonMelodicIntervals  MostCommonPitchClass  \\\n",
       "0                                        1.0                   7.0   \n",
       "1                                        2.0                   2.0   \n",
       "2                                        2.0                   2.0   \n",
       "3                                        2.0                   2.0   \n",
       "4                                        1.0                   6.0   \n",
       "\n",
       "   MelodicTritones  RelativeStrengthOfTopPitchClasses  DirectionOfMotion  \\\n",
       "0         0.005128                           0.906250           0.521212   \n",
       "1         0.005155                           0.666667           0.541667   \n",
       "2         0.000000                           0.684211           0.513889   \n",
       "3         0.005208                           0.627119           0.503597   \n",
       "4         0.020661                           0.941860           0.381148   \n",
       "\n",
       "   Composer_name  Composer_label  \n",
       "0           Bach             0.0  \n",
       "1           Bach             0.0  \n",
       "2           Bach             0.0  \n",
       "3           Bach             0.0  \n",
       "4           Bach             0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = './DataStreamFeatures/DataFrames/'\n",
    "\n",
    "df_best_features = pd.read_csv(data_dir + 'BestMusicFeatures_30seconds.csv')\n",
    "\n",
    "df_best_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0334e9b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240 samples with 22 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": [
       "(240,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df_best_features['Composer_label'].to_numpy()\n",
    "\n",
    "y.shape"
   ]
  },
  {
   "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": 5,
   "id": "7059bd05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240 samples with 22 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": 6,
   "id": "5fe9bb8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(240, 22)\n",
      "(240, 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": 7,
   "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=0.1, 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": 8,
   "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": 9,
   "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": 10,
   "id": "8285e87f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Linear Kernel: 0.5277777777777778\n",
      "Accuracy Polynomial Kernel: 0.5277777777777778\n",
      "Accuracy Radial Basis Kernel: 0.5555555555555556\n",
      "Accuracy Sigmoid Kernel: 0.25\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": 11,
   "id": "92515a6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240 samples with 22 features each <class 'numpy.ndarray'>\n",
      "(240, 22)\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": 12,
   "id": "ad4ec8a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_all = svm.SVC(kernel='linear', C=10, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "rbf_all = svm.SVC(kernel='rbf', gamma=0.1, C=10, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "poly_all = svm.SVC(kernel='poly', degree=3, C=10, decision_function_shape='ovo').fit(X_train, y_train)\n",
    "sig_all = svm.SVC(kernel='sigmoid', C=10, decision_function_shape='ovo').fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "475e2e35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Linear Kernel: 0.5833333333333334\n",
      "Accuracy Polynomial Kernel: 0.6944444444444444\n",
      "Accuracy Radial Basis Kernel: 0.75\n",
      "Accuracy Sigmoid Kernel: 0.5277777777777778\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": [
    "# Grid Search RBF 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)\n",
    "\n",
    "- First time I tried this, RBF was getting 0.3\n",
    "\n",
    "- after first analysis, realized RBF does very well, but needs to gave gamma and C tuned well \n",
    "\n",
    "- gamma and C values above from C, gamma grid search done on larger data set of 15 second clips \n",
    "\n",
    "- will show linear here for comparison , to show RBF does seem to do best, once C and Gamma tuned \n",
    "\n",
    "\n",
    "- Gamma more important than C value , so going to search slices of gamma , at fixed C "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6ca5fa35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240 samples with 22 features each <class 'numpy.ndarray'>\n",
      "(240, 22)\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": 15,
   "id": "e51bb382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{&#x27;C&#x27;: [100],\n",
       "                          &#x27;gamma&#x27;: [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7, 1.0, 3.0],\n",
       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{&#x27;C&#x27;: [100],\n",
       "                          &#x27;gamma&#x27;: [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7, 1.0, 3.0],\n",
       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{'C': [100],\n",
       "                          'gamma': [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7, 1.0, 3.0],\n",
       "                          'kernel': ['rbf']}])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV, RepeatedStratifiedKFold\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "param_grid = [{'kernel': ['rbf'], 'gamma': [1e-4,1e-3,3e-3,1e-2,3e-2,7e-2,1e-1,2e-1,3e-1,7e-1,1e0,3e0],\n",
    "                     'C': [100]}]\n",
    "\n",
    "# not sure why, grid search didn't like the polynomial kernel...\n",
    "\n",
    "svc = SVC(random_state=0)\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=0)\n",
    "\n",
    "search = GridSearchCV(estimator=svc, param_grid=param_grid, cv=cv)\n",
    "search.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bfff8374",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df_C1 = pd.DataFrame(search.cv_results_)\n",
    "\n",
    "results_df_C1['log_10[gamma]']=[np.log10(p['gamma']) for p in results_df_C1['params'].values]\n",
    "\n",
    "results_df_C1['C']=[p['C'] for p in results_df_C1['params'].values]\n",
    "\n",
    "results_df = results_df_C1\n",
    "\n",
    "#results_df = pd.concat([results_df,results_df_C1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eaabb0d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='log_10[gamma]', ylabel='mean_test_score'>"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "id": "8dcdf789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{&#x27;C&#x27;: [1000],\n",
       "                          &#x27;gamma&#x27;: [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7, 1.0, 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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{&#x27;C&#x27;: [1000],\n",
       "                          &#x27;gamma&#x27;: [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7, 1.0, 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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{'C': [1000],\n",
       "                          'gamma': [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7, 1.0, 3.0],\n",
       "                          'kernel': ['rbf']}])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = [{'kernel': ['rbf'], 'gamma': [1e-4,1e-3,3e-3,1e-2,3e-2,7e-2,1e-1,2e-1,3e-1,7e-1,1e0,3e0],\n",
    "                     'C': [1000]}]\n",
    "\n",
    "# not sure why, grid search didn't like the polynomial kernel...\n",
    "\n",
    "svc = SVC(random_state=0)\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=0)\n",
    "\n",
    "search_C2 = GridSearchCV(estimator=svc, param_grid=param_grid, cv=cv)\n",
    "search_C2.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "89ae3c89",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df_C2 = pd.DataFrame(search_C2.cv_results_)\n",
    "\n",
    "results_df_C2['log_10[gamma]']=[np.log10(p['gamma']) for p in results_df_C2['params'].values]\n",
    "\n",
    "results_df_C2['C']=[p['C'] for p in results_df_C2['params'].values]\n",
    "\n",
    "results_df = pd.concat([results_df,results_df_C2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "19b61c51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='log_10[gamma]', ylabel='mean_test_score'>"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "id": "9aa919f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{&#x27;C&#x27;: [10000.0],\n",
       "                          &#x27;gamma&#x27;: [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7],\n",
       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{&#x27;C&#x27;: [10000.0],\n",
       "                          &#x27;gamma&#x27;: [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7],\n",
       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=5, random_state=0),\n",
       "             estimator=SVC(random_state=0),\n",
       "             param_grid=[{'C': [10000.0],\n",
       "                          'gamma': [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7],\n",
       "                          'kernel': ['rbf']}])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = [{'kernel': ['rbf'], 'gamma': [1e-4,1e-3,3e-3,1e-2,3e-2,7e-2,1e-1,2e-1,3e-1,7e-1],\n",
    "                     'C': [1e4]}]\n",
    "\n",
    "# not sure why, grid search didn't like the polynomial kernel...\n",
    "\n",
    "svc = SVC(random_state=0)\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=0)\n",
    "\n",
    "search_C3 = GridSearchCV(estimator=svc, param_grid=param_grid, cv=cv)\n",
    "search_C3.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "664448b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df_C3 = pd.DataFrame(search_C3.cv_results_)\n",
    "\n",
    "results_df_C3['log_10[gamma]']=[np.log10(p['gamma']) for p in results_df_C3['params'].values]\n",
    "\n",
    "results_df_C3['C']=[p['C'] for p in results_df_C3['params'].values]\n",
    "\n",
    "results_df = pd.concat([results_df,results_df_C3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "10b0248c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='log_10[gamma]', ylabel='mean_test_score'>"
      ]
     },
     "execution_count": 23,
     "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": 24,
   "id": "1ae7acfc",
   "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;: [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],\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-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;: [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],\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-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': [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],\n",
       "                          'kernel': ['rbf']}])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = [{'kernel': ['rbf'], 'gamma': [1e-4,1e-3,3e-3,1e-2,3e-2,7e-2,1e-1,2e-1,3e-1,7e-1],\n",
    "                     'C': [1e6]}]\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_C4 = GridSearchCV(estimator=svc, param_grid=param_grid, cv=cv)\n",
    "search_C4.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "32fe70c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df_C4 = pd.DataFrame(search_C4.cv_results_)\n",
    "\n",
    "results_df_C4['log_10[gamma]']=[np.log10(p['gamma']) for p in results_df_C4['params'].values]\n",
    "\n",
    "results_df_C4['C']=[p['C'] for p in results_df_C4['params'].values]\n",
    "\n",
    "results_df = pd.concat([results_df,results_df_C4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "50d1351c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='log_10[gamma]', ylabel='mean_test_score'>"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "id": "f9904e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" 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;: [1000000000.0],\n",
       "                          &#x27;gamma&#x27;: [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7],\n",
       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" 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;: [1000000000.0],\n",
       "                          &#x27;gamma&#x27;: [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7],\n",
       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;]}])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" 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-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" 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': [1000000000.0],\n",
       "                          'gamma': [0.0001, 0.001, 0.003, 0.01, 0.03, 0.07, 0.1,\n",
       "                                    0.2, 0.3, 0.7],\n",
       "                          'kernel': ['rbf']}])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = [{'kernel': ['rbf'], 'gamma': [1e-4,1e-3,3e-3,1e-2,3e-2,7e-2,1e-1,2e-1,3e-1,7e-1],\n",
    "                     'C': [1e9]}]\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_C5 = GridSearchCV(estimator=svc, param_grid=param_grid, cv=cv)\n",
    "search_C5.fit(X_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e1f95350",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df_C5 = pd.DataFrame(search_C5.cv_results_)\n",
    "\n",
    "results_df_C5['log_10[gamma]']=[np.log10(p['gamma']) for p in results_df_C5['params'].values]\n",
    "\n",
    "results_df_C5['C']=[p['C'] for p in results_df_C5['params'].values]\n",
    "\n",
    "results_df = pd.concat([results_df,results_df_C5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "26c8857c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='log_10[gamma]', ylabel='mean_test_score'>"
      ]
     },
     "execution_count": 29,
     "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": 30,
   "id": "459b9a76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score for training data: 0.6791666666666667 \n",
      "\n",
      "Best C: 1000 \n",
      "\n",
      "Best Kernel: rbf \n",
      "\n",
      "Best Gamma: 0.03 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# View the accuracy score\n",
    "print('Best score for training data:', search.best_score_,\"\\n\") \n",
    "\n",
    "# View the best parameters for the model found using grid search\n",
    "\n",
    "Best_C = search_C2.best_estimator_.C\n",
    "\n",
    "Best_GAMMA = search_C2.best_estimator_.gamma\n",
    "\n",
    "print('Best C:',Best_C,\"\\n\") \n",
    "print('Best Kernel:',search_C2.best_estimator_.kernel,\"\\n\")\n",
    "print('Best Gamma:',Best_GAMMA,\"\\n\")\n",
    "\n",
    "final_model = search_C2.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": "markdown",
   "id": "2724add9",
   "metadata": {},
   "source": [
    "# Try model with grid search parameters and top two features ? \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "12e0c469",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240 samples with 22 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": 33,
   "id": "36edd3fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(240, 22)\n",
      "(240, 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": "code",
   "execution_count": 34,
   "id": "62260580",
   "metadata": {},
   "outputs": [],
   "source": [
    "rbf_two_features = svm.SVC(kernel='rbf', gamma=Best_GAMMA, C=Best_C, decision_function_shape='ovo').fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "05f588d2",
   "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 = [f'RBF g = {Best_GAMMA} C = {Best_C}']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e02a3279",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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((rbf_two_features,)):\n",
    "    #defines how many plots: 2 rows, 2columns=> leading to 4 plots\n",
    "    plt.subplot(1, 1, 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": 37,
   "id": "aee59581",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEST Accuracy Radial Basis Kernel two features: 0.5555555555555556\n",
      "TRAIN Accuracy Radial Basis Kernel two features: 0.5637254901960784\n"
     ]
    }
   ],
   "source": [
    "# retrieve the accuracy and print it for all 4 kernel functions\n",
    "\n",
    "accuracy_rbf_two_features_test = rbf_two_features.score(X_test, y_test)\n",
    "\n",
    "accuracy_rbf_two_features_train = rbf_two_features.score(X_train, y_train)\n",
    "\n",
    "print('TEST Accuracy Radial Basis Kernel two features:', accuracy_rbf_two_features_test)\n",
    "\n",
    "print('TRAIN Accuracy Radial Basis Kernel two features:', accuracy_rbf_two_features_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c9c268d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if time, search choice of C , see how it effects this, do we want more regularization ? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14ac5aa2",
   "metadata": {},
   "source": [
    "# Learning Curves...   Want to decide if it's worth it to collect larger data set of 30 second clips\n",
    "\n",
    "\n",
    "* 30 seconds clips best model had avg accuracy 0.67 \n",
    "\n",
    "* 15 seconds clips with twice the data had accur 0.69 \n",
    "\n",
    "* not sure if I would get much better performance going to larger data set of 30 second clips ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dfcb9de7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240 samples with 22 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": 40,
   "id": "b29e7b0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.43167\n",
      "64 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.45667\n",
      "71 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.46250\n",
      "78 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.46500\n",
      "85 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.46917\n",
      "92 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.47333\n",
      "100 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.48000\n",
      "107 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.51000\n",
      "114 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.54917\n",
      "121 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.57167\n",
      "128 samples were used to train the model\n",
      "The average train accuracy is 0.99969\n",
      "The average test accuracy is 0.58917\n",
      "135 samples were used to train the model\n",
      "The average train accuracy is 1.00000\n",
      "The average test accuracy is 0.60667\n",
      "142 samples were used to train the model\n",
      "The average train accuracy is 0.99915\n",
      "The average test accuracy is 0.61500\n",
      "149 samples were used to train the model\n",
      "The average train accuracy is 0.99758\n",
      "The average test accuracy is 0.62000\n",
      "156 samples were used to train the model\n",
      "The average train accuracy is 0.99128\n",
      "The average test accuracy is 0.62833\n",
      "163 samples were used to train the model\n",
      "The average train accuracy is 0.99141\n",
      "The average test accuracy is 0.63083\n",
      "170 samples were used to train the model\n",
      "The average train accuracy is 0.99247\n",
      "The average test accuracy is 0.62833\n",
      "177 samples were used to train the model\n",
      "The average train accuracy is 0.99277\n",
      "The average test accuracy is 0.64917\n",
      "184 samples were used to train the model\n",
      "The average train accuracy is 0.99304\n",
      "The average test accuracy is 0.67250\n",
      "192 samples were used to train the model\n",
      "The average train accuracy is 0.99125\n",
      "The average test accuracy is 0.67083\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "svm_best_params = svm.SVC(kernel='rbf', gamma=Best_GAMMA, C=0.01*Best_C, decision_function_shape='ovo')\n",
    "\n",
    "cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=0)\n",
    "\n",
    "X = scaler.fit_transform(X)\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": 42,
   "id": "5935d1d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learing_results_df = pd.DataFrame([])\n",
    "\n",
    "\n",
    "learning_df = pd.DataFrame(list(zip(cv_train_list, cv_test_list)),\n",
    "               columns =['train','test'],index=size_list)\n",
    "\n",
    "learning_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9d9325ac",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "predict_proba is not available when  probability=False",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[44], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfinal_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_proba\u001b[49m(X_test)\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/sklearn/utils/metaestimators.py:127\u001b[0m, in \u001b[0;36m_AvailableIfDescriptor.__get__\u001b[0;34m(self, obj, owner)\u001b[0m\n\u001b[1;32m    121\u001b[0m attr_err \u001b[38;5;241m=\u001b[39m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m    122\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mrepr\u001b[39m(owner\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mrepr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattribute_name)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    123\u001b[0m )\n\u001b[1;32m    124\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    125\u001b[0m     \u001b[38;5;66;03m# delegate only on instances, not the classes.\u001b[39;00m\n\u001b[1;32m    126\u001b[0m     \u001b[38;5;66;03m# this is to allow access to the docstrings.\u001b[39;00m\n\u001b[0;32m--> 127\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheck\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    128\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m attr_err\n\u001b[1;32m    129\u001b[0m     out \u001b[38;5;241m=\u001b[39m MethodType(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfn, obj)\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/sklearn/svm/_base.py:819\u001b[0m, in \u001b[0;36mBaseSVC._check_proba\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    817\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_check_proba\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    818\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprobability:\n\u001b[0;32m--> 819\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m    820\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredict_proba is not available when  probability=False\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    821\u001b[0m         )\n\u001b[1;32m    822\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_impl \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mc_svc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnu_svc\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    823\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredict_proba only implemented for SVC and NuSVC\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mAttributeError\u001b[0m: predict_proba is not available when  probability=False"
     ]
    }
   ],
   "source": [
    "final_model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dac8450f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240 samples with 22 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": null,
   "id": "e4bac75d",
   "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
}