ML_DM1_SupervisedLearning_SVC Dataset 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0efd6e8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time after training the sets and cross validation = 126.38368438100042 s\n",
      "[0.88709677 0.88709677 0.88172043 0.88172043 0.88709677 0.89189189\n",
      " 0.89189189]\n",
      "Accuracy score = 0.8869307095113548\n",
      "Test score = 0.9\n",
      "time for doing the learning curve = 1080.806872098 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd9a7b2c8e0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dataset 1 : Movies\n",
    "# Output : Awards : Yes or No\n",
    "# SVC() --> kernel = 'linear'\n",
    "\n",
    "import numpy as np\n",
    "import time\n",
    "import pandas as pd\n",
    "import io\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# split your datas between training and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "# comparing datas\n",
    "from sklearn.metrics import accuracy_score\n",
    "# model \n",
    "from sklearn.svm import SVC\n",
    "# cross validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# validation curve\n",
    "from yellowbrick.model_selection import validation_curve\n",
    "# learning curve\n",
    "from sklearn.model_selection import learning_curve\n",
    "# will be used to convert string into float\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder_X = LabelEncoder()\n",
    "\n",
    "#dataset\n",
    "a = io.open('Movie Dataset.csv', encoding='latin-1')\n",
    "df = pd.read_csv(a)\n",
    "df = df.take([i for i in range(1300)], axis = 0)\n",
    "df = df.apply(LabelEncoder().fit_transform)\n",
    "\n",
    "#input\n",
    "X = df.drop(columns=['Awards'])\n",
    "\n",
    "#output\n",
    "y = df['Awards']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "time1 = time.perf_counter()\n",
    "\n",
    "model = SVC(kernel = 'linear')\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "validation = cross_val_score(model, X, y, cv=7)\n",
    "\n",
    "#time after predicting the test sets\n",
    "time2 = time.perf_counter()\n",
    "timeA = time2 - time1\n",
    "print('time after training the sets and cross validation =',timeA,'s')\n",
    "\n",
    "print(validation)\n",
    "print('Accuracy score =', validation.mean())\n",
    "\n",
    "#Testing the test set\n",
    "predictions = model.predict(X_test)\n",
    "score = accuracy_score(y_test,predictions)\n",
    "print('Test score =', score)\n",
    "\n",
    "#Plot the learning curve\n",
    "train_sizes, train_scores, valid_scores = learning_curve(SVC(kernel = 'linear') , \n",
    "                                                         X , y , \n",
    "                                                         train_sizes=np.linspace(0.01,1,50), \n",
    "                                                         cv=7, \n",
    "                                                         scoring = 'accuracy', \n",
    "                                                         n_jobs= -1)\n",
    "\n",
    "time3 = time.perf_counter()\n",
    "timeB = time3 - time2\n",
    "print('time for doing the learning curve =',timeB,'s')\n",
    "\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "valid_std = np.std(valid_scores, axis = 1)\n",
    "\n",
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "valid_mean = np.mean(valid_scores, axis = 1)\n",
    "\n",
    "plt.plot(train_sizes,train_mean, label = 'Training score')\n",
    "plt.plot(train_sizes,valid_mean, label = 'Cross-validation score')\n",
    "\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color = '#DDDDDD')\n",
    "plt.fill_between(train_sizes, valid_mean - valid_std, valid_mean + valid_std, color = '#DDDDDD')\n",
    "\n",
    "\n",
    "plt.title('Learning curve')\n",
    "plt.xlabel('Training size')\n",
    "plt.ylabel('Accuracy score')\n",
    "plt.legend(loc ='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bd156103",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time after training the sets and cross validation = 1.1535702679975657 s\n",
      "[0.88709677 0.88709677 0.88172043 0.88172043 0.88172043 0.88648649\n",
      " 0.88648649]\n",
      "Accuracy score = 0.8846182588118071\n",
      "Test score = 0.8615384615384616\n",
      "time for doing the learning curve = 12.94036163000419 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd9af91beb0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dataset 1 : Movies\n",
    "# Output : Awards : Yes or No\n",
    "# SVC() --> kernel = 'poly'\n",
    "\n",
    "import numpy as np\n",
    "import time\n",
    "import pandas as pd\n",
    "import io\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# split your datas between training and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "# comparing datas\n",
    "from sklearn.metrics import accuracy_score\n",
    "# model \n",
    "from sklearn.svm import SVC\n",
    "# cross validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# validation curve\n",
    "from yellowbrick.model_selection import validation_curve\n",
    "# learning curve\n",
    "from sklearn.model_selection import learning_curve\n",
    "# will be used to convert string into float\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder_X = LabelEncoder()\n",
    "\n",
    "#dataset\n",
    "a = io.open('Movie Dataset.csv', encoding='latin-1')\n",
    "df = pd.read_csv(a)\n",
    "df = df.take([i for i in range(1300)], axis = 0)\n",
    "df = df.apply(LabelEncoder().fit_transform)\n",
    "\n",
    "#input\n",
    "X = df.drop(columns=['Awards'])\n",
    "\n",
    "#output\n",
    "y = df['Awards']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "time1 = time.perf_counter()\n",
    "\n",
    "model = SVC(kernel = 'poly')\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "validation = cross_val_score(model, X, y, cv=7)\n",
    "\n",
    "#time after predicting the test sets\n",
    "time2 = time.perf_counter()\n",
    "timeA = time2 - time1\n",
    "print('time after training the sets and cross validation =',timeA,'s')\n",
    "\n",
    "print(validation)\n",
    "print('Accuracy score =', validation.mean())\n",
    "\n",
    "#Testing the test set\n",
    "predictions = model.predict(X_test)\n",
    "score = accuracy_score(y_test,predictions)\n",
    "print('Test score =', score)\n",
    "\n",
    "#Plot the learning curve\n",
    "train_sizes, train_scores, valid_scores = learning_curve(SVC(kernel = 'poly') , \n",
    "                                                         X , y , \n",
    "                                                         train_sizes=np.linspace(0.01,1,50), \n",
    "                                                         cv=7, \n",
    "                                                         scoring = 'accuracy', \n",
    "                                                         n_jobs= -1)\n",
    "\n",
    "time3 = time.perf_counter()\n",
    "timeB = time3 - time2\n",
    "print('time for doing the learning curve =',timeB,'s')\n",
    "\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "valid_std = np.std(valid_scores, axis = 1)\n",
    "\n",
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "valid_mean = np.mean(valid_scores, axis = 1)\n",
    "\n",
    "plt.plot(train_sizes,train_mean, label = 'Training score')\n",
    "plt.plot(train_sizes,valid_mean, label = 'Cross-validation score')\n",
    "\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color = '#DDDDDD')\n",
    "plt.fill_between(train_sizes, valid_mean - valid_std, valid_mean + valid_std, color = '#DDDDDD')\n",
    "\n",
    "\n",
    "plt.title('Learning curve')\n",
    "plt.xlabel('Training size')\n",
    "plt.ylabel('Accuracy score')\n",
    "plt.legend(loc ='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "556383f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time after training the sets and cross validation = 0.4524657589990966 s\n",
      "[0.88709677 0.88709677 0.88172043 0.88172043 0.88172043 0.88648649\n",
      " 0.88648649]\n",
      "Accuracy score = 0.8846182588118071\n",
      "Test score = 0.8846153846153846\n",
      "time for doing the learning curve = 9.937501665999662 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd9ad065880>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dataset 1 : Movies\n",
    "# Output : Awards : Yes or No\n",
    "# SVC() --> kernel = 'rbf'\n",
    "\n",
    "import numpy as np\n",
    "import time\n",
    "import pandas as pd\n",
    "import io\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# split your datas between training and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "# comparing datas\n",
    "from sklearn.metrics import accuracy_score\n",
    "# model \n",
    "from sklearn.svm import SVC\n",
    "# cross validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# validation curve\n",
    "from yellowbrick.model_selection import validation_curve\n",
    "# learning curve\n",
    "from sklearn.model_selection import learning_curve\n",
    "# will be used to convert string into float\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder_X = LabelEncoder()\n",
    "\n",
    "#dataset\n",
    "a = io.open('Movie Dataset.csv', encoding='latin-1')\n",
    "df = pd.read_csv(a)\n",
    "df = df.take([i for i in range(1300)], axis = 0)\n",
    "df = df.apply(LabelEncoder().fit_transform)\n",
    "\n",
    "#input\n",
    "X = df.drop(columns=['Awards'])\n",
    "\n",
    "#output\n",
    "y = df['Awards']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "time1 = time.perf_counter()\n",
    "\n",
    "model = SVC(kernel = 'rbf')\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "validation = cross_val_score(model, X, y, cv=7)\n",
    "\n",
    "#time after predicting the test sets\n",
    "time2 = time.perf_counter()\n",
    "timeA = time2 - time1\n",
    "print('time after training the sets and cross validation =',timeA,'s')\n",
    "\n",
    "print(validation)\n",
    "print('Accuracy score =', validation.mean())\n",
    "\n",
    "#Testing the test set\n",
    "predictions = model.predict(X_test)\n",
    "score = accuracy_score(y_test,predictions)\n",
    "print('Test score =', score)\n",
    "\n",
    "#Plot the learning curve\n",
    "train_sizes, train_scores, valid_scores = learning_curve(SVC(kernel = 'rbf') , \n",
    "                                                         X , y , \n",
    "                                                         train_sizes=np.linspace(0.01,1,50), \n",
    "                                                         cv=7, \n",
    "                                                         scoring = 'accuracy', \n",
    "                                                         n_jobs= -1)\n",
    "\n",
    "time3 = time.perf_counter()\n",
    "timeB = time3 - time2\n",
    "print('time for doing the learning curve =',timeB,'s')\n",
    "\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "valid_std = np.std(valid_scores, axis = 1)\n",
    "\n",
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "valid_mean = np.mean(valid_scores, axis = 1)\n",
    "\n",
    "plt.plot(train_sizes,train_mean, label = 'Training score')\n",
    "plt.plot(train_sizes,valid_mean, label = 'Cross-validation score')\n",
    "\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color = '#DDDDDD')\n",
    "plt.fill_between(train_sizes, valid_mean - valid_std, valid_mean + valid_std, color = '#DDDDDD')\n",
    "\n",
    "\n",
    "plt.title('Learning curve')\n",
    "plt.xlabel('Training size')\n",
    "plt.ylabel('Accuracy score')\n",
    "plt.legend(loc ='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f1dcc379",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time after training the sets and cross validation = 0.22729876700032037 s\n",
      "[0.85483871 0.80645161 0.88172043 0.88172043 0.79032258 0.76756757\n",
      " 0.72972973]\n",
      "Accuracy score = 0.8160501515340225\n",
      "Test score = 0.8269230769230769\n",
      "time for doing the learning curve = 3.376475795999795 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd9ad74b1c0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dataset 1 : Movies\n",
    "# Output : Awards : Yes or No\n",
    "# SVC() --> kernel = 'sigmoid'\n",
    "\n",
    "import numpy as np\n",
    "import time\n",
    "import pandas as pd\n",
    "import io\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# split your datas between training and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "# comparing datas\n",
    "from sklearn.metrics import accuracy_score\n",
    "# model \n",
    "from sklearn.svm import SVC\n",
    "# cross validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# validation curve\n",
    "from yellowbrick.model_selection import validation_curve\n",
    "# learning curve\n",
    "from sklearn.model_selection import learning_curve\n",
    "# will be used to convert string into float\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder_X = LabelEncoder()\n",
    "\n",
    "#dataset\n",
    "a = io.open('Movie Dataset.csv', encoding='latin-1')\n",
    "df = pd.read_csv(a)\n",
    "df = df.take([i for i in range(1300)], axis = 0)\n",
    "df = df.apply(LabelEncoder().fit_transform)\n",
    "\n",
    "#input\n",
    "X = df.drop(columns=['Awards'])\n",
    "\n",
    "#output\n",
    "y = df['Awards']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "time1 = time.perf_counter()\n",
    "\n",
    "model = SVC(kernel = 'sigmoid')\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "validation = cross_val_score(model, X, y, cv=7)\n",
    "\n",
    "#time after predicting the test sets\n",
    "time2 = time.perf_counter()\n",
    "timeA = time2 - time1\n",
    "print('time after training the sets and cross validation =',timeA,'s')\n",
    "\n",
    "print(validation)\n",
    "print('Accuracy score =', validation.mean())\n",
    "\n",
    "#Testing the test set\n",
    "predictions = model.predict(X_test)\n",
    "score = accuracy_score(y_test,predictions)\n",
    "print('Test score =', score)\n",
    "\n",
    "#Plot the learning curve\n",
    "train_sizes, train_scores, valid_scores = learning_curve(SVC(kernel = 'sigmoid') , \n",
    "                                                         X , y , \n",
    "                                                         train_sizes=np.linspace(0.01,1,50), \n",
    "                                                         cv=7, \n",
    "                                                         scoring = 'accuracy', \n",
    "                                                         n_jobs= -1)\n",
    "\n",
    "time3 = time.perf_counter()\n",
    "timeB = time3 - time2\n",
    "print('time for doing the learning curve =',timeB,'s')\n",
    "\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "valid_std = np.std(valid_scores, axis = 1)\n",
    "\n",
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "valid_mean = np.mean(valid_scores, axis = 1)\n",
    "\n",
    "plt.plot(train_sizes,train_mean, label = 'Training score')\n",
    "plt.plot(train_sizes,valid_mean, label = 'Cross-validation score')\n",
    "\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color = '#DDDDDD')\n",
    "plt.fill_between(train_sizes, valid_mean - valid_std, valid_mean + valid_std, color = '#DDDDDD')\n",
    "\n",
    "\n",
    "plt.title('Learning curve')\n",
    "plt.xlabel('Training size')\n",
    "plt.ylabel('Accuracy score')\n",
    "plt.legend(loc ='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4ade2479",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Precomputed matrix must be a square matrix. Input is a 1040x8 matrix.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/ry/8803myjd0q12dyd2h81ryjcm0000gn/T/ipykernel_8870/2231066863.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'precomputed'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0mvalidation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"precomputed\"\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mn_samples\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    199\u001b[0m                 \u001b[0;34m\"Precomputed matrix must be a square matrix.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m                 \u001b[0;34m\" Input is a {}x{} matrix.\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Precomputed matrix must be a square matrix. Input is a 1040x8 matrix."
     ]
    }
   ],
   "source": [
    "# Dataset 1 : Movies\n",
    "# Output : Awards : Yes or No\n",
    "# SVC() --> kernel = 'precomputed'\n",
    "\n",
    "import numpy as np\n",
    "import time\n",
    "import pandas as pd\n",
    "import io\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# split your datas between training and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "# comparing datas\n",
    "from sklearn.metrics import accuracy_score\n",
    "# model \n",
    "from sklearn.svm import SVC\n",
    "# cross validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# validation curve\n",
    "from yellowbrick.model_selection import validation_curve\n",
    "# learning curve\n",
    "from sklearn.model_selection import learning_curve\n",
    "# will be used to convert string into float\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder_X = LabelEncoder()\n",
    "\n",
    "#dataset\n",
    "a = io.open('Movie Dataset.csv', encoding='latin-1')\n",
    "df = pd.read_csv(a)\n",
    "df = df.take([i for i in range(1300)], axis = 0)\n",
    "df = df.apply(LabelEncoder().fit_transform)\n",
    "\n",
    "#input\n",
    "X = df.drop(columns=['Awards'])\n",
    "\n",
    "#output\n",
    "y = df['Awards']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "time1 = time.perf_counter()\n",
    "\n",
    "model = SVC(kernel = 'precomputed')\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "validation = cross_val_score(model, X, y, cv=7)\n",
    "\n",
    "#time after predicting the test sets\n",
    "time2 = time.perf_counter()\n",
    "timeA = time2 - time1\n",
    "print('time after training the sets and cross validation =',timeA,'s')\n",
    "\n",
    "print(validation)\n",
    "print('Accuracy score =', validation.mean())\n",
    "\n",
    "#Testing the test set\n",
    "predictions = model.predict(X_test)\n",
    "score = accuracy_score(y_test,predictions)\n",
    "print('Test score =', score)\n",
    "\n",
    "#Plot the learning curve\n",
    "train_sizes, train_scores, valid_scores = learning_curve(SVC(kernel = 'sigmoid') , \n",
    "                                                         X , y , \n",
    "                                                         train_sizes=np.linspace(0.01,1,50), \n",
    "                                                         cv=7, \n",
    "                                                         scoring = 'accuracy', \n",
    "                                                         n_jobs= -1)\n",
    "\n",
    "time3 = time.perf_counter()\n",
    "timeB = time3 - time2\n",
    "print('time for doing the learning curve =',timeB,'s')\n",
    "\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "valid_std = np.std(valid_scores, axis = 1)\n",
    "\n",
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "valid_mean = np.mean(valid_scores, axis = 1)\n",
    "\n",
    "plt.plot(train_sizes,train_mean, label = 'Training score')\n",
    "plt.plot(train_sizes,valid_mean, label = 'Cross-validation score')\n",
    "\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color = '#DDDDDD')\n",
    "plt.fill_between(train_sizes, valid_mean - valid_std, valid_mean + valid_std, color = '#DDDDDD')\n",
    "\n",
    "\n",
    "plt.title('Learning curve')\n",
    "plt.xlabel('Training size')\n",
    "plt.ylabel('Accuracy score')\n",
    "plt.legend(loc ='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4a6b4636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ValidationCurve(ax=<AxesSubplot:title={'center':'Validation Curve for SVC'}, xlabel='C', ylabel='score'>,\n",
      "                cv=7, estimator=SVC(), param_name='C',\n",
      "                param_range=array([ 0.        ,  0.20408163,  0.40816327,  0.6122449 ,  0.81632653,\n",
      "        1.02040816,  1.2244898 ,  1.42857143,  1.63265306,  1.83673469,\n",
      "        2.04081633,  2.24489796,  2.44897959,  2.65306122,  2.85714286,\n",
      "        3.06122449,  3.26530612,  3.46938776,  3.67346939,  3.87755102,\n",
      "        4.08163265,  4.28571429,  4.48979592,  4.69387755,  4.89795918,\n",
      "        5.10204082,  5.30612245,  5.51020408,  5.71428571,  5.91836735,\n",
      "        6.12244898,  6.32653061,  6.53061224,  6.73469388,  6.93877551,\n",
      "        7.14285714,  7.34693878,  7.55102041,  7.75510204,  7.95918367,\n",
      "        8.16326531,  8.36734694,  8.57142857,  8.7755102 ,  8.97959184,\n",
      "        9.18367347,  9.3877551 ,  9.59183673,  9.79591837, 10.        ]))\n",
      "time for doing the validation and learning curve = 49.67319003599914 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd9a7a688e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hyperparameters : kernel = 'rbf'\n",
    "# Vary : C\n",
    "\n",
    "time1 = time.perf_counter()\n",
    "\n",
    "#validation curve\n",
    "print(validation_curve(SVC(kernel = 'rbf') , \n",
    "                       X , y , \n",
    "                       param_name ='C', \n",
    "                       param_range=np.linspace(0,10,50), \n",
    "                       cv=7, \n",
    "                       score='accuracy'))\n",
    "\n",
    "#learning curve\n",
    "train_sizes, train_scores, valid_scores = learning_curve(SVC(kernel = 'rbf') , \n",
    "                                                         X , y , \n",
    "                                                         train_sizes=np.linspace(0.01,1,50), \n",
    "                                                         cv=7, \n",
    "                                                         scoring = 'accuracy', \n",
    "                                                         n_jobs= -1)\n",
    "\n",
    "time2 = time.perf_counter()\n",
    "timeA = time2 - time1\n",
    "print('time for doing the validation and learning curve =',timeA,'s')\n",
    "\n",
    "#means\n",
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "valid_mean = np.mean(valid_scores, axis = 1)\n",
    "\n",
    "#standard deviation\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "valid_std = np.std(valid_scores, axis = 1)\n",
    "\n",
    "# plot the figure\n",
    "plt.plot(train_sizes,train_mean, label = 'Training score')\n",
    "plt.plot(train_sizes,valid_mean, label = 'Cross-validation score')\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color = '#DDDDDD')\n",
    "plt.fill_between(train_sizes, valid_mean - valid_std, valid_mean + valid_std, color = '#DDDDDD')\n",
    "\n",
    "plt.title('Learning curve')\n",
    "plt.xlabel('Training size')\n",
    "plt.ylabel('Accuracy score')\n",
    "plt.legend(loc = 'best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dcc5e899",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time after training the sets and cross validation = 0.42916972000057285 s\n",
      "[0.88709677 0.88709677 0.88172043 0.88172043 0.88172043 0.88648649\n",
      " 0.88648649]\n",
      "Accuracy score = 0.8846182588118071\n",
      "Test score = 0.8884615384615384\n",
      "time for doing the learning curve = 6.391628019999189 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd9a7a6d730>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hyperparameters : kernel = 'rbf', C = default\n",
    "# Vary : gamma = 'scale'\n",
    "\n",
    "time1 = time.perf_counter()\n",
    "\n",
    "model = SVC(kernel = 'rbf', gamma = 'scale')\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "validation = cross_val_score(model, X, y, cv=7)\n",
    "\n",
    "#time after predicting the test sets\n",
    "time2 = time.perf_counter()\n",
    "timeA = time2 - time1\n",
    "print('time after training the sets and cross validation =',timeA,'s')\n",
    "\n",
    "print(validation)\n",
    "print('Accuracy score =', validation.mean())\n",
    "\n",
    "#Testing the test set\n",
    "predictions = model.predict(X_test)\n",
    "score = accuracy_score(y_test,predictions)\n",
    "print('Test score =', score)\n",
    "\n",
    "#Plot the learning curve\n",
    "train_sizes, train_scores, valid_scores = learning_curve(SVC(kernel = 'rbf', gamma = 'scale') , \n",
    "                                                         X , y , \n",
    "                                                         train_sizes=np.linspace(0.01,1,50), \n",
    "                                                         cv=7, \n",
    "                                                         scoring = 'accuracy', \n",
    "                                                         n_jobs= -1)\n",
    "\n",
    "time3 = time.perf_counter()\n",
    "timeB = time3 - time2\n",
    "print('time for doing the learning curve =',timeB,'s')\n",
    "\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "valid_std = np.std(valid_scores, axis = 1)\n",
    "\n",
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "valid_mean = np.mean(valid_scores, axis = 1)\n",
    "\n",
    "plt.plot(train_sizes,train_mean, label = 'Training score')\n",
    "plt.plot(train_sizes,valid_mean, label = 'Cross-validation score')\n",
    "\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color = '#DDDDDD')\n",
    "plt.fill_between(train_sizes, valid_mean - valid_std, valid_mean + valid_std, color = '#DDDDDD')\n",
    "\n",
    "\n",
    "plt.title('Learning curve')\n",
    "plt.xlabel('Training size')\n",
    "plt.ylabel('Accuracy score')\n",
    "plt.legend(loc ='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d0152784",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time after training the sets and cross validation = 0.7437175920003938 s\n",
      "[0.88709677 0.88709677 0.88172043 0.88172043 0.88172043 0.88648649\n",
      " 0.88648649]\n",
      "Accuracy score = 0.8846182588118071\n",
      "Test score = 0.8884615384615384\n",
      "time for doing the learning curve = 17.063056536999284 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd9af2925b0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hyperparameters : kernel = 'rbf', C = default\n",
    "# Vary : gamma = 'auto'\n",
    "\n",
    "time1 = time.perf_counter()\n",
    "\n",
    "model = SVC(kernel = 'rbf', gamma = 'auto')\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "validation = cross_val_score(model, X, y, cv=7)\n",
    "\n",
    "#time after predicting the test sets\n",
    "time2 = time.perf_counter()\n",
    "timeA = time2 - time1\n",
    "print('time after training the sets and cross validation =',timeA,'s')\n",
    "\n",
    "print(validation)\n",
    "print('Accuracy score =', validation.mean())\n",
    "\n",
    "#Testing the test set\n",
    "predictions = model.predict(X_test)\n",
    "score = accuracy_score(y_test,predictions)\n",
    "print('Test score =', score)\n",
    "\n",
    "#Plot the learning curve\n",
    "train_sizes, train_scores, valid_scores = learning_curve(SVC(kernel = 'rbf', gamma = 'auto') , \n",
    "                                                         X , y , \n",
    "                                                         train_sizes=np.linspace(0.01,1,50), \n",
    "                                                         cv=7, \n",
    "                                                         scoring = 'accuracy', \n",
    "                                                         n_jobs= -1)\n",
    "\n",
    "time3 = time.perf_counter()\n",
    "timeB = time3 - time2\n",
    "print('time for doing the learning curve =',timeB,'s')\n",
    "\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "valid_std = np.std(valid_scores, axis = 1)\n",
    "\n",
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "valid_mean = np.mean(valid_scores, axis = 1)\n",
    "\n",
    "plt.plot(train_sizes,train_mean, label = 'Training score')\n",
    "plt.plot(train_sizes,valid_mean, label = 'Cross-validation score')\n",
    "\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color = '#DDDDDD')\n",
    "plt.fill_between(train_sizes, valid_mean - valid_std, valid_mean + valid_std, color = '#DDDDDD')\n",
    "\n",
    "\n",
    "plt.title('Learning curve')\n",
    "plt.xlabel('Training size')\n",
    "plt.ylabel('Accuracy score')\n",
    "plt.legend(loc ='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7935481",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}