SVM_GRIDSEARCH_Reduced.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.load('Xtrainhalf.npy')\n",
    "X_test = np.load('Xtesthalf.npy')\n",
    "Y_train = np.load('Ytrainhalf.npy')\n",
    "Y_test = np.load('Ytesthalf.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.40      0.50         5\n",
      "           1       1.00      0.60      0.75         5\n",
      "           2       0.50      0.40      0.44         5\n",
      "           3       0.80      0.80      0.80         5\n",
      "           4       0.40      0.40      0.40         5\n",
      "           5       0.40      0.40      0.40         5\n",
      "           6       0.00      0.00      0.00         5\n",
      "           7       0.50      0.20      0.29         5\n",
      "           8       1.00      0.80      0.89         5\n",
      "           9       0.00      0.00      0.00         5\n",
      "          10       1.00      1.00      1.00         5\n",
      "          11       1.00      0.20      0.33         5\n",
      "          12       1.00      0.60      0.75         5\n",
      "          13       1.00      0.80      0.89         5\n",
      "          14       0.33      0.40      0.36         5\n",
      "          15       0.18      0.60      0.27         5\n",
      "          16       0.80      0.80      0.80         5\n",
      "          17       1.00      0.20      0.33         5\n",
      "          18       0.17      0.20      0.18         5\n",
      "          19       0.45      1.00      0.62         5\n",
      "          20       0.80      0.80      0.80         5\n",
      "          21       0.20      0.20      0.20         5\n",
      "          22       0.40      0.80      0.53         5\n",
      "          23       0.00      0.00      0.00         5\n",
      "          24       0.06      0.20      0.10         5\n",
      "          25       0.62      1.00      0.77         5\n",
      "          26       0.60      0.60      0.60         5\n",
      "          27       0.67      0.80      0.73         5\n",
      "          28       0.50      0.60      0.55         5\n",
      "          29       0.62      1.00      0.77         5\n",
      "          30       0.67      0.40      0.50         5\n",
      "          31       0.25      0.20      0.22         5\n",
      "          32       0.42      1.00      0.59         5\n",
      "          33       1.00      0.40      0.57         5\n",
      "          34       0.50      0.20      0.29         5\n",
      "          35       0.29      0.40      0.33         5\n",
      "          36       0.22      0.80      0.35         5\n",
      "          37       0.33      0.20      0.25         5\n",
      "          38       0.00      0.00      0.00         5\n",
      "          39       0.50      0.60      0.55         5\n",
      "          40       0.50      0.20      0.29         5\n",
      "          41       0.67      0.40      0.50         5\n",
      "          42       0.50      0.20      0.29         5\n",
      "          43       1.00      0.20      0.33         5\n",
      "          44       0.71      1.00      0.83         5\n",
      "          45       0.33      0.20      0.25         5\n",
      "          46       0.29      0.40      0.33         5\n",
      "          47       1.00      0.40      0.57         5\n",
      "          48       0.38      0.60      0.46         5\n",
      "          49       0.43      0.60      0.50         5\n",
      "          50       0.33      1.00      0.50         5\n",
      "          51       0.60      0.60      0.60         5\n",
      "          52       1.00      0.40      0.57         5\n",
      "          53       1.00      1.00      1.00         5\n",
      "          54       1.00      0.60      0.75         5\n",
      "          55       0.83      1.00      0.91         5\n",
      "          56       0.27      0.80      0.40         5\n",
      "          57       0.23      0.60      0.33         5\n",
      "          58       0.50      0.60      0.55         5\n",
      "          59       1.00      0.80      0.89         5\n",
      "          60       1.00      1.00      1.00         5\n",
      "          61       0.71      1.00      0.83         5\n",
      "          62       0.18      0.40      0.25         5\n",
      "          63       1.00      0.60      0.75         5\n",
      "          64       0.00      0.00      0.00         5\n",
      "          65       0.00      0.00      0.00         5\n",
      "          66       0.00      0.00      0.00         5\n",
      "          67       1.00      1.00      1.00         5\n",
      "          68       0.50      1.00      0.67         5\n",
      "          69       0.60      0.60      0.60         5\n",
      "          70       0.20      0.40      0.27         5\n",
      "          71       1.00      0.20      0.33         5\n",
      "          72       0.50      0.60      0.55         5\n",
      "          73       0.56      1.00      0.71         5\n",
      "          74       1.00      0.20      0.33         5\n",
      "          75       1.00      0.20      0.33         5\n",
      "          76       0.43      0.60      0.50         5\n",
      "          77       1.00      0.40      0.57         5\n",
      "          78       0.83      1.00      0.91         5\n",
      "          79       1.00      0.60      0.75         5\n",
      "          80       0.00      0.00      0.00         5\n",
      "          81       0.22      0.40      0.29         5\n",
      "          82       0.08      0.20      0.12         5\n",
      "          83       1.00      0.60      0.75         5\n",
      "          84       0.50      0.20      0.29         5\n",
      "          85       1.00      0.40      0.57         5\n",
      "          86       0.75      0.60      0.67         5\n",
      "          87       0.00      0.00      0.00         5\n",
      "          88       1.00      0.40      0.57         5\n",
      "          89       0.00      0.00      0.00         5\n",
      "          90       0.33      0.40      0.36         5\n",
      "          91       1.00      0.80      0.89         5\n",
      "          92       0.33      0.20      0.25         5\n",
      "          93       0.67      0.40      0.50         5\n",
      "          94       1.00      0.60      0.75         5\n",
      "          95       0.83      1.00      0.91         5\n",
      "          96       0.67      0.40      0.50         5\n",
      "          97       1.00      0.20      0.33         5\n",
      "          98       1.00      0.20      0.33         5\n",
      "          99       1.00      0.80      0.89         5\n",
      "         100       0.71      1.00      0.83         5\n",
      "         101       0.25      0.80      0.38         5\n",
      "         102       0.50      0.60      0.55         5\n",
      "         103       0.80      0.80      0.80         5\n",
      "         104       1.00      0.80      0.89         5\n",
      "         105       1.00      0.80      0.89         5\n",
      "         106       1.00      0.20      0.33         5\n",
      "         107       0.25      0.20      0.22         5\n",
      "         108       1.00      1.00      1.00         5\n",
      "         109       0.17      0.40      0.24         5\n",
      "         110       0.67      0.40      0.50         5\n",
      "         111       0.10      0.40      0.15         5\n",
      "         112       1.00      0.40      0.57         5\n",
      "         113       1.00      1.00      1.00         5\n",
      "         114       0.50      0.80      0.62         5\n",
      "         115       0.00      0.00      0.00         5\n",
      "         116       1.00      0.40      0.57         5\n",
      "         117       0.60      0.60      0.60         5\n",
      "         118       0.75      0.60      0.67         5\n",
      "         119       0.50      0.60      0.55         5\n",
      "         120       0.80      0.80      0.80         5\n",
      "         121       0.67      0.40      0.50         5\n",
      "         122       0.38      0.60      0.46         5\n",
      "         123       0.36      0.80      0.50         5\n",
      "         124       1.00      0.20      0.33         5\n",
      "         125       0.83      1.00      0.91         5\n",
      "         126       1.00      0.40      0.57         5\n",
      "         127       0.75      0.60      0.67         5\n",
      "         128       1.00      0.20      0.33         5\n",
      "         129       0.60      0.60      0.60         5\n",
      "         130       0.80      0.80      0.80         5\n",
      "         131       0.50      0.60      0.55         5\n",
      "         132       0.00      0.00      0.00         5\n",
      "         133       0.40      0.40      0.40         5\n",
      "         134       1.00      0.20      0.33         5\n",
      "         135       0.36      0.80      0.50         5\n",
      "         136       1.00      1.00      1.00         5\n",
      "         137       0.33      0.20      0.25         5\n",
      "         138       0.62      1.00      0.77         5\n",
      "         139       0.11      0.20      0.14         5\n",
      "         140       0.00      0.00      0.00         5\n",
      "         141       0.57      0.80      0.67         5\n",
      "         142       0.00      0.00      0.00         5\n",
      "         143       0.05      0.20      0.08         5\n",
      "         144       0.00      0.00      0.00         5\n",
      "         145       1.00      0.40      0.57         5\n",
      "         146       0.67      0.40      0.50         5\n",
      "         147       0.00      0.00      0.00         5\n",
      "         148       1.00      1.00      1.00         5\n",
      "         149       0.75      0.60      0.67         5\n",
      "         150       0.50      0.20      0.29         5\n",
      "         151       0.83      1.00      0.91         5\n",
      "         152       0.60      0.60      0.60         5\n",
      "         153       1.00      0.60      0.75         5\n",
      "         154       0.60      0.60      0.60         5\n",
      "         155       0.67      0.40      0.50         5\n",
      "         156       0.71      1.00      0.83         5\n",
      "         157       0.71      1.00      0.83         5\n",
      "         158       0.60      0.60      0.60         5\n",
      "         159       0.00      0.00      0.00         5\n",
      "         160       0.00      0.00      0.00         5\n",
      "         161       0.00      0.00      0.00         5\n",
      "         162       0.50      0.60      0.55         5\n",
      "         163       1.00      0.60      0.75         5\n",
      "         164       0.71      1.00      0.83         5\n",
      "         165       0.62      1.00      0.77         5\n",
      "         166       1.00      0.40      0.57         5\n",
      "         167       0.00      0.00      0.00         5\n",
      "         168       1.00      0.20      0.33         5\n",
      "         169       1.00      0.40      0.57         5\n",
      "         170       0.62      1.00      0.77         5\n",
      "         171       0.50      0.40      0.44         5\n",
      "         172       0.00      0.00      0.00         5\n",
      "         173       1.00      0.20      0.33         5\n",
      "         174       1.00      0.20      0.33         5\n",
      "         175       0.67      0.80      0.73         5\n",
      "         176       0.50      0.40      0.44         5\n",
      "         177       0.29      0.40      0.33         5\n",
      "         178       0.57      0.80      0.67         5\n",
      "         179       0.57      0.80      0.67         5\n",
      "         180       0.57      0.80      0.67         5\n",
      "         181       0.50      1.00      0.67         5\n",
      "         182       0.67      0.40      0.50         5\n",
      "         183       0.38      0.60      0.46         5\n",
      "         184       1.00      0.60      0.75         5\n",
      "         185       1.00      0.40      0.57         5\n",
      "         186       0.33      1.00      0.50         5\n",
      "         187       0.40      0.40      0.40         5\n",
      "         188       0.62      1.00      0.77         5\n",
      "         189       0.50      0.60      0.55         5\n",
      "         190       0.67      0.80      0.73         5\n",
      "         191       0.67      0.40      0.50         5\n",
      "         192       1.00      0.20      0.33         5\n",
      "         193       0.75      0.60      0.67         5\n",
      "         194       0.50      0.40      0.44         5\n",
      "         195       0.56      1.00      0.71         5\n",
      "         196       1.00      0.20      0.33         5\n",
      "         197       1.00      1.00      1.00         5\n",
      "         198       0.50      0.20      0.29         5\n",
      "         199       0.71      1.00      0.83         5\n",
      "         200       0.75      0.60      0.67         5\n",
      "         201       1.00      0.20      0.33         5\n",
      "         202       0.50      0.80      0.62         5\n",
      "         203       0.67      0.80      0.73         5\n",
      "         204       0.50      0.20      0.29         5\n",
      "         205       1.00      0.80      0.89         5\n",
      "         206       0.00      0.00      0.00         5\n",
      "         207       0.13      0.40      0.20         5\n",
      "         208       1.00      0.60      0.75         5\n",
      "         209       0.00      0.00      0.00         5\n",
      "         210       0.83      1.00      0.91         5\n",
      "         211       0.60      0.60      0.60         5\n",
      "         212       0.00      0.00      0.00         5\n",
      "         213       0.75      0.60      0.67         5\n",
      "         214       0.43      0.60      0.50         5\n",
      "         215       0.00      0.00      0.00         5\n",
      "         216       0.00      0.00      0.00         5\n",
      "         217       0.20      0.40      0.27         5\n",
      "         218       0.00      0.00      0.00         5\n",
      "         219       0.33      0.80      0.47         5\n",
      "         220       0.71      1.00      0.83         5\n",
      "         221       0.71      1.00      0.83         5\n",
      "         222       0.50      0.40      0.44         5\n",
      "         223       0.27      0.60      0.37         5\n",
      "         224       0.20      0.20      0.20         5\n",
      "\n",
      "    accuracy                           0.51      1125\n",
      "   macro avg       0.58      0.51      0.49      1125\n",
      "weighted avg       0.58      0.51      0.49      1125\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sathyanarayanan.s\\.conda\\envs\\tf-gpu\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "model = SVC()\n",
    "model.fit(X_train , Y_train)\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "print(classification_report(Y_test , predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 25 candidates, totalling 125 fits\n",
      "[CV] C=0.1, gamma=1, kernel=linear ...................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....... C=0.1, gamma=1, kernel=linear, score=0.577, total=52.1min\n",
      "[CV] C=0.1, gamma=1, kernel=linear ...................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 52.1min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....... C=0.1, gamma=1, kernel=linear, score=0.558, total=52.2min\n",
      "[CV] C=0.1, gamma=1, kernel=linear ...................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed: 104.2min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....... C=0.1, gamma=1, kernel=linear, score=0.583, total=52.2min\n",
      "[CV] C=0.1, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=0.1, gamma=1, kernel=linear, score=0.551, total=52.2min\n",
      "[CV] C=0.1, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=0.1, gamma=1, kernel=linear, score=0.579, total=52.1min\n",
      "[CV] C=0.1, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=0.1, gamma=0.1, kernel=linear, score=0.577, total=52.1min\n",
      "[CV] C=0.1, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=0.1, gamma=0.1, kernel=linear, score=0.558, total=52.1min\n",
      "[CV] C=0.1, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=0.1, gamma=0.1, kernel=linear, score=0.583, total=52.1min\n",
      "[CV] C=0.1, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=0.1, gamma=0.1, kernel=linear, score=0.551, total=52.2min\n",
      "[CV] C=0.1, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=0.1, gamma=0.1, kernel=linear, score=0.579, total=52.1min\n",
      "[CV] C=0.1, gamma=0.01, kernel=linear ................................\n",
      "[CV] .... C=0.1, gamma=0.01, kernel=linear, score=0.577, total=52.1min\n",
      "[CV] C=0.1, gamma=0.01, kernel=linear ................................\n",
      "[CV] .... C=0.1, gamma=0.01, kernel=linear, score=0.558, total=52.0min\n",
      "[CV] C=0.1, gamma=0.01, kernel=linear ................................\n",
      "[CV] .... C=0.1, gamma=0.01, kernel=linear, score=0.583, total=51.9min\n",
      "[CV] C=0.1, gamma=0.01, kernel=linear ................................\n",
      "[CV] .... C=0.1, gamma=0.01, kernel=linear, score=0.551, total=51.9min\n",
      "[CV] C=0.1, gamma=0.01, kernel=linear ................................\n",
      "[CV] .... C=0.1, gamma=0.01, kernel=linear, score=0.579, total=51.9min\n",
      "[CV] C=0.1, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=0.1, gamma=0.001, kernel=linear, score=0.577, total=51.9min\n",
      "[CV] C=0.1, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=0.1, gamma=0.001, kernel=linear, score=0.558, total=51.8min\n",
      "[CV] C=0.1, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=0.1, gamma=0.001, kernel=linear, score=0.583, total=51.8min\n",
      "[CV] C=0.1, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=0.1, gamma=0.001, kernel=linear, score=0.551, total=51.8min\n",
      "[CV] C=0.1, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=0.1, gamma=0.001, kernel=linear, score=0.579, total=51.7min\n",
      "[CV] C=0.1, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=0.1, gamma=0.0001, kernel=linear, score=0.577, total=51.7min\n",
      "[CV] C=0.1, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=0.1, gamma=0.0001, kernel=linear, score=0.558, total=51.7min\n",
      "[CV] C=0.1, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=0.1, gamma=0.0001, kernel=linear, score=0.583, total=51.6min\n",
      "[CV] C=0.1, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=0.1, gamma=0.0001, kernel=linear, score=0.551, total=52.0min\n",
      "[CV] C=0.1, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=0.1, gamma=0.0001, kernel=linear, score=0.579, total=52.1min\n",
      "[CV] C=1, gamma=1, kernel=linear .....................................\n",
      "[CV] ......... C=1, gamma=1, kernel=linear, score=0.577, total=52.2min\n",
      "[CV] C=1, gamma=1, kernel=linear .....................................\n",
      "[CV] ......... C=1, gamma=1, kernel=linear, score=0.558, total=52.1min\n",
      "[CV] C=1, gamma=1, kernel=linear .....................................\n",
      "[CV] ......... C=1, gamma=1, kernel=linear, score=0.583, total=52.1min\n",
      "[CV] C=1, gamma=1, kernel=linear .....................................\n",
      "[CV] ......... C=1, gamma=1, kernel=linear, score=0.551, total=52.1min\n",
      "[CV] C=1, gamma=1, kernel=linear .....................................\n",
      "[CV] ......... C=1, gamma=1, kernel=linear, score=0.579, total=52.0min\n",
      "[CV] C=1, gamma=0.1, kernel=linear ...................................\n",
      "[CV] ....... C=1, gamma=0.1, kernel=linear, score=0.577, total=52.1min\n",
      "[CV] C=1, gamma=0.1, kernel=linear ...................................\n",
      "[CV] ....... C=1, gamma=0.1, kernel=linear, score=0.558, total=52.1min\n",
      "[CV] C=1, gamma=0.1, kernel=linear ...................................\n",
      "[CV] ....... C=1, gamma=0.1, kernel=linear, score=0.583, total=52.0min\n",
      "[CV] C=1, gamma=0.1, kernel=linear ...................................\n",
      "[CV] ....... C=1, gamma=0.1, kernel=linear, score=0.551, total=52.0min\n",
      "[CV] C=1, gamma=0.1, kernel=linear ...................................\n",
      "[CV] ....... C=1, gamma=0.1, kernel=linear, score=0.579, total=52.0min\n",
      "[CV] C=1, gamma=0.01, kernel=linear ..................................\n",
      "[CV] ...... C=1, gamma=0.01, kernel=linear, score=0.577, total=52.2min\n",
      "[CV] C=1, gamma=0.01, kernel=linear ..................................\n",
      "[CV] ...... C=1, gamma=0.01, kernel=linear, score=0.558, total=52.2min\n",
      "[CV] C=1, gamma=0.01, kernel=linear ..................................\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {'C' : [0.1 , 1 , 10 , 100 , 1000],\n",
    "             'gamma' : [1 , 0.1 , 0.01 , 0.001 , 0.0001],\n",
    "             'kernel' : ['linear']}\n",
    "\n",
    "grid = GridSearchCV(SVC() , param_grid , refit = True , verbose = 3)\n",
    "\n",
    "grid.fit(X_train , Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print best parameter after tuning\n",
    "print(grid.best_params_)\n",
    "\n",
    "# print how our model looks after hyper-parameter tuning\n",
    "print(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_predictions = grid.predict(X_test)\n",
    "\n",
    "print(classification_report(Y_test , grid_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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