Model-implementation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Language_vocab</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Visual_discrimination</th>\n",
       "      <th>Audio_Discrimination</th>\n",
       "      <th>Survey_Score</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Language_vocab  Memory  Speed  Visual_discrimination  \\\n",
       "0               0.5     0.6    0.5                    0.8   \n",
       "1               0.6     0.7    0.8                    0.9   \n",
       "2               0.6     0.4    0.3                    0.3   \n",
       "3               0.3     0.5    0.2                    0.1   \n",
       "4               0.7     0.6    0.7                    0.8   \n",
       "..              ...     ...    ...                    ...   \n",
       "495             0.4     0.3    0.1                    0.4   \n",
       "496             0.4     0.6    0.5                    0.6   \n",
       "497             0.7     0.5    0.8                    0.9   \n",
       "498             0.3     0.5    0.4                    0.6   \n",
       "499             0.7     0.7    0.8                    0.6   \n",
       "\n",
       "     Audio_Discrimination  Survey_Score  Label  \n",
       "0                     0.6           0.7    1.0  \n",
       "1                     0.5           0.8    2.0  \n",
       "2                     0.4           0.6    1.0  \n",
       "3                     0.3           0.5    0.0  \n",
       "4                     0.9           0.5    2.0  \n",
       "..                    ...           ...    ...  \n",
       "495                   0.2           0.5    0.0  \n",
       "496                   0.5           0.4    1.0  \n",
       "497                   0.8           0.5    1.0  \n",
       "498                   0.6           0.6    1.0  \n",
       "499                   0.8           0.6    2.0  \n",
       "\n",
       "[500 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('labelled_data_new.csv')\n",
    "y=data.Label\n",
    "X=data.drop(['Label'],axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Language_vocab', 'Memory', 'Speed', 'Visual_discrimination',\n",
       "       'Audio_Discrimination', 'Survey_Score', 'Label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "prec=[0,0,0,0,0]\n",
    "rec=[0,0,0,0,0]\n",
    "f1=[0,0,0,0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.7,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test2=X_test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.34038599,  0.11560035,  0.63822535,  1.14716531,  0.62539939,\n",
       "         0.52455605],\n",
       "       [ 2.16245217,  0.57191753, -0.26068359,  0.19384234,  0.12905067,\n",
       "         0.52455605],\n",
       "       [ 0.16018164,  0.11560035,  0.18877088, -1.23614212, -0.36729805,\n",
       "         0.06709438],\n",
       "       ...,\n",
       "       [ 0.16018164, -0.34071682, -0.26068359,  2.10048828,  0.62539939,\n",
       "         0.52455605],\n",
       "       [-0.34038599,  1.0282347 ,  1.98658875,  0.19384234, -0.36729805,\n",
       "        -0.39036729],\n",
       "       [ 0.16018164,  1.94086905,  1.08767981,  1.62382679,  1.61809683,\n",
       "        -0.39036729]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc=StandardScaler(copy=False)\n",
    "sc.fit_transform(X_train)\n",
    "sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Language_vocab</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Visual_discrimination</th>\n",
       "      <th>Audio_Discrimination</th>\n",
       "      <th>Survey_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1.661885</td>\n",
       "      <td>1.484552</td>\n",
       "      <td>0.638225</td>\n",
       "      <td>1.147165</td>\n",
       "      <td>1.121748</td>\n",
       "      <td>0.067094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>-0.840954</td>\n",
       "      <td>-0.797034</td>\n",
       "      <td>0.188771</td>\n",
       "      <td>-0.282819</td>\n",
       "      <td>-0.863647</td>\n",
       "      <td>-0.390367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.160182</td>\n",
       "      <td>0.571918</td>\n",
       "      <td>0.188771</td>\n",
       "      <td>0.193842</td>\n",
       "      <td>0.129051</td>\n",
       "      <td>0.524556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2.162452</td>\n",
       "      <td>1.028235</td>\n",
       "      <td>1.537134</td>\n",
       "      <td>-0.282819</td>\n",
       "      <td>0.625399</td>\n",
       "      <td>0.982018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>-0.340386</td>\n",
       "      <td>-0.340717</td>\n",
       "      <td>0.188771</td>\n",
       "      <td>-0.759481</td>\n",
       "      <td>-0.367298</td>\n",
       "      <td>0.067094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>0.660749</td>\n",
       "      <td>0.115600</td>\n",
       "      <td>1.537134</td>\n",
       "      <td>-0.282819</td>\n",
       "      <td>1.121748</td>\n",
       "      <td>0.982018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.160182</td>\n",
       "      <td>-0.340717</td>\n",
       "      <td>-0.260684</td>\n",
       "      <td>-0.282819</td>\n",
       "      <td>-0.863647</td>\n",
       "      <td>-0.847829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>-0.340386</td>\n",
       "      <td>-0.340717</td>\n",
       "      <td>-0.260684</td>\n",
       "      <td>0.670504</td>\n",
       "      <td>-0.863647</td>\n",
       "      <td>0.524556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>-0.340386</td>\n",
       "      <td>-0.340717</td>\n",
       "      <td>0.188771</td>\n",
       "      <td>0.193842</td>\n",
       "      <td>-0.863647</td>\n",
       "      <td>-0.390367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>-0.840954</td>\n",
       "      <td>0.571918</td>\n",
       "      <td>0.638225</td>\n",
       "      <td>-0.759481</td>\n",
       "      <td>0.129051</td>\n",
       "      <td>0.067094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Language_vocab    Memory     Speed  Visual_discrimination  \\\n",
       "67         1.661885  1.484552  0.638225               1.147165   \n",
       "322       -0.840954 -0.797034  0.188771              -0.282819   \n",
       "96         0.160182  0.571918  0.188771               0.193842   \n",
       "22         2.162452  1.028235  1.537134              -0.282819   \n",
       "254       -0.340386 -0.340717  0.188771              -0.759481   \n",
       "..              ...       ...       ...                    ...   \n",
       "320        0.660749  0.115600  1.537134              -0.282819   \n",
       "15         0.160182 -0.340717 -0.260684              -0.282819   \n",
       "484       -0.340386 -0.340717 -0.260684               0.670504   \n",
       "125       -0.340386 -0.340717  0.188771               0.193842   \n",
       "265       -0.840954  0.571918  0.638225              -0.759481   \n",
       "\n",
       "     Audio_Discrimination  Survey_Score  \n",
       "67               1.121748      0.067094  \n",
       "322             -0.863647     -0.390367  \n",
       "96               0.129051      0.524556  \n",
       "22               0.625399      0.982018  \n",
       "254             -0.367298      0.067094  \n",
       "..                    ...           ...  \n",
       "320              1.121748      0.982018  \n",
       "15              -0.863647     -0.847829  \n",
       "484             -0.863647      0.524556  \n",
       "125             -0.863647     -0.390367  \n",
       "265              0.129051      0.067094  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM with linear kernel - Precision = 0.905, Recall = 0.935, F1-score = 0.919\n"
     ]
    }
   ],
   "source": [
    "clf=SVC(kernel='linear')\n",
    "clf.fit(X_train,y_train)\n",
    "pre=clf.predict(X_test)\n",
    "prec[0],rec[0],f1[0],_=precision_recall_fscore_support(y_test,pre,average='macro')\n",
    "print(\"SVM with linear kernel - Precision = %0.3f, Recall = %0.3f,\n",
    "      F1-score = %0.3f\" %(prec[0],rec[0],f1[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM with rbf kernel - Precision = 0.909, Recall = 0.942, F1-score = 0.923\n"
     ]
    }
   ],
   "source": [
    "clf2=SVC(kernel='rbf')\n",
    "clf2.fit(X_train,y_train)\n",
    "pre2=clf2.predict(X_test)\n",
    "prec[1],rec[1],f1[1],_=precision_recall_fscore_support(y_test,pre2,average='macro')\n",
    "print(\"SVM with rbf kernel - Precision = %0.3f, Recall = %0.3f, F1-score = %0.3f\"\n",
    "      %(prec[1],rec[1],f1[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters set found on development set:\n",
      "{'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n",
    "                     'C': [1, 10, 100, 1000]},\n",
    "                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]\n",
    "clf3 = GridSearchCV(SVC(), tuned_parameters,scoring='f1_macro')\n",
    "clf3.fit(X_train, y_train)\n",
    "print(\"Best parameters set found on development set:\")\n",
    "print(clf3.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.234 for {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.234 for {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.909 for {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.234 for {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.947 for {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.909 for {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.953 for {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
      "0.947 for {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
      "0.942 for {'C': 1, 'kernel': 'linear'}\n",
      "0.941 for {'C': 10, 'kernel': 'linear'}\n",
      "0.930 for {'C': 100, 'kernel': 'linear'}\n",
      "0.930 for {'C': 1000, 'kernel': 'linear'}\n"
     ]
    }
   ],
   "source": [
    "means = clf3.cv_results_['mean_test_score']\n",
    "for mean, params in zip(means, clf3.cv_results_['params']):\n",
    "        print(\"%0.3f for %r\"% (mean, params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gridsearch with SVM - Precision = 0.919, Recall = 0.949, F1-score = 0.933\n"
     ]
    }
   ],
   "source": [
    "pre3=clf3.predict(X_test)\n",
    "prec[2],rec[2],f1[2],_=precision_recall_fscore_support(y_test,pre3,average='macro')\n",
    "print(\"Gridsearch with SVM - Precision = %0.3f, Recall = %0.3f, F1-score = %0.3f\"\n",
    "      %(prec[2],rec[2],f1[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random forest classifier - Precision = 0.904, Recall = 0.896, F1-score = 0.899\n"
     ]
    }
   ],
   "source": [
    "clf4=RandomForestClassifier(random_state=0)\n",
    "clf4.fit(X_train,y_train)\n",
    "pre4=clf4.predict(X_test)\n",
    "prec[3],rec[3],f1[3],_=precision_recall_fscore_support(y_test,pre4,average='macro')\n",
    "print(\"Random forest classifier - Precision = %0.3f, Recall = %0.3f,\n",
    "      F1-score = %0.3f\" %(prec[3],rec[3],f1[3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters set found on development set:\n",
      "{'n_estimators': 1000}\n"
     ]
    }
   ],
   "source": [
    "param2={'n_estimators' : [10,100,500,1000]}\n",
    "clf5=GridSearchCV(RandomForestClassifier(random_state=0),param2,scoring='f1_macro')\n",
    "clf5.fit(X_train,y_train)\n",
    "print(\"Best parameters set found on development set:\")\n",
    "print(clf5.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.893 for {'n_estimators': 10}\n",
      "0.932 for {'n_estimators': 100}\n",
      "0.929 for {'n_estimators': 500}\n",
      "0.935 for {'n_estimators': 1000}\n"
     ]
    }
   ],
   "source": [
    "means1 = clf5.cv_results_['mean_test_score']\n",
    "for mean1, params1 in zip(means1, clf5.cv_results_['params']):\n",
    "        print(\"%0.3f for %r\"% (mean1, params1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gridsearch with Random forst classifier - Precision = 0.904, Recall = 0.919, F1-score = 0.911\n"
     ]
    }
   ],
   "source": [
    "pre5=clf5.predict(X_test)\n",
    "prec[4],rec[4],f1[4],_=precision_recall_fscore_support(y_test,pre5,average='macro')\n",
    "print(\"Gridsearch with Random forst classifier - Precision = %0.3f, Recall = %0.3f,\n",
    "      F1-score = %0.3f\" %(prec[4],rec[4],f1[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x259817a3088>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.lineplot(x=['SVM linear ','SVM rbf','SVM grid search','RandomForest','RandomForest gridsearch'],y=prec,label='precision')\n",
    "sns.lineplot(x=['SVM linear ','SVM rbf','SVM grid search','RandomForest','RandomForest gridsearch'],y=rec,label='recall')\n",
    "sns.lineplot(x=['SVM linear ','SVM rbf','SVM grid search','RandomForest','RandomForest gridsearch'],y=f1,label='f1-score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Language_vocab</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Visual_discrimination</th>\n",
       "      <th>Audio_Discrimination</th>\n",
       "      <th>Survey_Score</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Language_vocab  Memory  Speed  Visual_discrimination  \\\n",
       "151             0.5     0.6    0.7                    0.8   \n",
       "424             1.0     0.7    0.5                    0.6   \n",
       "154             0.6     0.6    0.6                    0.3   \n",
       "190             0.3     0.3    0.5                    0.5   \n",
       "131             0.3     0.3    0.3                    0.2   \n",
       "..              ...     ...    ...                    ...   \n",
       "445             0.3     0.3    0.3                    0.4   \n",
       "443             0.2     0.3    0.2                    0.2   \n",
       "399             0.6     0.5    0.5                    1.0   \n",
       "82              0.5     0.8    1.0                    0.6   \n",
       "95              0.6     1.0    0.8                    0.9   \n",
       "\n",
       "     Audio_Discrimination  Survey_Score  Label  \n",
       "151                   0.7           0.7    1.0  \n",
       "424                   0.6           0.7    2.0  \n",
       "154                   0.5           0.6    1.0  \n",
       "190                   0.6           0.7    1.0  \n",
       "131                   0.4           0.2    0.0  \n",
       "..                    ...           ...    ...  \n",
       "445                   0.3           0.0    0.0  \n",
       "443                   0.3           0.3    0.0  \n",
       "399                   0.7           0.7    1.0  \n",
       "82                    0.5           0.5    1.0  \n",
       "95                    0.9           0.5    2.0  \n",
       "\n",
       "[350 rows x 7 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([X_test2,y_test],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151    1.0\n",
       "424    2.0\n",
       "154    1.0\n",
       "190    1.0\n",
       "131    0.0\n",
       "      ... \n",
       "445    0.0\n",
       "443    0.0\n",
       "399    1.0\n",
       "82     2.0\n",
       "95     2.0\n",
       "Length: 350, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(pre3,index=X_test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}