ML_AI_4_SVM_Metrics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# The purpose of this code is review metrics and scoring."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://scikit-learn.org/stable/modules/model_evaluation.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import sklearn \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import accuracy_score\n",
    "import decimal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"human-activity-recognition-with-smartphones.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe shape : (7352, 563)\n"
     ]
    }
   ],
   "source": [
    "print(f\"Dataframe shape : {df.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>tBodyAcc-mean()-X</th>\n",
       "      <th>tBodyAcc-mean()-Y</th>\n",
       "      <th>tBodyAcc-mean()-Z</th>\n",
       "      <th>tBodyAcc-std()-X</th>\n",
       "      <th>tBodyAcc-std()-Y</th>\n",
       "      <th>tBodyAcc-std()-Z</th>\n",
       "      <th>tBodyAcc-mad()-X</th>\n",
       "      <th>tBodyAcc-mad()-Y</th>\n",
       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
       "      <th>angle(tBodyAccMean,gravity)</th>\n",
       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
       "      <th>angle(X,gravityMean)</th>\n",
       "      <th>angle(Y,gravityMean)</th>\n",
       "      <th>angle(Z,gravityMean)</th>\n",
       "      <th>subject</th>\n",
       "      <th>Activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.288585</td>\n",
       "      <td>-0.020294</td>\n",
       "      <td>-0.132905</td>\n",
       "      <td>-0.995279</td>\n",
       "      <td>-0.983111</td>\n",
       "      <td>-0.913526</td>\n",
       "      <td>-0.995112</td>\n",
       "      <td>-0.983185</td>\n",
       "      <td>-0.923527</td>\n",
       "      <td>-0.934724</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.710304</td>\n",
       "      <td>-0.112754</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>-0.464761</td>\n",
       "      <td>-0.018446</td>\n",
       "      <td>-0.841247</td>\n",
       "      <td>0.179941</td>\n",
       "      <td>-0.058627</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.278419</td>\n",
       "      <td>-0.016411</td>\n",
       "      <td>-0.123520</td>\n",
       "      <td>-0.998245</td>\n",
       "      <td>-0.975300</td>\n",
       "      <td>-0.960322</td>\n",
       "      <td>-0.998807</td>\n",
       "      <td>-0.974914</td>\n",
       "      <td>-0.957686</td>\n",
       "      <td>-0.943068</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.861499</td>\n",
       "      <td>0.053477</td>\n",
       "      <td>-0.007435</td>\n",
       "      <td>-0.732626</td>\n",
       "      <td>0.703511</td>\n",
       "      <td>-0.844788</td>\n",
       "      <td>0.180289</td>\n",
       "      <td>-0.054317</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.279653</td>\n",
       "      <td>-0.019467</td>\n",
       "      <td>-0.113462</td>\n",
       "      <td>-0.995380</td>\n",
       "      <td>-0.967187</td>\n",
       "      <td>-0.978944</td>\n",
       "      <td>-0.996520</td>\n",
       "      <td>-0.963668</td>\n",
       "      <td>-0.977469</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.760104</td>\n",
       "      <td>-0.118559</td>\n",
       "      <td>0.177899</td>\n",
       "      <td>0.100699</td>\n",
       "      <td>0.808529</td>\n",
       "      <td>-0.848933</td>\n",
       "      <td>0.180637</td>\n",
       "      <td>-0.049118</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.279174</td>\n",
       "      <td>-0.026201</td>\n",
       "      <td>-0.123283</td>\n",
       "      <td>-0.996091</td>\n",
       "      <td>-0.983403</td>\n",
       "      <td>-0.990675</td>\n",
       "      <td>-0.997099</td>\n",
       "      <td>-0.982750</td>\n",
       "      <td>-0.989302</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.482845</td>\n",
       "      <td>-0.036788</td>\n",
       "      <td>-0.012892</td>\n",
       "      <td>0.640011</td>\n",
       "      <td>-0.485366</td>\n",
       "      <td>-0.848649</td>\n",
       "      <td>0.181935</td>\n",
       "      <td>-0.047663</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.276629</td>\n",
       "      <td>-0.016570</td>\n",
       "      <td>-0.115362</td>\n",
       "      <td>-0.998139</td>\n",
       "      <td>-0.980817</td>\n",
       "      <td>-0.990482</td>\n",
       "      <td>-0.998321</td>\n",
       "      <td>-0.979672</td>\n",
       "      <td>-0.990441</td>\n",
       "      <td>-0.942469</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.699205</td>\n",
       "      <td>0.123320</td>\n",
       "      <td>0.122542</td>\n",
       "      <td>0.693578</td>\n",
       "      <td>-0.615971</td>\n",
       "      <td>-0.847865</td>\n",
       "      <td>0.185151</td>\n",
       "      <td>-0.043892</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.277199</td>\n",
       "      <td>-0.010098</td>\n",
       "      <td>-0.105137</td>\n",
       "      <td>-0.997335</td>\n",
       "      <td>-0.990487</td>\n",
       "      <td>-0.995420</td>\n",
       "      <td>-0.997627</td>\n",
       "      <td>-0.990218</td>\n",
       "      <td>-0.995549</td>\n",
       "      <td>-0.942469</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.844619</td>\n",
       "      <td>0.082632</td>\n",
       "      <td>-0.143439</td>\n",
       "      <td>0.275041</td>\n",
       "      <td>-0.368224</td>\n",
       "      <td>-0.849632</td>\n",
       "      <td>0.184823</td>\n",
       "      <td>-0.042126</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.279454</td>\n",
       "      <td>-0.019641</td>\n",
       "      <td>-0.110022</td>\n",
       "      <td>-0.996921</td>\n",
       "      <td>-0.967186</td>\n",
       "      <td>-0.983118</td>\n",
       "      <td>-0.997003</td>\n",
       "      <td>-0.966097</td>\n",
       "      <td>-0.983116</td>\n",
       "      <td>-0.940987</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.564430</td>\n",
       "      <td>-0.212754</td>\n",
       "      <td>-0.230622</td>\n",
       "      <td>0.014637</td>\n",
       "      <td>-0.189512</td>\n",
       "      <td>-0.852150</td>\n",
       "      <td>0.182170</td>\n",
       "      <td>-0.043010</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.277432</td>\n",
       "      <td>-0.030488</td>\n",
       "      <td>-0.125360</td>\n",
       "      <td>-0.996559</td>\n",
       "      <td>-0.966728</td>\n",
       "      <td>-0.981585</td>\n",
       "      <td>-0.996485</td>\n",
       "      <td>-0.966313</td>\n",
       "      <td>-0.982982</td>\n",
       "      <td>-0.940987</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.421715</td>\n",
       "      <td>-0.020888</td>\n",
       "      <td>0.593996</td>\n",
       "      <td>-0.561871</td>\n",
       "      <td>0.467383</td>\n",
       "      <td>-0.851017</td>\n",
       "      <td>0.183779</td>\n",
       "      <td>-0.041976</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.277293</td>\n",
       "      <td>-0.021751</td>\n",
       "      <td>-0.120751</td>\n",
       "      <td>-0.997328</td>\n",
       "      <td>-0.961245</td>\n",
       "      <td>-0.983672</td>\n",
       "      <td>-0.997596</td>\n",
       "      <td>-0.957236</td>\n",
       "      <td>-0.984379</td>\n",
       "      <td>-0.940598</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.572995</td>\n",
       "      <td>0.012954</td>\n",
       "      <td>0.080936</td>\n",
       "      <td>-0.234313</td>\n",
       "      <td>0.117797</td>\n",
       "      <td>-0.847971</td>\n",
       "      <td>0.188982</td>\n",
       "      <td>-0.037364</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.280586</td>\n",
       "      <td>-0.009960</td>\n",
       "      <td>-0.106065</td>\n",
       "      <td>-0.994803</td>\n",
       "      <td>-0.972758</td>\n",
       "      <td>-0.986244</td>\n",
       "      <td>-0.995405</td>\n",
       "      <td>-0.973663</td>\n",
       "      <td>-0.985642</td>\n",
       "      <td>-0.940028</td>\n",
       "      <td>...</td>\n",
       "      <td>0.140452</td>\n",
       "      <td>-0.020590</td>\n",
       "      <td>-0.127730</td>\n",
       "      <td>-0.482871</td>\n",
       "      <td>-0.070670</td>\n",
       "      <td>-0.848294</td>\n",
       "      <td>0.190310</td>\n",
       "      <td>-0.034417</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 563 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
       "0           0.288585          -0.020294          -0.132905         -0.995279   \n",
       "1           0.278419          -0.016411          -0.123520         -0.998245   \n",
       "2           0.279653          -0.019467          -0.113462         -0.995380   \n",
       "3           0.279174          -0.026201          -0.123283         -0.996091   \n",
       "4           0.276629          -0.016570          -0.115362         -0.998139   \n",
       "5           0.277199          -0.010098          -0.105137         -0.997335   \n",
       "6           0.279454          -0.019641          -0.110022         -0.996921   \n",
       "7           0.277432          -0.030488          -0.125360         -0.996559   \n",
       "8           0.277293          -0.021751          -0.120751         -0.997328   \n",
       "9           0.280586          -0.009960          -0.106065         -0.994803   \n",
       "\n",
       "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
       "0         -0.983111         -0.913526         -0.995112         -0.983185   \n",
       "1         -0.975300         -0.960322         -0.998807         -0.974914   \n",
       "2         -0.967187         -0.978944         -0.996520         -0.963668   \n",
       "3         -0.983403         -0.990675         -0.997099         -0.982750   \n",
       "4         -0.980817         -0.990482         -0.998321         -0.979672   \n",
       "5         -0.990487         -0.995420         -0.997627         -0.990218   \n",
       "6         -0.967186         -0.983118         -0.997003         -0.966097   \n",
       "7         -0.966728         -0.981585         -0.996485         -0.966313   \n",
       "8         -0.961245         -0.983672         -0.997596         -0.957236   \n",
       "9         -0.972758         -0.986244         -0.995405         -0.973663   \n",
       "\n",
       "   tBodyAcc-mad()-Z  tBodyAcc-max()-X  ...  fBodyBodyGyroJerkMag-kurtosis()  \\\n",
       "0         -0.923527         -0.934724  ...                        -0.710304   \n",
       "1         -0.957686         -0.943068  ...                        -0.861499   \n",
       "2         -0.977469         -0.938692  ...                        -0.760104   \n",
       "3         -0.989302         -0.938692  ...                        -0.482845   \n",
       "4         -0.990441         -0.942469  ...                        -0.699205   \n",
       "5         -0.995549         -0.942469  ...                        -0.844619   \n",
       "6         -0.983116         -0.940987  ...                        -0.564430   \n",
       "7         -0.982982         -0.940987  ...                        -0.421715   \n",
       "8         -0.984379         -0.940598  ...                        -0.572995   \n",
       "9         -0.985642         -0.940028  ...                         0.140452   \n",
       "\n",
       "   angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
       "0                    -0.112754                              0.030400   \n",
       "1                     0.053477                             -0.007435   \n",
       "2                    -0.118559                              0.177899   \n",
       "3                    -0.036788                             -0.012892   \n",
       "4                     0.123320                              0.122542   \n",
       "5                     0.082632                             -0.143439   \n",
       "6                    -0.212754                             -0.230622   \n",
       "7                    -0.020888                              0.593996   \n",
       "8                     0.012954                              0.080936   \n",
       "9                    -0.020590                             -0.127730   \n",
       "\n",
       "   angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \\\n",
       "0                         -0.464761                             -0.018446   \n",
       "1                         -0.732626                              0.703511   \n",
       "2                          0.100699                              0.808529   \n",
       "3                          0.640011                             -0.485366   \n",
       "4                          0.693578                             -0.615971   \n",
       "5                          0.275041                             -0.368224   \n",
       "6                          0.014637                             -0.189512   \n",
       "7                         -0.561871                              0.467383   \n",
       "8                         -0.234313                              0.117797   \n",
       "9                         -0.482871                             -0.070670   \n",
       "\n",
       "   angle(X,gravityMean)  angle(Y,gravityMean)  angle(Z,gravityMean)  subject  \\\n",
       "0             -0.841247              0.179941             -0.058627        1   \n",
       "1             -0.844788              0.180289             -0.054317        1   \n",
       "2             -0.848933              0.180637             -0.049118        1   \n",
       "3             -0.848649              0.181935             -0.047663        1   \n",
       "4             -0.847865              0.185151             -0.043892        1   \n",
       "5             -0.849632              0.184823             -0.042126        1   \n",
       "6             -0.852150              0.182170             -0.043010        1   \n",
       "7             -0.851017              0.183779             -0.041976        1   \n",
       "8             -0.847971              0.188982             -0.037364        1   \n",
       "9             -0.848294              0.190310             -0.034417        1   \n",
       "\n",
       "   Activity  \n",
       "0  STANDING  \n",
       "1  STANDING  \n",
       "2  STANDING  \n",
       "3  STANDING  \n",
       "4  STANDING  \n",
       "5  STANDING  \n",
       "6  STANDING  \n",
       "7  STANDING  \n",
       "8  STANDING  \n",
       "9  STANDING  \n",
       "\n",
       "[10 rows x 563 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['STANDING', 'SITTING', 'LAYING', 'WALKING', 'WALKING_DOWNSTAIRS', 'WALKING_UPSTAIRS']\n"
     ]
    }
   ],
   "source": [
    "list_of_activity = df[\"Activity\"].unique().tolist() \n",
    "print(list_of_activity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7352, 562)\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(columns = \"Activity\")\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7352,)\n"
     ]
    }
   ],
   "source": [
    "y = df[\"Activity\"]\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test,y_train, y_test = train_test_split(X,y, test_size=0.2, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "SVM_classifer = svm.SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitted_SVM = SVM_classifer.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = fitted_SVM.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9517335146159075\n"
     ]
    }
   ],
   "source": [
    "prediction = accuracy_score(y_test, y_predicted)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_33181/987590380.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprecision\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprecision_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_predicted\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[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprecision\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mprecision_score\u001b[0;34m(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\u001b[0m\n\u001b[1;32m   1755\u001b[0m     \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.\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[1;32m   1756\u001b[0m     \"\"\"\n\u001b[0;32m-> 1757\u001b[0;31m     p, _, _, _ = precision_recall_fscore_support(\n\u001b[0m\u001b[1;32m   1758\u001b[0m         \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1759\u001b[0m         \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mprecision_recall_fscore_support\u001b[0;34m(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\u001b[0m\n\u001b[1;32m   1542\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mbeta\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1543\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"beta should be >=0 in the F-beta score\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1544\u001b[0;31m     \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_set_wise_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\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   1545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1546\u001b[0m     \u001b[0;31m# Calculate tp_sum, pred_sum, true_sum ###\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_set_wise_labels\u001b[0;34m(y_true, y_pred, average, labels, pos_label)\u001b[0m\n\u001b[1;32m   1363\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1364\u001b[0m                 \u001b[0maverage_options\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"samples\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1365\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m   1366\u001b[0m                 \u001b[0;34m\"Target is %s but average='binary'. Please \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1367\u001b[0m                 \u001b[0;34m\"choose another average setting, one of %r.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage_options\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: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted']."
     ]
    }
   ],
   "source": [
    "precision = precision_score(y_test, y_predicted, )\n",
    "print(precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average : micro, precision = 0.9517335146159075\n",
      "average : macro, precision = 0.9544659140673852\n",
      "average : weighted, precision = 0.9517333589121746\n"
     ]
    }
   ],
   "source": [
    "average = ['micro', 'macro', 'weighted']\n",
    "for i in range (3):\n",
    "    precision = precision_score(y_test, y_predicted, average=average[i])\n",
    "    print(f\"average : {average[i]}, precision = {precision}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[254,   0,   0,   0,   0,   0],\n",
       "       [  5, 224,  31,   0,   0,   0],\n",
       "       [  0,  21, 257,   0,   0,   0],\n",
       "       [  0,   0,   0, 243,   1,   1],\n",
       "       [  0,   0,   0,   3, 199,   6],\n",
       "       [  0,   0,   0,   2,   1, 223]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm =confusion_matrix(y_test, y_predicted)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "cmd_obj = ConfusionMatrixDisplay(cm, display_labels=['STANDING', 'SITTING', 'LAYING',\n",
    "                                                     'WALKING', 'WALKING_DOWNSTAIRS',\n",
    "                                                     'WALKING_UPSTAIRS'])\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (20,20)\n",
    "\n",
    "\n",
    "cmd_obj.plot()\n",
    "\n",
    "cmd_obj.ax_.set()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}