NaiveBayes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HumidityRatio</th>\n",
       "      <th>Occupancy</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>day_minute</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.7000</td>\n",
       "      <td>26.272</td>\n",
       "      <td>585.200000</td>\n",
       "      <td>749.200000</td>\n",
       "      <td>0.004764</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>859</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.7180</td>\n",
       "      <td>26.290</td>\n",
       "      <td>578.400000</td>\n",
       "      <td>760.400000</td>\n",
       "      <td>0.004773</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>860</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.7300</td>\n",
       "      <td>26.230</td>\n",
       "      <td>572.666667</td>\n",
       "      <td>769.666667</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>861</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.7225</td>\n",
       "      <td>26.125</td>\n",
       "      <td>493.750000</td>\n",
       "      <td>774.750000</td>\n",
       "      <td>0.004744</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.7540</td>\n",
       "      <td>26.200</td>\n",
       "      <td>488.600000</td>\n",
       "      <td>779.000000</td>\n",
       "      <td>0.004767</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>863</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature  Humidity       Light         CO2  HumidityRatio  Occupancy  \\\n",
       "0      23.7000    26.272  585.200000  749.200000       0.004764          1   \n",
       "1      23.7180    26.290  578.400000  760.400000       0.004773          1   \n",
       "2      23.7300    26.230  572.666667  769.666667       0.004765          1   \n",
       "3      23.7225    26.125  493.750000  774.750000       0.004744          1   \n",
       "4      23.7540    26.200  488.600000  779.000000       0.004767          1   \n",
       "\n",
       "   hour  minute  day_minute  weekend  \n",
       "0    14      19         859        0  \n",
       "1    14      20         860        0  \n",
       "2    14      21         861        0  \n",
       "3    14      22         862        0  \n",
       "4    14      23         863        0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import cross_val_score \n",
    "\n",
    "from sklearn.metrics import accuracy_score, f1_score, classification_report\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB, CategoricalNB\n",
    "\n",
    "class_name = 'Occupancy'\n",
    "df_training = pd.read_csv('training.csv', skipinitialspace=True, na_values='?', \n",
    "                 keep_default_na=True)\n",
    "\n",
    "df_test = pd.read_csv('test.csv', skipinitialspace=True, na_values='?', \n",
    "                 keep_default_na=True)\n",
    "\n",
    "columns2remove = ['date', 'Unnamed: 0', 'cumulative_hour', 'cumulative_minute', 'day']\n",
    "df_training.drop(columns2remove, inplace=True, axis=1)\n",
    "df_test.drop(columns2remove, inplace=True, axis=1)\n",
    "\n",
    "df_training.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributes = [col for col in df_training.columns if col != class_name]\n",
    "\n",
    "#X_train = scaler.fit_transform(df_training[attributes].values) # .values per convertire il df_training da pandas dataframe in numpy array\n",
    "X_train = df_training[attributes].values\n",
    "y_train = df_training[class_name]\n",
    "\n",
    "#X_test = scaler.fit_transform(df_test[attributes].values) \n",
    "X_test = df_test[attributes].values # senza normalizzazione\n",
    "y_test = df_test[class_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.8905642023346303\n",
      "F1-score [0.92869969 0.76472639]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.87      0.93      5071\n",
      "           1       0.62      1.00      0.76      1097\n",
      "\n",
      "    accuracy                           0.89      6168\n",
      "   macro avg       0.81      0.93      0.85      6168\n",
      "weighted avg       0.93      0.89      0.90      6168\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nprecision = TP / ( TP + FP ) TP + FP tutti i record con valore predetto positivo (=0 in questo caso)\\nprecision[0] = 1 vuol dire che non ci sono FP rispetto a 0 (non ci sono 1 classificati come 0)\\nrecall = TP / ( TP + FN ) TP + FN tutti i record con valore reale positivo (=0 in questo caso)\\nrecall[0] = 0.87 vuol dire che ci sono FN rispetto a 0 (ci sono 0 che classifico 1)\\nTP = 87%,    FN = 13%\\n\\nrecall = TP / ( TP + FN )\\nrecall[1] = 1 vuol dire che non ci sono FN rispetto a 1 (non ci sono 1 classificati come 0)\\n\\n\\n\\npositivo = 0\\nclassifico 0, reale 0 ---> TP\\nclassifico 1, reale 1 ---> TN\\nclassifico 0, reale 1 ---> FP\\nclassifico 1, reale 0 ---> FN\\n\\npositivo = 1\\n\\n'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Per prima cosa proviamo a creare un modello utilizzando tutte le variabili.\n",
    "# sotto poi proveremo alcune combinazioni e vedremo che hanno delle performance migliori.\n",
    "clf = GaussianNB()\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
    "print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
    "print(classification_report(y_test, y_pred))\n",
    "\"\"\"\n",
    "precision = TP / ( TP + FP ) TP + FP tutti i record con valore predetto positivo (=0 in questo caso)\n",
    "precision[0] = 1 vuol dire che non ci sono FP rispetto a 0 (non ci sono 1 classificati come 0)\n",
    "recall = TP / ( TP + FN ) TP + FN tutti i record con valore reale positivo (=0 in questo caso)\n",
    "recall[0] = 0.87 vuol dire che ci sono FN rispetto a 0 (ci sono 0 che classifico 1)\n",
    "TP = 87%,    FN = 13%\n",
    "\n",
    "recall = TP / ( TP + FN )\n",
    "recall[1] = 1 vuol dire che non ci sono FN rispetto a 1 (non ci sono 1 classificati come 0)\n",
    "\n",
    "\n",
    "\n",
    "positivo = 0\n",
    "classifico 0, reale 0 ---> TP\n",
    "classifico 1, reale 1 ---> TN\n",
    "classifico 0, reale 1 ---> FP\n",
    "classifico 1, reale 0 ---> FN\n",
    "\n",
    "positivo = 1\n",
    "\n",
    "\"\"\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Light</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HumidityRatio</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>day_minute</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.7000</td>\n",
       "      <td>26.2720</td>\n",
       "      <td>585.200000</td>\n",
       "      <td>749.200000</td>\n",
       "      <td>0.004764</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>859</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.7180</td>\n",
       "      <td>26.2900</td>\n",
       "      <td>578.400000</td>\n",
       "      <td>760.400000</td>\n",
       "      <td>0.004773</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>860</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.7300</td>\n",
       "      <td>26.2300</td>\n",
       "      <td>572.666667</td>\n",
       "      <td>769.666667</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>861</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.7225</td>\n",
       "      <td>26.1250</td>\n",
       "      <td>493.750000</td>\n",
       "      <td>774.750000</td>\n",
       "      <td>0.004744</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.7540</td>\n",
       "      <td>26.2000</td>\n",
       "      <td>488.600000</td>\n",
       "      <td>779.000000</td>\n",
       "      <td>0.004767</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>863</td>\n",
       "      <td>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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14387</th>\n",
       "      <td>19.7900</td>\n",
       "      <td>36.2000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>0.005172</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>147</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14388</th>\n",
       "      <td>19.7900</td>\n",
       "      <td>36.2000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>0.005172</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>148</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14389</th>\n",
       "      <td>19.7900</td>\n",
       "      <td>36.2675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>506.333333</td>\n",
       "      <td>0.005182</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14390</th>\n",
       "      <td>19.7900</td>\n",
       "      <td>36.2900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>510.333333</td>\n",
       "      <td>0.005185</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14391</th>\n",
       "      <td>19.7900</td>\n",
       "      <td>36.2900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>0.005185</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14392 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Temperature  Humidity       Light         CO2  HumidityRatio  hour  \\\n",
       "0          23.7000   26.2720  585.200000  749.200000       0.004764    14   \n",
       "1          23.7180   26.2900  578.400000  760.400000       0.004773    14   \n",
       "2          23.7300   26.2300  572.666667  769.666667       0.004765    14   \n",
       "3          23.7225   26.1250  493.750000  774.750000       0.004744    14   \n",
       "4          23.7540   26.2000  488.600000  779.000000       0.004767    14   \n",
       "...            ...       ...         ...         ...            ...   ...   \n",
       "14387      19.7900   36.2000    0.000000  501.000000       0.005172     2   \n",
       "14388      19.7900   36.2000    0.000000  502.000000       0.005172     2   \n",
       "14389      19.7900   36.2675    0.000000  506.333333       0.005182     2   \n",
       "14390      19.7900   36.2900    0.000000  510.333333       0.005185     2   \n",
       "14391      19.7900   36.2900    0.000000  510.000000       0.005185     2   \n",
       "\n",
       "       minute  day_minute  weekend  \n",
       "0          19         859        0  \n",
       "1          20         860        0  \n",
       "2          21         861        0  \n",
       "3          22         862        0  \n",
       "4          23         863        0  \n",
       "...       ...         ...      ...  \n",
       "14387      27         147        1  \n",
       "14388      28         148        1  \n",
       "14389      29         149        1  \n",
       "14390      30         150        1  \n",
       "14391      31         151        1  \n",
       "\n",
       "[14392 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_training[attributes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attributes to test in iteration 1: ('Temperature', 'Light')\n",
      "Best performance with cv = 48, f1-score = 0.955658464426352\n",
      "Best performance with cv = 48, accuracy = 0.9583472686733555\n",
      "\n",
      "Accuracy 0.9785992217898832\n",
      "F1-score [0.98681582 0.94320138]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99      5071\n",
      "           1       0.89      1.00      0.94      1097\n",
      "\n",
      "    accuracy                           0.98      6168\n",
      "   macro avg       0.95      0.99      0.97      6168\n",
      "weighted avg       0.98      0.98      0.98      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 2: ('Temperature', 'CO2')\n",
      "Best performance with cv = 14, f1-score = 0.8291232492383797\n",
      "Best performance with cv = 14, accuracy = 0.8729155086158976\n",
      "\n",
      "Accuracy 0.8218223086900129\n",
      "F1-score [0.87942951 0.65880161]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.79      0.88      5071\n",
      "           1       0.50      0.97      0.66      1097\n",
      "\n",
      "    accuracy                           0.82      6168\n",
      "   macro avg       0.75      0.88      0.77      6168\n",
      "weighted avg       0.90      0.82      0.84      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 3: ('Temperature', 'Humidity')\n",
      "Best performance with cv = 3, f1-score = 0.746395927553411\n",
      "Best performance with cv = 3, accuracy = 0.8362965610395942\n",
      "\n",
      "Accuracy 0.830739299610895\n",
      "F1-score [0.90053354 0.4326087 ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.93      0.90      5071\n",
      "           1       0.54      0.36      0.43      1097\n",
      "\n",
      "    accuracy                           0.83      6168\n",
      "   macro avg       0.70      0.65      0.67      6168\n",
      "weighted avg       0.81      0.83      0.82      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 4: ('Temperature', 'HumidityRatio')\n",
      "Best performance with cv = 6, f1-score = 0.7657844704930992\n",
      "Best performance with cv = 6, accuracy = 0.8376190999330992\n",
      "\n",
      "Accuracy 0.8344682230869002\n",
      "F1-score [0.8985392 0.5508139]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.89      0.90      5071\n",
      "           1       0.53      0.57      0.55      1097\n",
      "\n",
      "    accuracy                           0.83      6168\n",
      "   macro avg       0.72      0.73      0.72      6168\n",
      "weighted avg       0.84      0.83      0.84      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 5: ('Light', 'CO2')\n",
      "Best performance with cv = 47, f1-score = 0.9592327271864137\n",
      "Best performance with cv = 47, accuracy = 0.9621898890080207\n",
      "\n",
      "Accuracy 0.8636511024643321\n",
      "F1-score [0.90959905 0.72271678]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.83      0.91      5071\n",
      "           1       0.57      1.00      0.72      1097\n",
      "\n",
      "    accuracy                           0.86      6168\n",
      "   macro avg       0.78      0.92      0.82      6168\n",
      "weighted avg       0.92      0.86      0.88      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 6: ('Light', 'Humidity')\n",
      "Best performance with cv = 3, f1-score = 0.9654498953467483\n",
      "Best performance with cv = 3, accuracy = 0.972832239732066\n",
      "\n",
      "Accuracy 0.980544747081712\n",
      "F1-score [0.98802873 0.94809689]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.90      1.00      0.95      1097\n",
      "\n",
      "    accuracy                           0.98      6168\n",
      "   macro avg       0.95      0.99      0.97      6168\n",
      "weighted avg       0.98      0.98      0.98      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 7: ('Light', 'HumidityRatio')\n",
      "Best performance with cv = 41, f1-score = 0.9635431324157956\n",
      "Best performance with cv = 41, accuracy = 0.9670672879514343\n",
      "\n",
      "Accuracy 0.9810311284046692\n",
      "F1-score [0.9883315  0.94932871]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.90      1.00      0.95      1097\n",
      "\n",
      "    accuracy                           0.98      6168\n",
      "   macro avg       0.95      0.99      0.97      6168\n",
      "weighted avg       0.98      0.98      0.98      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 8: ('CO2', 'Humidity')\n",
      "Best performance with cv = 4, f1-score = 0.7694955217285517\n",
      "Best performance with cv = 4, accuracy = 0.8579071706503613\n",
      "\n",
      "Accuracy 0.7900453955901426\n",
      "F1-score [0.85365578 0.62862059]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.74      0.85      5071\n",
      "           1       0.46      1.00      0.63      1097\n",
      "\n",
      "    accuracy                           0.79      6168\n",
      "   macro avg       0.73      0.87      0.74      6168\n",
      "weighted avg       0.90      0.79      0.81      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 9: ('CO2', 'HumidityRatio')\n",
      "Best performance with cv = 6, f1-score = 0.7795412914656205\n",
      "Best performance with cv = 6, accuracy = 0.8633856742042111\n",
      "\n",
      "Accuracy 0.7869649805447471\n",
      "F1-score [0.85125651 0.62478584]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.74      0.85      5071\n",
      "           1       0.45      1.00      0.62      1097\n",
      "\n",
      "    accuracy                           0.79      6168\n",
      "   macro avg       0.73      0.87      0.74      6168\n",
      "weighted avg       0.90      0.79      0.81      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 10: ('Humidity', 'HumidityRatio')\n",
      "Best performance with cv = 5, f1-score = 0.5945060393574765\n",
      "Best performance with cv = 5, accuracy = 0.772859128707776\n",
      "\n",
      "Accuracy 0.5168612191958496\n",
      "F1-score [0.63808598 0.27352511]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.52      0.64      5071\n",
      "           1       0.19      0.51      0.27      1097\n",
      "\n",
      "    accuracy                           0.52      6168\n",
      "   macro avg       0.51      0.51      0.46      6168\n",
      "weighted avg       0.72      0.52      0.57      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 11: ('Temperature', 'Light', 'CO2')\n",
      "Best performance with cv = 46, f1-score = 0.9488136458879007\n",
      "Best performance with cv = 46, accuracy = 0.9526211795797849\n",
      "\n",
      "Accuracy 0.8630025940337225\n",
      "F1-score [0.90913001 0.7217649 ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.83      0.91      5071\n",
      "           1       0.56      1.00      0.72      1097\n",
      "\n",
      "    accuracy                           0.86      6168\n",
      "   macro avg       0.78      0.92      0.82      6168\n",
      "weighted avg       0.92      0.86      0.88      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 12: ('Temperature', 'Light', 'Humidity')\n",
      "Best performance with cv = 46, f1-score = 0.9564469628627199\n",
      "Best performance with cv = 46, accuracy = 0.9597736152813247\n",
      "\n",
      "Accuracy 0.9777885862516212\n",
      "F1-score [0.98630958 0.94117647]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99      5071\n",
      "           1       0.89      1.00      0.94      1097\n",
      "\n",
      "    accuracy                           0.98      6168\n",
      "   macro avg       0.94      0.99      0.96      6168\n",
      "weighted avg       0.98      0.98      0.98      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 13: ('Temperature', 'Light', 'HumidityRatio')\n",
      "Best performance with cv = 48, f1-score = 0.955658464426352\n",
      "Best performance with cv = 48, accuracy = 0.9583472686733555\n",
      "\n",
      "Accuracy 0.9785992217898832\n",
      "F1-score [0.98681582 0.94320138]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99      5071\n",
      "           1       0.89      1.00      0.94      1097\n",
      "\n",
      "    accuracy                           0.98      6168\n",
      "   macro avg       0.95      0.99      0.97      6168\n",
      "weighted avg       0.98      0.98      0.98      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 14: ('Temperature', 'CO2', 'Humidity')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best performance with cv = 6, f1-score = 0.8202797037795125\n",
      "Best performance with cv = 6, accuracy = 0.8651249136218025\n",
      "\n",
      "Accuracy 0.8114461738002594\n",
      "F1-score [0.87139224 0.6468266 ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.78      0.87      5071\n",
      "           1       0.48      0.97      0.65      1097\n",
      "\n",
      "    accuracy                           0.81      6168\n",
      "   macro avg       0.74      0.87      0.76      6168\n",
      "weighted avg       0.90      0.81      0.83      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 15: ('Temperature', 'CO2', 'HumidityRatio')\n",
      "Best performance with cv = 14, f1-score = 0.8289918657502184\n",
      "Best performance with cv = 14, accuracy = 0.8728460255697609\n",
      "\n",
      "Accuracy 0.8216601815823605\n",
      "F1-score [0.87930656 0.65859714]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.79      0.88      5071\n",
      "           1       0.50      0.97      0.66      1097\n",
      "\n",
      "    accuracy                           0.82      6168\n",
      "   macro avg       0.75      0.88      0.77      6168\n",
      "weighted avg       0.90      0.82      0.84      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 16: ('Temperature', 'Humidity', 'HumidityRatio')\n",
      "Best performance with cv = 6, f1-score = 0.7740515297367616\n",
      "Best performance with cv = 6, accuracy = 0.839770312507424\n",
      "\n",
      "Accuracy 0.8142023346303502\n",
      "F1-score [0.88325183 0.5452381 ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.85      0.88      5071\n",
      "           1       0.48      0.63      0.55      1097\n",
      "\n",
      "    accuracy                           0.81      6168\n",
      "   macro avg       0.70      0.74      0.71      6168\n",
      "weighted avg       0.84      0.81      0.82      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 17: ('Light', 'CO2', 'Humidity')\n",
      "Best performance with cv = 46, f1-score = 0.9597424607065644\n",
      "Best performance with cv = 46, accuracy = 0.9633240104572928\n",
      "\n",
      "Accuracy 0.8625162127107653\n",
      "F1-score [0.90877797 0.72105263]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.83      0.91      5071\n",
      "           1       0.56      1.00      0.72      1097\n",
      "\n",
      "    accuracy                           0.86      6168\n",
      "   macro avg       0.78      0.92      0.81      6168\n",
      "weighted avg       0.92      0.86      0.88      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 18: ('Light', 'CO2', 'HumidityRatio')\n",
      "Best performance with cv = 47, f1-score = 0.9592327271864137\n",
      "Best performance with cv = 47, accuracy = 0.9621898890080207\n",
      "\n",
      "Accuracy 0.8636511024643321\n",
      "F1-score [0.90959905 0.72271678]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.83      0.91      5071\n",
      "           1       0.57      1.00      0.72      1097\n",
      "\n",
      "    accuracy                           0.86      6168\n",
      "   macro avg       0.78      0.92      0.82      6168\n",
      "weighted avg       0.92      0.86      0.88      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 19: ('Light', 'Humidity', 'HumidityRatio')\n",
      "Best performance with cv = 3, f1-score = 0.9654498953467483\n",
      "Best performance with cv = 3, accuracy = 0.972832239732066\n",
      "\n",
      "Accuracy 0.980544747081712\n",
      "F1-score [0.98802873 0.94809689]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99      5071\n",
      "           1       0.90      1.00      0.95      1097\n",
      "\n",
      "    accuracy                           0.98      6168\n",
      "   macro avg       0.95      0.99      0.97      6168\n",
      "weighted avg       0.98      0.98      0.98      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 20: ('CO2', 'Humidity', 'HumidityRatio')\n",
      "Best performance with cv = 4, f1-score = 0.7695762320429651\n",
      "Best performance with cv = 4, accuracy = 0.8579071706503613\n",
      "\n",
      "Accuracy 0.790207522697795\n",
      "F1-score [0.85378531 0.62880092]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.75      0.85      5071\n",
      "           1       0.46      1.00      0.63      1097\n",
      "\n",
      "    accuracy                           0.79      6168\n",
      "   macro avg       0.73      0.87      0.74      6168\n",
      "weighted avg       0.90      0.79      0.81      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 21: ('Temperature', 'Light', 'CO2', 'Humidity')\n",
      "Best performance with cv = 46, f1-score = 0.9490941040658377\n",
      "Best performance with cv = 46, accuracy = 0.9528295418523226\n",
      "\n",
      "Accuracy 0.8620298313878081\n",
      "F1-score [0.9084257  0.72034177]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.83      0.91      5071\n",
      "           1       0.56      1.00      0.72      1097\n",
      "\n",
      "    accuracy                           0.86      6168\n",
      "   macro avg       0.78      0.92      0.81      6168\n",
      "weighted avg       0.92      0.86      0.87      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 22: ('Temperature', 'Light', 'CO2', 'HumidityRatio')\n",
      "Best performance with cv = 46, f1-score = 0.9488136458879007\n",
      "Best performance with cv = 46, accuracy = 0.9526211795797849\n",
      "\n",
      "Accuracy 0.8630025940337225\n",
      "F1-score [0.90913001 0.7217649 ]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.83      0.91      5071\n",
      "           1       0.56      1.00      0.72      1097\n",
      "\n",
      "    accuracy                           0.86      6168\n",
      "   macro avg       0.78      0.92      0.82      6168\n",
      "weighted avg       0.92      0.86      0.88      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 23: ('Temperature', 'Light', 'Humidity', 'HumidityRatio')\n",
      "Best performance with cv = 46, f1-score = 0.9564469628627199\n",
      "Best performance with cv = 46, accuracy = 0.9597736152813247\n",
      "\n",
      "Accuracy 0.9777885862516212\n",
      "F1-score [0.98630958 0.94117647]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99      5071\n",
      "           1       0.89      1.00      0.94      1097\n",
      "\n",
      "    accuracy                           0.98      6168\n",
      "   macro avg       0.94      0.99      0.96      6168\n",
      "weighted avg       0.98      0.98      0.98      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 24: ('Temperature', 'CO2', 'Humidity', 'HumidityRatio')\n",
      "Best performance with cv = 6, f1-score = 0.8201069795648217\n",
      "Best performance with cv = 6, accuracy = 0.8650554112587224\n",
      "\n",
      "Accuracy 0.811284046692607\n",
      "F1-score [0.87126742 0.64663024]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.78      0.87      5071\n",
      "           1       0.48      0.97      0.65      1097\n",
      "\n",
      "    accuracy                           0.81      6168\n",
      "   macro avg       0.74      0.87      0.76      6168\n",
      "weighted avg       0.90      0.81      0.83      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 25: ('Light', 'CO2', 'Humidity', 'HumidityRatio')\n",
      "Best performance with cv = 46, f1-score = 0.9597424607065644\n",
      "Best performance with cv = 46, accuracy = 0.9633240104572928\n",
      "\n",
      "Accuracy 0.8625162127107653\n",
      "F1-score [0.90877797 0.72105263]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.83      0.91      5071\n",
      "           1       0.56      1.00      0.72      1097\n",
      "\n",
      "    accuracy                           0.86      6168\n",
      "   macro avg       0.78      0.92      0.81      6168\n",
      "weighted avg       0.92      0.86      0.88      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n",
      "Attributes to test in iteration 26: ('Temperature', 'Light', 'CO2', 'Humidity', 'HumidityRatio')\n",
      "Best performance with cv = 46, f1-score = 0.9490941040658377\n",
      "Best performance with cv = 46, accuracy = 0.9528295418523226\n",
      "\n",
      "Accuracy 0.8620298313878081\n",
      "F1-score [0.9084257  0.72034177]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.83      0.91      5071\n",
      "           1       0.56      1.00      0.72      1097\n",
      "\n",
      "    accuracy                           0.86      6168\n",
      "   macro avg       0.78      0.92      0.81      6168\n",
      "weighted avg       0.92      0.86      0.87      6168\n",
      "\n",
      "---------------------------------------------------------\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from itertools import combinations\n",
    "import operator\n",
    "\n",
    "tested_attributes = ['Temperature', 'Light', 'CO2', 'Humidity', 'HumidityRatio']\n",
    "\n",
    "summary = []\n",
    "i=1\n",
    "\n",
    "y_train = df_training[class_name]\n",
    "y_test = df_test[class_name]\n",
    "\n",
    "for r in range(2, 6):\n",
    "    for attr in combinations(tested_attributes, r):\n",
    "        # 'attr' è la combinazione di attributi da testare per ogni iterazione\n",
    "        print(\"Attributes to test in iteration {}: {}\".format(i, attr))\n",
    "        X_train = df_training[list(attr)].values\n",
    "        X_test = df_test[list(attr)].values\n",
    "\n",
    "        clf = GaussianNB()\n",
    "        clf.fit(X_train, y_train)\n",
    "        \n",
    "        #Cross validation per trovare il numero migliore di cv      \n",
    "        cv_performances_f1 = {}\n",
    "        cv_performances_acc = {}\n",
    "        for k in range(3, 50):\n",
    "            scores_acc = cross_val_score(clf, X_train, y_train, cv=k, n_jobs = -1)\n",
    "            scores_f1 = cross_val_score(clf, X_train, y_train, cv=k, scoring='f1_macro', n_jobs = -1)\n",
    "            cv_performances_f1[k] = scores_f1.mean()\n",
    "            cv_performances_acc[k] = scores_acc.mean()\n",
    "            \n",
    "        best_performances_f1 = max(cv_performances_f1.items(), key=operator.itemgetter(1))[0]\n",
    "        print(\"Best performance with cv = {}, f1-score = {}\".format(best_performances_f1, cv_performances_f1[best_performances_f1]))\n",
    "        print(\"Best performance with cv = {}, accuracy = {}\".format(best_performances_f1, cv_performances_acc[best_performances_f1]))\n",
    "        \n",
    "        #classification on test set using best classifier for this iteration\n",
    "        y_pred = clf.predict(X_test)\n",
    "        \n",
    "        print()\n",
    "        print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
    "        print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
    "        print(classification_report(y_test, y_pred))\n",
    "        report = classification_report(y_test, y_pred, output_dict=True)\n",
    "        \n",
    "        summary.append({'iteration' : i,\n",
    "                    'attributes': attr,\n",
    "                    'classificator': clf,\n",
    "                    'cv': best_performances_f1,\n",
    "                    'cv_accuracy' : cv_performances_acc[best_performances_f1],\n",
    "                    'cv_f1': cv_performances_f1[best_performances_f1],\n",
    "                    'test_accuracy' : accuracy_score(y_test, y_pred),\n",
    "                    'f1-score [0]' : f1_score(y_test, y_pred, average=None)[0],\n",
    "                    'f1-score [1]' : f1_score(y_test, y_pred, average=None)[1],\n",
    "                    'precision [1]' : report['1']['precision'],\n",
    "                    'recall [1]' : report['1']['recall']})\n",
    "        print(\"---------------------------------------------------------\\n\\n\\n\")\n",
    "        \n",
    "        i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'iteration': 7,\n",
       " 'attributes': ('Light', 'HumidityRatio'),\n",
       " 'classificator': GaussianNB(priors=None, var_smoothing=1e-09),\n",
       " 'cv': 41,\n",
       " 'cv_accuracy': 0.9670672879514343,\n",
       " 'cv_f1': 0.9635431324157956,\n",
       " 'test_accuracy': 0.9810311284046692,\n",
       " 'f1-score [0]': 0.988331504936671,\n",
       " 'f1-score [1]': 0.9493287137288869,\n",
       " 'precision [1]': 0.9042904290429042,\n",
       " 'recall [1]': 0.9990884229717412}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ordiniamo i risultati in ordine decrescente per precision\n",
    "precisions = []\n",
    "\n",
    "for i in range(len(summary)):\n",
    "    precisions.append(float(summary[i]['precision [1]']))\n",
    "\n",
    "#precision e summary hanno gli stessi indici\n",
    "    \n",
    "best_idx = precisions.index(max(precisions)) #index of the best classificator according to precision\n",
    "summary[best_idx] #best classificator description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attributes          :       precision[1]\n",
      "\n",
      "('Humidity', 'HumidityRatio') : 0.18668885191347753\n",
      "('CO2', 'HumidityRatio') : 0.4548856548856549\n",
      "('CO2', 'Humidity') : 0.4585774058577406\n",
      "('CO2', 'Humidity', 'HumidityRatio') : 0.4587693595646714\n",
      "('Temperature', 'Humidity', 'HumidityRatio') : 0.48278285312719604\n",
      "('Temperature', 'CO2', 'Humidity', 'HumidityRatio') : 0.48475193445607645\n",
      "('Temperature', 'CO2', 'Humidity') : 0.4849726775956284\n",
      "('Temperature', 'CO2', 'HumidityRatio') : 0.49929411764705883\n",
      "('Temperature', 'CO2') : 0.4995291902071563\n",
      "('Temperature', 'HumidityRatio') : 0.532312925170068\n",
      "('Temperature', 'Humidity') : 0.5356662180349933\n",
      "('Temperature', 'Light', 'CO2', 'Humidity') : 0.5632065775950668\n",
      "('Temperature', 'Light', 'CO2', 'Humidity', 'HumidityRatio') : 0.5632065775950668\n",
      "('Light', 'CO2', 'Humidity') : 0.564076170869789\n",
      "('Light', 'CO2', 'Humidity', 'HumidityRatio') : 0.564076170869789\n",
      "('Temperature', 'Light', 'CO2') : 0.5649484536082474\n",
      "('Temperature', 'Light', 'CO2', 'HumidityRatio') : 0.5649484536082474\n",
      "('Light', 'CO2') : 0.5661157024793388\n",
      "('Light', 'CO2', 'HumidityRatio') : 0.5661157024793388\n",
      "\n",
      "------------------ precision[1] > 88% ----------------------------------\n",
      "\n",
      "('Temperature', 'Light', 'Humidity') : 0.8896103896103896\n",
      "('Temperature', 'Light', 'Humidity', 'HumidityRatio') : 0.8896103896103896\n",
      "('Temperature', 'Light') : 0.8932355338223309\n",
      "('Temperature', 'Light', 'HumidityRatio') : 0.8932355338223309\n",
      "('Light', 'Humidity') : 0.9020576131687242\n",
      "('Light', 'Humidity', 'HumidityRatio') : 0.9020576131687242\n",
      "('Light', 'HumidityRatio') : 0.9042904290429042\n"
     ]
    }
   ],
   "source": [
    "#Combinazioni degli attributi ordinate per precision[1], per selezionare quali modelli aggiungere alla tabella del report\n",
    "from collections import OrderedDict\n",
    "\n",
    "d = {}\n",
    "i = 0\n",
    "for r in range(2, 6):\n",
    "    for attr in combinations(tested_attributes, r):\n",
    "        d[attr] = precisions[i]\n",
    "        i += 1\n",
    "\n",
    "sortedDict = OrderedDict(sorted(d.items(), key=lambda x: x[1]))\n",
    "print('Attributes          :       precision[1]\\n')    \n",
    "for key in sortedDict:\n",
    "    print(key, ':', sortedDict[key])\n",
    "    if(key == ('Light', 'CO2', 'HumidityRatio')):\n",
    "        print('\\n------------------ precision[1] > 88% ----------------------------------\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attributes          :       recall[1]\n",
      "\n",
      "('Temperature', 'Humidity') : 0.3628076572470374\n",
      "('Humidity', 'HumidityRatio') : 0.5113947128532361\n",
      "('Temperature', 'HumidityRatio') : 0.5706472196900638\n",
      "('Temperature', 'Humidity', 'HumidityRatio') : 0.626253418413856\n",
      "\n",
      "------------------ recall[1] > 96% ------------------------------\n",
      "\n",
      "('Temperature', 'CO2') : 0.96718322698268\n",
      "('Temperature', 'CO2', 'HumidityRatio') : 0.96718322698268\n",
      "('Temperature', 'CO2', 'Humidity') : 0.9708295350957156\n",
      "('Temperature', 'CO2', 'Humidity', 'HumidityRatio') : 0.9708295350957156\n",
      "('CO2', 'HumidityRatio') : 0.9972652689152234\n",
      "\n",
      "--------------------------------------------------------------\n",
      "\n",
      "('Temperature', 'Light') : 0.9990884229717412\n",
      "('Light', 'CO2') : 0.9990884229717412\n",
      "('Light', 'Humidity') : 0.9990884229717412\n",
      "('Light', 'HumidityRatio') : 0.9990884229717412\n",
      "('CO2', 'Humidity') : 0.9990884229717412\n",
      "('Temperature', 'Light', 'CO2') : 0.9990884229717412\n",
      "('Temperature', 'Light', 'Humidity') : 0.9990884229717412\n",
      "('Temperature', 'Light', 'HumidityRatio') : 0.9990884229717412\n",
      "('Light', 'CO2', 'Humidity') : 0.9990884229717412\n",
      "('Light', 'CO2', 'HumidityRatio') : 0.9990884229717412\n",
      "('Light', 'Humidity', 'HumidityRatio') : 0.9990884229717412\n",
      "('CO2', 'Humidity', 'HumidityRatio') : 0.9990884229717412\n",
      "('Temperature', 'Light', 'CO2', 'Humidity') : 0.9990884229717412\n",
      "('Temperature', 'Light', 'CO2', 'HumidityRatio') : 0.9990884229717412\n",
      "('Temperature', 'Light', 'Humidity', 'HumidityRatio') : 0.9990884229717412\n",
      "('Light', 'CO2', 'Humidity', 'HumidityRatio') : 0.9990884229717412\n",
      "('Temperature', 'Light', 'CO2', 'Humidity', 'HumidityRatio') : 0.9990884229717412\n"
     ]
    }
   ],
   "source": [
    "#Combinazioni degli attributi ordinate per recall[1], per selezionare quali modelli aggiungere alla tabella del report\n",
    "recalls = []\n",
    "\n",
    "for i in range(len(summary)):\n",
    "    recalls.append(float(summary[i]['recall [1]']))\n",
    "d2 = {}\n",
    "i = 0\n",
    "for r in range(2, 6):\n",
    "    for attr in combinations(tested_attributes, r):\n",
    "        d2[attr] = recalls[i]\n",
    "        i += 1\n",
    "\n",
    "sortedDict = OrderedDict(sorted(d2.items(), key=lambda x: x[1]))\n",
    "print('Attributes          :       recall[1]\\n')          \n",
    "for key in sortedDict:\n",
    "    print(key, ':', sortedDict[key])\n",
    "    if(key == ('Temperature', 'Humidity', 'HumidityRatio')):\n",
    "        print('\\n------------------ recall[1] > 96% ------------------------------\\n')\n",
    "    if(key == ('CO2', 'HumidityRatio')):\n",
    "        print('\\n--------------------------------------------------------------\\n')"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}