Open_Food_Facts_SISection6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e18d139a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import joblib\n",
    "import matplotlib\n",
    "import operator\n",
    "from zipfile import ZipFile\n",
    "# Set \n",
    "matplotlib.rcParams['font.serif'] = 'Times New Roman'\n",
    "matplotlib.rcParams['font.family'] = \"serif\"\n",
    "matplotlib.rcParams['font.size'] = 10\n",
    "\n",
    "# import custom functions\n",
    "from functions_statistical_performance import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e9b9cb68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# True to exactly reproduce the results presented in the paper (same classifiers)\n",
    "reproduceflag=True\n",
    "pathdir1='./input_data/'\n",
    "pathdir2='./paper_classifiers_outcomes/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "267994f6",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4d40a822",
   "metadata": {},
   "outputs": [],
   "source": [
    "RFOFF=pd.read_csv(pathdir1+'OpenFoodFacts_10nutrients.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "58610a77",
   "metadata": {
    "scrolled": true
   },
   "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>_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>brands</th>\n",
       "      <th>nova_group</th>\n",
       "      <th>scans_n</th>\n",
       "      <th>additives_n</th>\n",
       "      <th>additives_original_tags</th>\n",
       "      <th>categories</th>\n",
       "      <th>len_countries</th>\n",
       "      <th>USA</th>\n",
       "      <th>...</th>\n",
       "      <th>sugars_100g</th>\n",
       "      <th>fiber_100g</th>\n",
       "      <th>calcium_100g</th>\n",
       "      <th>iron_100g</th>\n",
       "      <th>sodium_100g</th>\n",
       "      <th>vitamin-c_100g</th>\n",
       "      <th>cholesterol_100g</th>\n",
       "      <th>saturated-fat_100g</th>\n",
       "      <th>vitamin-a_100g</th>\n",
       "      <th>trans-fat_100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.530000e+03</td>\n",
       "      <td>Banana Chips Sweetened (Whole)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.29</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00129</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>28.57</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.364000e+04</td>\n",
       "      <td>Granola Cranberry And Acai</td>\n",
       "      <td>Viva La Salud</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.45</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.03600</td>\n",
       "      <td>0.00262</td>\n",
       "      <td>0.101600</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.368800e+04</td>\n",
       "      <td>Peanuts, Mixed Nuts</td>\n",
       "      <td>Northgate Market</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>['en:e415']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.29</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.07100</td>\n",
       "      <td>0.00514</td>\n",
       "      <td>0.217424</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>7.14</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.626900e+04</td>\n",
       "      <td>Milk Chocolate Peanut Butter Malt Balls</td>\n",
       "      <td>Sunridge</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>['en:e322i', 'en:e500ii', 'en:e471']</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>57.50</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.00090</td>\n",
       "      <td>0.038608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>20.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.359500e+04</td>\n",
       "      <td>Cranberries</td>\n",
       "      <td>Torn &amp; Glasser</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>Plant-based foods and beverages, Plant-based f...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>66.67</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233826</th>\n",
       "      <td>6.811310e+11</td>\n",
       "      <td>Triple Chocolate Brownie</td>\n",
       "      <td>Marketside</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>['en:e322i', 'en:e471', 'en:e211', 'en:e330', ...</td>\n",
       "      <td>Desserts,Biscuits and cakes,Cakes,Chocolate ca...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>39.20</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.05410</td>\n",
       "      <td>0.00135</td>\n",
       "      <td>0.203000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0676</td>\n",
       "      <td>12.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233827</th>\n",
       "      <td>7.874222e+10</td>\n",
       "      <td>Chicken egg roll</td>\n",
       "      <td>Walmart</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>['en:e330', 'en:e412', 'en:e322i']</td>\n",
       "      <td>Egg Roll</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03438</td>\n",
       "      <td>0.00110</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>1.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233828</th>\n",
       "      <td>6.038308e+10</td>\n",
       "      <td>Crisp &amp; Crunchy Spicy Southern-Style Chicken W...</td>\n",
       "      <td>President's Choice</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>['en:e322i', 'en:e433', 'en:e415', 'en:e412', ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01830</td>\n",
       "      <td>0.00132</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0688</td>\n",
       "      <td>4.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233829</th>\n",
       "      <td>7.709150e+12</td>\n",
       "      <td>Yogurt Fresa</td>\n",
       "      <td>Prílac</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>['en:e14xx', 'en:e331', 'en:e129', 'en:e102']</td>\n",
       "      <td>Lácteos,Comidas fermentadas,Productos fermenta...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>16.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.045000</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233830</th>\n",
       "      <td>5.974988e+10</td>\n",
       "      <td>Sliced Smoked Coho Salmon</td>\n",
       "      <td>Irresistibles,The Fish Market</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>Smoked salmons from farming</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.636000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0455</td>\n",
       "      <td>2.73</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>233831 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 _id                                       product_name  \\\n",
       "0       4.530000e+03                     Banana Chips Sweetened (Whole)   \n",
       "1       3.364000e+04                         Granola Cranberry And Acai   \n",
       "2       3.368800e+04                                Peanuts, Mixed Nuts   \n",
       "3       3.626900e+04            Milk Chocolate Peanut Butter Malt Balls   \n",
       "4       4.359500e+04                                        Cranberries   \n",
       "...              ...                                                ...   \n",
       "233826  6.811310e+11                           Triple Chocolate Brownie   \n",
       "233827  7.874222e+10                                   Chicken egg roll   \n",
       "233828  6.038308e+10  Crisp & Crunchy Spicy Southern-Style Chicken W...   \n",
       "233829  7.709150e+12                                       Yogurt Fresa   \n",
       "233830  5.974988e+10                          Sliced Smoked Coho Salmon   \n",
       "\n",
       "                               brands  nova_group  scans_n  additives_n  \\\n",
       "0                                 NaN           4      NaN          0.0   \n",
       "1                       Viva La Salud           4      NaN          0.0   \n",
       "2                    Northgate Market           4      NaN          1.0   \n",
       "3                            Sunridge           4      NaN          3.0   \n",
       "4                      Torn & Glasser           3      NaN          0.0   \n",
       "...                               ...         ...      ...          ...   \n",
       "233826                     Marketside           4      NaN          5.0   \n",
       "233827                        Walmart           4      NaN          3.0   \n",
       "233828             President's Choice           4      NaN          9.0   \n",
       "233829                         Prílac           4      NaN          4.0   \n",
       "233830  Irresistibles,The Fish Market           3      NaN          0.0   \n",
       "\n",
       "                                  additives_original_tags  \\\n",
       "0                                                      []   \n",
       "1                                                      []   \n",
       "2                                             ['en:e415']   \n",
       "3                    ['en:e322i', 'en:e500ii', 'en:e471']   \n",
       "4                                                      []   \n",
       "...                                                   ...   \n",
       "233826  ['en:e322i', 'en:e471', 'en:e211', 'en:e330', ...   \n",
       "233827                 ['en:e330', 'en:e412', 'en:e322i']   \n",
       "233828  ['en:e322i', 'en:e433', 'en:e415', 'en:e412', ...   \n",
       "233829      ['en:e14xx', 'en:e331', 'en:e129', 'en:e102']   \n",
       "233830                                                 []   \n",
       "\n",
       "                                               categories  len_countries  USA  \\\n",
       "0                                                     NaN            1.0  1.0   \n",
       "1                                                     NaN            1.0  1.0   \n",
       "2                                                     NaN            1.0  1.0   \n",
       "3                                                     NaN            1.0  1.0   \n",
       "4       Plant-based foods and beverages, Plant-based f...            1.0  1.0   \n",
       "...                                                   ...            ...  ...   \n",
       "233826  Desserts,Biscuits and cakes,Cakes,Chocolate ca...            1.0  1.0   \n",
       "233827                                           Egg Roll            1.0  1.0   \n",
       "233828                                                NaN            1.0  NaN   \n",
       "233829  Lácteos,Comidas fermentadas,Productos fermenta...            1.0  NaN   \n",
       "233830                        Smoked salmons from farming            1.0  NaN   \n",
       "\n",
       "        ... sugars_100g  fiber_100g  calcium_100g  iron_100g  sodium_100g  \\\n",
       "0       ...       14.29         3.6       0.00000    0.00129     0.000000   \n",
       "1       ...       25.45         5.5       0.03600    0.00262     0.101600   \n",
       "2       ...       14.29         7.1       0.07100    0.00514     0.217424   \n",
       "3       ...       57.50         2.5       0.10000    0.00090     0.038608   \n",
       "4       ...       66.67        10.0       0.00000    0.00000     0.000000   \n",
       "...     ...         ...         ...           ...        ...          ...   \n",
       "233826  ...       39.20         2.7       0.05410    0.00135     0.203000   \n",
       "233827  ...        3.75         1.0       0.03438    0.00110     0.562500   \n",
       "233828  ...        1.83         0.0       0.01830    0.00132     0.450000   \n",
       "233829  ...       16.50         0.0       0.10000    0.00000     0.045000   \n",
       "233830  ...        0.00         0.0       0.00000    0.00000     0.636000   \n",
       "\n",
       "        vitamin-c_100g  cholesterol_100g  saturated-fat_100g  vitamin-a_100g  \\\n",
       "0               0.0214            0.0180               28.57         0.00000   \n",
       "1               0.0000            0.0000                0.91         0.00000   \n",
       "2               0.0000            0.0000                7.14         0.00000   \n",
       "3                  NaN            0.0120               20.00             NaN   \n",
       "4               0.0000            0.0000                0.00         0.00000   \n",
       "...                ...               ...                 ...             ...   \n",
       "233826             NaN            0.0676               12.20             NaN   \n",
       "233827             NaN            0.0100                1.25             NaN   \n",
       "233828             NaN            0.0688                4.59             NaN   \n",
       "233829          0.0012            0.0100                1.50         0.00003   \n",
       "233830          0.0000            0.0455                2.73         0.00000   \n",
       "\n",
       "        trans-fat_100g  \n",
       "0                0.000  \n",
       "1                0.000  \n",
       "2                0.000  \n",
       "3                  NaN  \n",
       "4                0.000  \n",
       "...                ...  \n",
       "233826           0.000  \n",
       "233827           0.000  \n",
       "233828           0.183  \n",
       "233829             NaN  \n",
       "233830           0.000  \n",
       "\n",
       "[233831 rows x 25 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFOFF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60843900",
   "metadata": {},
   "source": [
    "# Missing Data in Nutrition Facts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f077ef2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "proteins_100g             0\n",
       "fat_100g                  0\n",
       "carbohydrates_100g        0\n",
       "sugars_100g               0\n",
       "fiber_100g                0\n",
       "calcium_100g              0\n",
       "iron_100g                 0\n",
       "sodium_100g               0\n",
       "vitamin-c_100g        44555\n",
       "cholesterol_100g          0\n",
       "saturated-fat_100g        0\n",
       "vitamin-a_100g        46099\n",
       "trans-fat_100g         5142\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nl=['proteins_100g',\n",
    " 'fat_100g',\n",
    " 'carbohydrates_100g',\n",
    " 'sugars_100g',\n",
    " 'fiber_100g',\n",
    " 'calcium_100g',\n",
    " 'iron_100g',\n",
    " 'sodium_100g',\n",
    " 'vitamin-c_100g',\n",
    " 'cholesterol_100g',\n",
    " 'saturated-fat_100g',\n",
    " 'vitamin-a_100g',\n",
    " 'trans-fat_100g']\n",
    "RFOFF[nl].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5f8f81d",
   "metadata": {},
   "source": [
    "# Keep 11 nutrients including Trans-Fats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0c99130a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/giulia/anaconda/lib/python3.6/site-packages/pandas/core/frame.py:3069: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "RFOFF=RFOFF[RFOFF['trans-fat_100g'].notna()]\n",
    "# select the right nutrients\n",
    "nl=['proteins_100g',\n",
    " 'fat_100g',\n",
    " 'carbohydrates_100g',\n",
    " 'sugars_100g',\n",
    " 'fiber_100g',\n",
    " 'calcium_100g',\n",
    " 'iron_100g',\n",
    " 'sodium_100g',\n",
    " 'cholesterol_100g',\n",
    " 'saturated-fat_100g',\n",
    " 'trans-fat_100g']\n",
    "\n",
    "RFOFF[nl]=RFOFF[nl].apply(np.log).replace(-np.inf,-20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4d1bddda",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/giulia/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "RFOFF['pythonlabel']=(RFOFF['nova_group']-1)\n",
    "X=RFOFF.loc[:, nl].copy()\n",
    "y=RFOFF['pythonlabel'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "00dd1eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# zip file\n",
    "with ZipFile(pathdir2+'OpenFoodFacts11p_Performance.pkl.zip', 'r') as zObject:\n",
    "    zObject.extractall(path=pathdir2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "847c8adc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: average over the folds [0.98798905 0.9860438  0.93203265 0.95076783]\n",
      "AUC: std over the folds [0.00064719 0.00447191 0.00152541 0.00085953]\n",
      "AUP: average over the folds [0.94917486 0.85326995 0.82867634 0.9724672 ]\n",
      "AUP: std over the folds [0.00269426 0.01671317 0.00275089 0.00071206]\n"
     ]
    }
   ],
   "source": [
    "if reproduceflag:\n",
    "    (performancesAUC11P, performancesAUP11P, splits11P, model_per_fold11P)=joblib.load(pathdir2+'OpenFoodFacts11p_Performance.pkl')\n",
    "    print(\"AUC: average over the folds\", performancesAUC11P.mean(axis=0))\n",
    "    print(\"AUC: std over the folds\", performancesAUC11P.std(axis=0))\n",
    "    print(\"AUP: average over the folds\", performancesAUP11P.mean(axis=0))\n",
    "    print(\"AUP: std over the folds\", performancesAUP11P.std(axis=0))\n",
    "else:\n",
    "    smoteflag=False\n",
    "    (performancesAUC11P, performancesAUP11P, splits11P, model_per_fold11P)=AUCAUPkfold(X,y,smoteflag);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc3c1be8",
   "metadata": {},
   "source": [
    "# Including Additives Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "18f4e736",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=RFOFF.loc[:, nl+['additives_n']].copy()\n",
    "X.fillna(0,inplace=True)\n",
    "y=RFOFF['pythonlabel'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eca1ce17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: average over the folds\n",
      "[0.99255319 0.98778501 0.96530099 0.97824244]\n",
      "AUC: std over the folds\n",
      "[0.00032709 0.00468209 0.00096202 0.00068267]\n"
     ]
    }
   ],
   "source": [
    "# keep same splits as before\n",
    "(performancesAUC11PA, performancesROC11PA, mean_fpr11PA, mean_tpr11PA, std_tpr11PA, tprs_upper11PA, tprs_lower11PA)=ROCkfold(X,y,splits11P)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "243cdff5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUP: average over the folds\n",
      "[0.9629157  0.89025262 0.89563901 0.98872219]\n",
      "AUP: std over the folds\n",
      "[0.00158259 0.01305424 0.00298316 0.00038775]\n"
     ]
    }
   ],
   "source": [
    "(performancesAUP11PA, performancesPrecisionRecall11PA, mean_recall11PA, mean_precision11PA, std_precision11PA, precision_upper11PA, precision_lower11PA)=PrecisionRecallCurvekfold(X,y,splits11P)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b467541",
   "metadata": {},
   "source": [
    "# Using just the Number of Additives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7cde3081",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: average over the folds\n",
      "[0.85992325 0.83202339 0.82025567 0.90730657]\n",
      "AUC: std over the folds\n",
      "[0.00048496 0.00337543 0.00069272 0.00060892]\n"
     ]
    }
   ],
   "source": [
    "# keep same splits as before\n",
    "(performancesAUCna, performancesROCna, mean_fprna, mean_tprna, std_tprna, tprs_upperna, tprs_lowerna)=ROCkfold(X[['additives_n']],y,splits11P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d3efbb21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUP: average over the folds\n",
      "[0.28879856 0.02622742 0.43920244 0.94154806]\n",
      "AUP: std over the folds\n",
      "[0.00092989 0.00049781 0.00126893 0.00035017]\n"
     ]
    }
   ],
   "source": [
    "(performancesAUPna, performancesPrecisionRecallna, mean_recallna, mean_precisionna, std_precisionna, precision_upperna, precision_lowerna)=PrecisionRecallCurvekfold(X[['additives_n']],y,splits11P)"
   ]
  }
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