20151106_Ingredients_with_ZRC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_json('train.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cuisine</th>\n",
       "      <th>id</th>\n",
       "      <th>ingredients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>greek</td>\n",
       "      <td>10259</td>\n",
       "      <td>[romaine lettuce, black olives, grape tomatoes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>southern_us</td>\n",
       "      <td>25693</td>\n",
       "      <td>[plain flour, ground pepper, salt, tomatoes, g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>filipino</td>\n",
       "      <td>20130</td>\n",
       "      <td>[eggs, pepper, salt, mayonaise, cooking oil, g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>indian</td>\n",
       "      <td>22213</td>\n",
       "      <td>[water, vegetable oil, wheat, salt]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>indian</td>\n",
       "      <td>13162</td>\n",
       "      <td>[black pepper, shallots, cornflour, cayenne pe...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cuisine     id                                        ingredients\n",
       "0        greek  10259  [romaine lettuce, black olives, grape tomatoes...\n",
       "1  southern_us  25693  [plain flour, ground pepper, salt, tomatoes, g...\n",
       "2     filipino  20130  [eggs, pepper, salt, mayonaise, cooking oil, g...\n",
       "3       indian  22213                [water, vegetable oil, wheat, salt]\n",
       "4       indian  13162  [black pepper, shallots, cornflour, cayenne pe..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cuisine\n",
       "brazilian        467\n",
       "british          804\n",
       "cajun_creole    1546\n",
       "chinese         2673\n",
       "filipino         755\n",
       "french          2646\n",
       "greek           1175\n",
       "indian          3003\n",
       "irish            667\n",
       "italian         7838\n",
       "jamaican         526\n",
       "japanese        1423\n",
       "korean           830\n",
       "mexican         6438\n",
       "moroccan         821\n",
       "russian          489\n",
       "southern_us     4320\n",
       "spanish          989\n",
       "thai            1539\n",
       "vietnamese       825\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = df.groupby('cuisine').count()['id']\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cuisine\n",
       "brazilian        467\n",
       "russian          489\n",
       "jamaican         526\n",
       "irish            667\n",
       "filipino         755\n",
       "british          804\n",
       "moroccan         821\n",
       "vietnamese       825\n",
       "korean           830\n",
       "spanish          989\n",
       "greek           1175\n",
       "japanese        1423\n",
       "thai            1539\n",
       "cajun_creole    1546\n",
       "french          2646\n",
       "chinese         2673\n",
       "indian          3003\n",
       "southern_us     4320\n",
       "mexican         6438\n",
       "italian         7838\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.sort_values(inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['romaine lettuce',\n",
       " 'black olives',\n",
       " 'grape tomatoes',\n",
       " 'garlic',\n",
       " 'pepper',\n",
       " 'purple onion',\n",
       " 'seasoning',\n",
       " 'garbanzo beans',\n",
       " 'feta cheese crumbles']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0]['ingredients']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['ingredient_string'] = pd.Series([\";\".join(ingr) for ingr in df['ingredients']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'romaine lettuce;black olives;grape tomatoes;garlic;pepper;purple onion;seasoning;garbanzo beans;feta cheese crumbles'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0]['ingredient_string']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cuisine\n",
       "french          0.002646\n",
       "italian         0.003445\n",
       "spanish         0.004044\n",
       "moroccan        0.004872\n",
       "greek           0.005106\n",
       "russian         0.006135\n",
       "british         0.006219\n",
       "brazilian       0.006424\n",
       "southern_us     0.008102\n",
       "cajun_creole    0.009056\n",
       "mexican         0.009941\n",
       "irish           0.010495\n",
       "indian          0.011988\n",
       "jamaican        0.155894\n",
       "vietnamese      0.281212\n",
       "thai            0.319038\n",
       "filipino        0.378808\n",
       "japanese        0.533380\n",
       "korean          0.661446\n",
       "chinese         0.760569\n",
       "Name: id, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[df['ingredient_string'].str.contains('soy')].groupby('cuisine').count()['id'] / total).sort_values(inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        romaine lettuce;black olives;grape tomatoes;ga...\n",
       "1        plain flour;ground pepper;salt;tomatoes;ground...\n",
       "2        eggs;pepper;salt;mayonaise;cooking oil;green c...\n",
       "3                           water;vegetable oil;wheat;salt\n",
       "4        black pepper;shallots;cornflour;cayenne pepper...\n",
       "5        plain flour;sugar;butter;eggs;fresh ginger roo...\n",
       "6        olive oil;salt;medium shrimp;pepper;garlic;cho...\n",
       "7        sugar;pistachio nuts;white almond bark;flour;v...\n",
       "8        olive oil;purple onion;fresh pineapple;pork;po...\n",
       "9        chopped tomatoes;fresh basil;garlic;extra-virg...\n",
       "10       pimentos;sweet pepper;dried oregano;olive oil;...\n",
       "11       low sodium soy sauce;fresh ginger;dry mustard;...\n",
       "12       Italian parsley leaves;walnuts;hot red pepper ...\n",
       "13       ground cinnamon;fresh cilantro;chili powder;gr...\n",
       "14       fresh parmesan cheese;butter;all-purpose flour...\n",
       "15       tumeric;vegetable stock;tomatoes;garam masala;...\n",
       "16       greek yogurt;lemon curd;confectioners sugar;ra...\n",
       "17       italian seasoning;broiler-fryer chicken;mayona...\n",
       "18             sugar;hot chili;asian fish sauce;lime juice\n",
       "19       soy sauce;vegetable oil;red bell pepper;chicke...\n",
       "20       pork loin;roasted peanuts;chopped cilantro fre...\n",
       "21       roma tomatoes;kosher salt;purple onion;jalapen...\n",
       "22       low-fat mayonnaise;pepper;salt;baking potatoes...\n",
       "23       sesame seeds;red pepper;yellow peppers;water;e...\n",
       "24       marinara sauce;flat leaf parsley;olive oil;lin...\n",
       "25       sugar;lo mein noodles;salt;chicken broth;light...\n",
       "26       herbs;lemon juice;fresh tomatoes;paprika;mango...\n",
       "27       ground black pepper;butter;sliced mushrooms;sh...\n",
       "28       green bell pepper;egg roll wrappers;sweet and ...\n",
       "29       flour tortillas;cheese;breakfast sausages;larg...\n",
       "                               ...                        \n",
       "39744    extra-virgin olive oil;oregano;potatoes;garlic...\n",
       "39745    quinoa;extra-virgin olive oil;fresh thyme leav...\n",
       "39746    clove;bay leaves;ginger;chopped cilantro;groun...\n",
       "39747    water;sugar;grated lemon zest;butter;pitted da...\n",
       "39748    sea salt;pizza doughs;all-purpose flour;cornme...\n",
       "39749    kosher salt;minced onion;tortilla chips;sugar;...\n",
       "39750    ground black pepper;chicken breasts;salsa;ched...\n",
       "39751    olive oil;cayenne pepper;chopped cilantro fres...\n",
       "39752    self rising flour;milk;white sugar;butter;peac...\n",
       "39753    rosemary sprigs;lemon zest;garlic cloves;groun...\n",
       "39754    jasmine rice;bay leaves;sticky rice;rotisserie...\n",
       "39755    mint leaves;cilantro leaves;ghee;tomatoes;cinn...\n",
       "39756    vegetable oil;cinnamon sticks;water;all-purpos...\n",
       "39757    red bell pepper;garlic cloves;extra-virgin oli...\n",
       "39758    milk;salt;ground cayenne pepper;ground lamb;gr...\n",
       "39759    red chili peppers;sea salt;onions;water;chilli...\n",
       "39760    butter;large eggs;cornmeal;baking powder;boili...\n",
       "39761    honey;chicken breast halves;cilantro leaves;ca...\n",
       "39762    curry powder;salt;chicken;water;vegetable oil;...\n",
       "39763    fettuccine pasta;low-fat cream cheese;garlic;n...\n",
       "39764    chili powder;worcestershire sauce;celery;red k...\n",
       "39765    coconut;unsweetened coconut milk;mint leaves;p...\n",
       "39766    rutabaga;ham;thick-cut bacon;potatoes;fresh pa...\n",
       "39767    low-fat sour cream;grated parmesan cheese;salt...\n",
       "39768    shredded cheddar cheese;crushed cheese cracker...\n",
       "39769    light brown sugar;granulated sugar;butter;warm...\n",
       "39770    KRAFT Zesty Italian Dressing;purple onion;broc...\n",
       "39771    eggs;citrus fruit;raisins;sourdough starter;fl...\n",
       "39772    boneless chicken skinless thigh;minced garlic;...\n",
       "39773    green chile;jalapeno chilies;onions;ground bla...\n",
       "Name: ingredient_string, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ingredient_string']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# La matrice des poids "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_json('train.json')\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(token_pattern=r\"(?u)\\b\\w[\\w\\s]+\\b\")\n",
    "X = cv.fit_transform([\";\".join(_) for _ in df_train.ingredients])\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "enc = LabelEncoder()\n",
    "y = enc.fit_transform(df_train.cuisine)\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "logistic = LogisticRegression()\n",
    "logistic.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x11038a048>,\n",
       "  <matplotlib.axis.XTick at 0x110309f98>,\n",
       "  <matplotlib.axis.XTick at 0x1103348d0>,\n",
       "  <matplotlib.axis.XTick at 0x11080d240>,\n",
       "  <matplotlib.axis.XTick at 0x110879908>,\n",
       "  <matplotlib.axis.XTick at 0x11088b7b8>,\n",
       "  <matplotlib.axis.XTick at 0x11089b390>,\n",
       "  <matplotlib.axis.XTick at 0x110867710>,\n",
       "  <matplotlib.axis.XTick at 0x1108b6a58>,\n",
       "  <matplotlib.axis.XTick at 0x1108c9780>,\n",
       "  <matplotlib.axis.XTick at 0x1108da588>,\n",
       "  <matplotlib.axis.XTick at 0x1108ec7b8>,\n",
       "  <matplotlib.axis.XTick at 0x1108fe0f0>,\n",
       "  <matplotlib.axis.XTick at 0x110a0e1d0>,\n",
       "  <matplotlib.axis.XTick at 0x110a201d0>,\n",
       "  <matplotlib.axis.XTick at 0x110a31a90>,\n",
       "  <matplotlib.axis.XTick at 0x110281748>,\n",
       "  <matplotlib.axis.XTick at 0x110295320>,\n",
       "  <matplotlib.axis.XTick at 0x1102a0fd0>,\n",
       "  <matplotlib.axis.XTick at 0x1102afba8>],\n",
       " <a list of 20 Text xticklabel objects>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kappamaki/anaconda/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
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7d0ZLAGDmzJnREjRj5/bt26MlcOGFF0ZLABypqcnIMDhpTXFYlP2ITiuyPFjD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\nEQRBEARBEARBt/L/AZ2AC8oBy25lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1102d94a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "plt.imshow(-np.abs(logistic.coef_.T), aspect='auto', cmap='gray', interpolation='nearest')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(20), enc.inverse_transform(np.arange(20)), rotation='90')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 6815)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic.coef_.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Et si on regardait ligne par ligne (ingrédient donné) ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
     ]
    }
   ],
   "source": [
    "from IPython.html.widgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tqJIXM7NFkSQiYli7ZqWSe0TMkXQYcCEwCTg1Im6XdGhefzLwTuADkuYAzwLvqnJMMzMb\nW6WSe51ccjczK2+kkrtHqJqZNZCDu5lZAzm4m5k1kIO7mVkDObibmTWQg7uZWQM5uJuZNZCDu5lZ\nAzm4m5k1kIO7mVkDObibmTWQg7uZWQM5uJuZNZCDu5lZAzm4m5k1kIO7mVkDObibmTWQg7uZWQM5\nuJuZNVDl4C5puqRZku6UdOQI2xyf198kacuqxzQzs9FVCu6SJgHfAKYDmwD7SNq4Y5tdgPUjYgPg\nEODEKsc0M7OxVS25bwvcFRGzI+IF4Cxg945tdgNOB4iIq4CVJK1W8bhmZjaKqsF9TeCettf35mVj\nbbNWxeOamdkoqgb3KLidiuwnqdRjcHBw7r6Dg4Ol9++XNNr3ryMNn4uJfx8+FwvufSzq52L//fdn\ncHBw7mMkiigan7vsLG0HDEbE9Pz6U8BLEXFs2zYnAUMRcVZ+PQvYMSIe6kgrquTFzGxRJImI6CxA\nVy65XwtsIGmapCWAvYHzOrY5D9gvZ2I74PHOwG5mZvWaXGXniJgj6TDgQmAScGpE3C7p0Lz+5Ij4\nhaRdJN0FPAMcUDnXZmY2qkrVMnVytYyZWXkLqlrGzMz6kIO7mVkDObibmTWQg7uZWQM5uJuZNZCD\nu5lZAzm4m5k1kIO7mVkDObibmTWQg7uZWQM5uJuZNZCDu5lZAzm4m5k1kIO7mVkDObibmTWQg7uZ\nWQM5uJuZNZCDu5lZAzm4m5k1UM83yJY0BTgbWAeYDewVEY932W428CTwIvBCRGzb6zHNzKyYKiX3\nTwIzI2JD4Nf5dTcBDETElg7sZmbjo0pw3w04PT8/HfinUbYddmduMzNbcKoE99Ui4qH8/CFgtRG2\nC+BXkq6VdHCF45mZWUGj1rlLmgms3mXVp9tfRERIihGSeX1EPCBpVWCmpFkRcVm3DQcHB+c+HxgY\nYGBgYLTsmZktcoaGhhgaGhpzO0WMFJPH2FGaRapLf1DSK4CLI+JVY+wzA3g6Ir7SZV30mhczs0WV\nJCJiWNV3lWqZ84D35ufvBc7pctBlJC2fny8LvBW4pcIxzcysgCol9ynAj4GptHWFlLQG8J2I+EdJ\n6wI/y7tMBs6MiC+NkJ5L7mZmJY1Ucu85uNfNwd3MrLwFUS1jZmZ9ysHdzKyBHNzNzBrIwd3MrIEc\n3M3MGsjB3cysgRzczcwayMHdzKyBHNzNzBrIwd3MrIEc3M3MGsjB3cysgRzczcwayMHdzKyBHNzN\nzBrIwd3MrIEc3M3MGsjB3cysgRzczcwaqOfgLmlPSbdKelHSVqNsN13SLEl3Sjqy1+OZmVlxVUru\ntwB7AJeOtIGkScA3gOnAJsA+kjaucEwzMytgcq87RsQsSHfeHsW2wF0RMTtvexawO3B7r8c1M7Ox\nLeg69zWBe9pe35uXmZnZAjRqyV3STGD1LquOiojzC6QfZTIzODg49/nAwAADAwNldjcza7yhoSGG\nhobG3E4RpeLv8ASki4GPRcT1XdZtBwxGxPT8+lPASxFxbJdto2pezMwWNZKIiGH143VVy4xU8X4t\nsIGkaZKWAPYGzqvpmGZmNoIqXSH3kHQPsB1wgaRf5uVrSLoAICLmAIcBFwK3AWdHhBtTzcwWsMrV\nMnVxtYyZWXkLulrGzMz6iIO7mVkDObibmTWQg7uZWQM5uJuZNZCDu5lZAzm4m5k1kIO7mVkDObib\nmTWQg7uZWQM5uJuZNZCDu5lZAzm4m5k1kIO7mVkDObibmTWQg7uZWQM5uJuZNZCDu5lZA1W5h+qe\nkm6V9KKkrUbZbrakmyXdIOnqXo9nZmbFTa6w7y3AHsDJY2wXwEBEPFrhWGZmVkLPwT0iZkG6OWsB\nhTYyM7N6jEedewC/knStpIPH4XhmZou8UUvukmYCq3dZdVREnF/wGK+PiAckrQrMlDQrIi4rm1Ez\nMytu1OAeEW+peoCIeCD/fUTS/wDbAl2D++Dg4NznAwMDDAwMVD28mVmjDA0NMTQ0NOZ2iohKB5J0\nMfDxiLiuy7plgEkR8ZSkZYGLgM9GxEVdto2qeTEzW9RIIiKGtWtW6Qq5h6R7gO2ACyT9Mi9fQ9IF\nebPVgcsk3QhcBfy8W2A3M7N6VS6518UldzOz8movuZuZWf9ycDczayAHdzOzBnJwNzNrIAd3M7MG\ncnA3M2sgB3czswZycDczayAHdzOzBnJwNzNrIAd3M7MGcnA3M2sgB3czswZycDczayAHdzOzBnJw\nNzNrIAd3M7MGcnA3M2sgB3czswaqcoPs4yTdLukmST+TtOII202XNEvSnZKO7D2rZmZWVJWS+0XA\nphGxBfB74FOdG0iaBHwDmA5sAuwjaeNeDjY0NNR7TvsojX7IQ7+k0Q95qCONfshDv6TRD3nolzQm\nOg89B/eImBkRL+WXVwFrddlsW+CuiJgdES8AZwG793K8iT5RdaXRD3nolzT6IQ91pNEPeeiXNPoh\nD/2SxkTnoa469wOBX3RZviZwT9vre/MyMzNbgCaPtlLSTGD1LquOiojz8zafBp6PiB922S6qZ9HM\nzMpSRO/xV9L+wMHAzhHxty7rtwMGI2J6fv0p4KWIOLbLtv4hMDPrQUSoc9moJffRSJoOfALYsVtg\nz64FNpA0Dbgf2BvYp2jmzMysN1Xq3E8AlgNmSrpB0rcAJK0h6QKAiJgDHAZcCNwGnB0Rt1fMs5mZ\njaFStYyZmfUnj1A1M2ugnuvcFzRJOwAzgGnMy2dExLol0tgI+HiXNHYqmZdlgLUj4o4y+3WmERHP\nVth/CWAjUg+kO/K4gXElabG2sQ2tZUtGxN9LpDEJWI22715E3F1i/5eTGvGnMf9nemDRNHI6a+Y0\nJgHKaVxacN/K3ytJywL/DkyNiIMlbQBsFBE/L/wmaiBpKeBfGP5ePlcijaqfaR15WB+4NyL+JulN\nwGbAGRHxeIk0avlu9Yu+De7AqcBHgOuBF3tM4yfAicApbWmUqoeStBtwHLAkME3SlsBnI2K3gvtv\nn4+/PLC2pNcAh0TEv5XIwwBwOvDnvGiqpPdGxCUl0vgX4BjSP2Gr8ToiYoWiaZA+kwPa0lwOOA8o\nFNQkfYj0g/0w83+mm5XIw7nApcBMoPVDU/YzPZbUuH9bRz4KBXdq+F4BpwHXAdvn1/cDPwUKB/ea\nPtNzgcdzXkbqGDFaHur6THvOQ/bfwNY5yJ+c0/whsEvJfJT+bknaOSJ+nT+PYN5nAenz+FnRDEha\nHfgPYM2ImC5pE+B1EXFq0TTaj9yXD+CqGtK4roY0rgdWAm5oW/a7EvtfDUzt2P/WHvKwUdvrDYHr\nS6bxB2Djiufi88C38vOVgSuAA0rmYZWKebixhs/098CSE/y9ui7/bf9e3DQBn2nh7/IC/Ewr5aH9\nPAJHAB/qPLcF0+jpu0Uq7AF8j/SjPd+jZFr/Syp43JxfL97r+ennkvvFko4DfgbMveyPiOvH2lHS\nFNKv5/mSPtgljUdL5OOFiHhcmq+n5ksjbdxNRNzdsf+cMvsDk6OtSigifi+p7Gf3YFTsqRQRR+cJ\n404GtgaOiYiflkjibuDJKnkAfi7pHyPiggpp/AFYgrbvREl1fK/+Lmnp1gtJ6/WQn8qfKXCFpM0j\n4uYe96/jM62aB4DnJb0b2A/YNS9bvGQaPX23ImJG/rt/yeN187KIOFvSJ3OaL0gqGy+A/q6W2Y50\nibNNx/I3Fdj3eua/nPp4x/pXlsjHrZL2BSbnetHDSSXWou6W9HqYW29+OFD2H/I6SacAPyD9aO1L\nGkMwpnypCHCtpLOBc4Dn87KIApeMbWkEcCVwNHANEJL+uUga2Z9IP9oXdOThqwXy8DTzPtOjJD0P\ntNodIspVRTwH3Cjp18wLqBERhxfcf/+clyrfq0FSKW0tST8EXp/TLaPnz7TNG4ADJP2J+c/F5gX3\n7/kzrTEPkKZAeT/wHxHxJ0nrkv5fxlTnd0vSO0iTJC7VWhYl2g6ApyWt0pbedsATJfafl5dc9LcR\n5IavTwNvzYsuBD4fIw/c6tx/VeDrwJtJgfki4PCI+GuJPCwFfJAUAAAuI1WPjFnSk/Q95n1xRUcd\nYkQc0LnPGGkMS6dIGjmdwdYu7elExGeL7F+XPLK6U0TE6eOcj5eRCjEAV0bEX0ru/738tPRn2pbG\ntG7LI2J2wf0HO/JQ+jOtmod+ka9olya1QX0H2JNUvfy+EmlsTRpDtClwK7Aq8M6IuKl0fvo5uFf9\nFZS0J3BhRDwp6WhgS+ALRap2rD/lq6CbIuJpSe8hfaZfj4g/j7Fr3fl4NcO/m2eU2H8HUh3vhL6P\ntvy8nPnfS+HeLv2Qh1zq7xRRrnddpe+WpFsiYjNJN0fE5rnDwf9GxA5F85DTWZzUMw6q9IzrpaJ+\nPB6kFu8zSDNJzgB+B5xaMo1b8t8dgCHgHcDVBfc9f5THeSXycBywAqn+79fAX4D3lHwfO5Ba8O8k\nXQb/CfhjyTROB1Zqe70y8N2SaWyU38Ot+fXmwGdK7P9y4D9JM4henB+/KfuZksZnbAHcQBoBfUnJ\nNDYk9Uy5rZfzSapSuZjUQ+Q04EHgpxXfxwd7eB9L5/f/rZyP7/bwme6Wv1fP5PPwEiUa/Gv6TCvl\nIafxsrbHWqSedp8fz+9WK7aQqi7XJP1Q3VUyD3sBK+TnR5PadbYqk8bctHrZaTwezAvMrVbj5YDf\nlkzjxvz3GGDf/LxQCzowMMpjxxJ5uCn/3YPUlXDF1nsqkcYdwNtJXd7mfol7ORdjLRsjjUuB1zKv\nZ4JKBoKZwEHALGDHHJC+XDIPrWPPAA7Kz8v2HLqcVE12M7AOKVgXDgSkgsakts92NeBXE/A+fkrq\nwfRH4L35/B5fMo2b8/eplZ83UeIHoqbPtFIeRkm37Pms9JmQgvHKpD77D+ZH6R+Y/Ld0gXRYWlVP\n4IJ6UM+v4AXAt0mlgZVyGqW6m+V0liT12301sHjJfVul3FOBt+fnZbu81dEt9CZgStvrKa0vUok0\nrs1/27vvFf6BaP2j0Pbj1kqzRBqXAkeRSnqr5yBb9n208nFL57KC+1+T/15H+rEW6fJ5vN9Hq/DS\n3m2u1HeFeV0ybwImdX4+4/SZVspD3n5rYKv82IbUuFr2/6zyZ9KW1lK0XSn38JmWLpB2Pvq5t8zP\nJa1Mqta4Li/7Tsk09iLd4u+4SN0ZX0GaybKwGgYQnS9pFmlwxgdyvWLZgRo9dwtt8xXg/yT9mBSM\n9iQNlijjkTxIBABJ7wQeKLF/qzfFg7k95X5SSaeMvYF3AwdGxIOSppKqBcr4Wx5VeZekw3I+li2x\n/zX5u/kdUq+lZyjXgwrmzZDa/j6OK5lG63w+IWkzUklx1ZJpPCZpeVIj/ZmSHgae7iEPVT7TqnmA\n9P2O/HwOMJv0/19G5e9WrrefRvphQBJRoi0GuE/St4G3AMfkzhQ9TRPT1w2qLfkNLhUFhxJLWiFS\nI+qUbuujRH9kSdcD+0TuZy5pQ+CsiNiqRBpTgCci4sXc+2b5iHiwxP5DdBkpFxFFuoW2p7MpqSU/\nSPWit5Xcfz3SldD2wGOkK6J9o3jPil1J/8Brk3oErECa7/+8MvmoStK2pO6oK5GqNVYgVSVc2UNa\nryTVkZbvzZB6iawfEb9SmuJickQU7jMu6WDSyMzNSANolgOOjoiTSqSxLKmwsRipi+0KwJlRsDdX\nHZ9pbnh8rtc8FDzGe2MB94aS9ANgXeBG2kbrRsSHSqSxLPA20hXDnblAullEXFQ6P/0W3DV8KO98\noli/7Asi4h8lzR4hjcL9kVst32MtG2X/vphDJOdlEulyczL5vEQPvSLye5pUJhBVJenyiHh9R5/k\nlohy/dyr5mUP4OJWYUPSSsBARJxTIo1DSPOYTImI9XKh4cSI2HmBZHrkfLySNBjqufx6aWC1oj/Y\nCwtJN0TEliOsq+W7Jel2YJOoGFQlvYH0o39a7kq9fET8sXQ6fRjcPxsRM7r0rQbK9eGtKT+nkX6F\n2wcQLRYFJxPK1SDXAftFxKY5MF4REVsU2Pc9EfF9SR+jSz/zKDFQRCPMARIRhecAUY/zXkg6MiKO\nlXRCl9URxQcPVSLp6xHxYUnnj5CPovMF3dT5+Um6MSJeUyIvN5FuIH9lK+i0utKVSKPyPCSSrsv7\nPJ9fLwkqlqttAAAT00lEQVRcHhGdgwdH2v904CMR8Vh+vTLwlSL/H+P5gz1acK/xGD8BPhwR91dI\nY5DUfrBRRGyoNMHdjyPi9aPvOVzf1blHjUN5Jf26syTUbdkYPkDqptYKQJeRup4VtV5E7CXpXQAR\n8Yzmn4pgNMvkv8szyiCigj5C+sJUudT9Hqk3xKfz6zuBH5Mai0fTqv65rsu6wu9DacqF30XEq4ru\n0+H7+e9Xetx/bla6LJtUMo2/R8TfW9+F/N7Kfqbfo7fPo92kVmAHyHkqM2x/i1Zgz/s/JqlQlWUr\nYEXEciWOt0Cphxku2woLywG3Sbqa+UfaFio0ZHuQ+tdfl3e+L7dHlNZ3wT2XUjsFJUqr+dJyGWDV\njnr3FUg9bwqLNIXoicAFETGrzL5Zz3OIRMTJ+e9gD8ftVMccID3NexH5ZuoR8b0qB4+IOZLukLRO\n9DDYJyKuzX+HquSDNB3EV4Fvkr6XH6T7D9doLlG6ufwykt4C/BtpDEUZdcxD8hdJu0fEuQCSdieN\nxShKkqa02rHy/1upH7r8P3FfVJiutw4jXd0y9gyXrcLCl4Hdmf/H/8sls/H3iHip7Ue/TEP/fPou\nuDO8lNpSprR6KPBhYA3m/6d7CvhGmcyo4pS/1DCHSP7yfw14HekcXAF8tGQ93J+oPgdIT/NedFSD\ntH6o574uWbKZQprv52pSL5XCaUi6ZZTVUbQdBfgQqU/z2fn1TFKAL+OTwPtIA2cOJQ0COqVkGnXM\nQ/J+Ug+V1v/FvcB7SuxfRy+sn1F9ut6xXF5gm56ubluFBUmLR0cvuvaCXUE/UZrGYKXcLnMg5b8X\n6dj9VudeJ0kfiohu9bxl0rie1MPk4ra60d9FxKtLpFF1DpGrSD9KZ+VFe5OmNX1tiTQG89P5PvAo\nNwdIT/NeKHUnhXTJuTrz2i/2AR6KiI+UyMNAt+VFSuMaYQ6TtjRmF81HP+j18xghreUAIqJsF8Q6\nemHdEBFbSjoCeC4iTihbR656bvhxMfDWKDncX9IHSFde65FmHG1ZntR+sW/J9N5K21xWETGzzP5z\n0+m34F5H45uknSLiN6rQ46Ytrasi4rXtXzaV6C2Tt2/d9ae9l0rRG0OM1GNnWKNewbSWjYhnxt5y\n2H6TSO0OJwCvgrkDd54fdcf507guIrYea1m/qqtBNqdV6U5jVT+Puhrr89XCbZF7TklagTTH/FVF\n9s/7XEWaXO8o0nS9s0ldAcsUoC5k3g0/2jsMFG5fkfRd0tQUpa5uJa1I6tt/DHAk865Mn+q1jSun\n2R4vykwnDfRntUxn41vri1emWmZH4DekobvdlJkStdKUv6p+1x+AX0r6FPCj/HrvvGwKFPvgNfyO\nUFsAh0bBO0JF6qP/7oj4L9Lw+14sI2m9iPhDztO6zGs0HlWdPSskvQ44njTx1xKkOuKnC6TRGoxS\ntUEWKt5prIbPY6TG+rJOIjUAtjzTZdlYDiRVTbWm630l8xq/i1ozIt5Wcp9Od+fHEvlRKOZExBOk\n6rB3VTw+kg4FPktql2u/G1ThCdDmptVvJfcWSW8kXdK82LZs64go1HAlaTFgz4g4e8yNR09nGeAz\n9D7l7+9JgxB6vTEEGqG/flaotJfrqN8JnNt2BXJrRGxaIh//RRrifjbpn7hVyis0UlbSdOZNBwGp\n1HpIRFxYNA91UOr+9y5Sz5JtSDd42CgiPjmOebiqTLXaCGlU+jxyGoqOIKAS98VVly6gZa9sO/ad\nAqwVJW/coTSq8xtl9+s3ku4Ctitbdds1rT4O7s+SbgixV0Q8lJeVrYerdMmv1D1tZpQcCdqRxi9J\n7+GpXtOog6SrI2LbjuqlUlU7uU6yU0S5G0MvRapGCGBWlR+9XrW+F+1BqFuQGmX/DYEvkuq6W1PU\nFq5SyWkcQ7pi6HlKCdUwclnSJcD+EfGn/Hpb4JSiwVnS/5BmgjyR9OPyAeBNEfFPJfOwK6km4Trg\nEVLB7qMl0rgdWJ9UcOjphh9KU4McQbqiazWElvp+VyXpImCPXqpOO/VjtUzLHaR5HYYkHRQRRVq7\nO82U9HHmlWyA4vVXkbrevSRppei9W1bPd/1RDaN129RxR6huo2qfkPSaiLixYBpbAa8kffe2UPm5\nN+rwjNJgnZskfZk0J0vhwQekvuUzgK+S5i46gPL93KvcaQyAiBgoecxuvkiq4juB1E347ZTrzfV+\nUhXXZ/LrXwOHlMzDipGmCzmI1AVyhkbv2TQfSSJV61Sdg/5MUqx4R05vf9IPzXj6JKn30f8xf71/\n6YF+/Vxyb7Wgb0A64aeRJvQpU3KfTfXpB84j1R/OZP6ud4VOtirc9Uc1jtZV6rFzPNXuCPVDUjBq\nNSi+g9SVbx3SfObHjrF/5bk36iBpHVJf5iWAj5LGP3wrIu4quP/1EbGV2kaUtpYtsEx3z8dKpB+Z\nN+ZFQ8Dnch1wmXTeRPp+PwJsGSXmPapDDuRvJU3Q95mIuLpM1U4O7qUaYEdIp/W5tl/RXRsFR+vW\nQdK1pPa4W0h17q2qttLz4vRzyR2ASJPnvJF0I4Ky9Xgbk/of70A6Ub8lXT6W8bP86KVht9LAnahp\ntG6uXvp6RLy7SjqkyaG2itxdTtIMUv/sHUmX06MGd9Kw6spzb9TgnyLi66SrqkEASR8m9dgoouqs\nkuRjVr3f5ndJQWBP0vfyPaRC0D+XyMPRpAb6N5D+vy6R9LEoOPeRpLVJhYbW3YYuJQ3Bv7doHoDP\nkdqyLs+BfT3SaNtCIiIkXSdp24i4usRxO9Uxw2VVkyLi3+tIqG9L7t1Imhrlbr31E9KozFa/6neT\nLgH3LJHGcqS+ty/m15NIM1SOWicm6ScRsecIl5eF6gJVw2jdtrR+C+xcsWF3FrB5zD8Pyc0RsVGR\n9hDVMPdGHbrltWSde+VZJVXP/Ta7zXFTth3la8CnYt7EYeuQ6tzfUnD/X5GqM1o3o96XNFNoof3r\nIukOUp37n5n/CrtMnfuEz1oq6Yuk93Ae87fFlO4K2bfBXWlk1/uY12jV6u9ZaMKunMZtEbHJWMvG\nSONK4M1tpdXlSQMLth9jvzUi4n5VuPmv0sCjEUfrRrkBSGeQrmTOA56dl41SPxBHk0qF5+Q87JrT\n+0/g2zHGYI3cAPgaoMrcGz2TtA/pB/4NpH/iluWBF2McZ2RUDffbzN/NT0TEZfn1DqR7F7yuZF6W\nJs1aekeZ/fK+dfzAnEb3kctl/tendVte5P+sn9RRldzSz9Uy3yeVjt5G6vf5r5RvALxe0usi4v+A\n1oCLsnOALBVto/Yi4iml7pGjapVOq3y5ooY5ZSR9PyLeQ7pP5X+R5szuaaKmiPi8pP8lTaEQpH7y\n1+bVRUbhDfZy3BpdQbq5yKqkH6S5g01IdwEqRNI/kAbcTGP+AUhlqg2fy3+fVRrk9lfS6N0yDgO+\nqzTgBdIc+z8tk4CqT6/xV6WbSf+QdD7fRbm5aSANGmoFtKVJI5lLXd1FxGwNnyq31Pdc9UzzUUlE\nTKszsb58UOEWYqR6yFtIPwYvkS5zZufnt5fMx+XA1m2vtwH+r8T+/0KqP3ySFESeAp4suO8R+e8J\nXR6F7pVJGjy1Buk+lVOAVdofE/05L4wP4PekH8t1SQF+GjCtZBrt99t8iN7ut3k9aVKrFfPj3ZS8\n32ZOYyXmv3Xi70rsvw6pgf2R/DiXdBVQ5fwuVuZ/LO8zmPPx+/x6TVIdfpk0riK1WyyeH/9aNObU\n/P16NekuUvu1Hr2k088l9yq3ENt1lHW9TJX7Y0mt28m9gtQAVdSXgXdERNmrDkjdor5Mmq/iMTou\nWwumcRKpe9q6DL9q6WnkW1nqkxtt1JiPR6JiPWxEfD4//W9JPyddIZad9OudpJJ6q6ppP9Lt2cp4\nIdItKNuXvTTSxu1yQ/0XI2K0/7debEj52wXWMVXu0hHRPjL2B5JK3ZazqlwVuyOpOvoCUtfU3zJv\ndHRh/Rzcv600Wu0zpHrd5UilnTFFjfVsEXGNpI2BjfKi+ebvkPSWGH1inwd7DOyQWu3XIA3PHqCH\n4B4RxwPHSzopIt7fYz4qiT6Zt7vGfHxW0qnAr5i/L3KZOYuWJk02tQPps7xM0olRcORzPuAfczvC\nOaSr07dFxLNj7Nap5+k1Io0DWUclRrR2UhpJPoe2RlDSlcyRJZOqY6rcytN81OCdwBakG48fIGk1\nUoN1aX3ZoKqapg4YDyP1ElEaeASpD/LqpH/AUoFA0uGkEX/rMrwOMqLEiEirj6QzST/2t9JWyo1y\n4w567snVpQfWy0mTZj1P+R4iy5LaDzqn1yg6/cD3SSOOe2qor7GP+idIvWXeCnyJVCD6YS7cFE1j\nNhWn+ahK0jUR8Q9KU2TsRPqOzIqIjcbYdZi+LLnnX+AjmDdf9sJoV+Z9UZ5l3j8PefmYwb0fSt3W\n1TbAq6JayWjTmL/X1m8kFZ0qt85qkE3yY3J+7E5qTyj6A/GH/Gg11JcdB1JLH/WIOE5pqtynSNU6\nR49xRd3NEaQeS09K+n+kap4vRMH5rGpyjdKtCr8DXEu6oik8UWG7viy5w9y5N/5Cj1MHjJex+nfn\nLogfjnn3mJxCusfkuN4L1uqTu+79Z0TcWiGNHwDfjPl7cn0wUs+mcaM0sd3HSTNLtl+FzC6ZzvJ5\nv9JzKNXRR70Obd1TdwC+QOpRdXRUnOCtQn5eSbo5dk+TofVzcJ9NTf09F6QCwb3brHmlbqZs/SUP\n5lqPapNUzSKVMO8hfc+nkuZTmlM2rSpajcwV9t+M1NjXuiPUI8B7I6LwNMR19FHP1aDHkO5/2mqb\nKtVY3/q/zAXLWyLizLH+vxeE3DV2HdKVVGtMS5kpwoE+rZbJ6pg6YDz8aYz1UsV7TFrfmd4nadSh\nauPwt4F/j4iLAZTulPVtYNRBfu1q6gBRpVday31KUwe/BThGaQbTxWrIW2Gq5/4PKa0+LrlXnjqg\nxry8nuEDVgp1TZK0H+nu9PPdY7Lo/ta/lKaIbZ8XpvSshHWkUUXVxuE6RqjWoeoVSE5jWdKP7s2R\n5rR6BeleDBfVkslieah8/4e5afVxcK88dUBN+ag8k6Eq3mPS+kse1fkV0uCwh0mX0LdHuRufVE6j\nDrm+u+fGYUnnkPqWf59UeNmXNOhvj/pyOerxK/dK6yeq8f4P/VwtU8fUAXWoPJNhbnjrufHN+s4X\nSEPUZ0aalvpNpJGN451GHa4g9Zbp9ft5IGl0aGvm1MvysvHS3ivtOebvlQblbqnZD3q+/0Onvgvu\nbX14JwOXS+pscBpvvyONSp3QmQytr7wQEX+RtJikSRFxsaSi0wXXmUYdXkcKJr02Dq9HmkVxMVJb\n0s6kq9RxaRCOPB22pB0i4rft63Kvl4XNuaQxAz1NMd6u74I79U4dUIdVgduU7kE67jMZWl96LHf9\nuww4U9LDwNNj7LMg0qhD1YbdM0ldKW9hYv4/W44n3eVrrGX9buWI+Fr7Akkf6SWhvq1z7xe59X+Y\niBga35xYv8gNb8+RSqv7kibtOjPK3dWqchr9oI6GzIrHfx2pZ85HSbc9bHWDXJ50L9JxbditqlvX\ny167Tju4mxWkMSYeAx4lzaf+zQWZRj/Jo0L3psI8OxWPvyPpvrOHkibJa3kKOD8iCt/RaSJpAdxr\nwMF9DB3/hEuQpgJ9uszgCFs0SFoFuKKXeUDqTGM81THPTk35WCci/jyex6yT0h2wXkkaiHUk865A\nniR1zZxTOk0H9+LyhGa7AdtFxCcnOj/Wf5TvwDXRaYyXql0pa8zHxV0WR0TsNO6ZqSiP2F0/In6l\ndGOgST1N6+DgXp6nDzBL6phnp6Z8bNP2cinSTVDmRMS4zsdelaRDgIOBKRGxnqQNgRN7qZbpx94y\nfaVtkASkxq+tmXeLNLNFXdWulLWIebd7bPmtpGvGMw81+SCwLXAlQET8Po9iLs3BfWztgyTmkG7X\nt/uE5casv/TFHDl5zqaWxUjTMi+M7WJ/j4i/a95NRybToH7ufaU1SMLMhqtp0q86XM/wQtj7Jiw3\nvbtE0qeBZSS9hXS3rvN7Sch17iOQdGREHCvphC6rg3S3+h9ExB/GOWtm1lCSJpF+lNrvjHVKLw3W\nDu4jkLRrRJwvaX9SMFfHJquQ7kq+UA2SMGsiSUuQbkn5RtL/6yXASRHxwoRmbAI5uFcg6f0RcdLY\nW5rZgpTnpJ8MnE4qiL2H1FvmoAnNWEGSfhIRe2r4/XGhxwZqB/cx5JbqI0gz5y2dFy+U/WfNmkrS\nzZ0BsNuyfiXpFRHxgKSPAVeR7tAFucagl7aNcb3LyELqTGAWaU73QVJDTWe3KzObWHMkrd96IWk9\nUsPqQiEiHshPlwdOJsWdXYHnem20dsl9DJKuj4it2ksBkq6NiG3G2tfMxoeknYHTmHfby3WAAyPi\nNxOXq95J2gLYC3gncK8HMS0YrcmQHpT0DtK87itPYH7MbLhVgM1It8PcnTS46vGJzFBFDwMPknrl\nrdpLAq6WGdsXJK0EfIw0b/UppOlFzax/HB0RT5CqNXYCvgmcOLFZKk/Sv0kaAn4NvAw4qNd2Awf3\nse1Fqr66JSIGgDcD43J/SDMrrHV/43cA34mIn5NmcV3YrA18JCI2iYgZVe637Dr3MXSbJMwTh5n1\nF0kXAPcBbwG2BP4GXLUoj0NxyX1sap+3Ij+fNIH5MbPh9iKN5nxrRDxOahdbqGaErJtL7mOQtB/w\naeDHpD6newL/ERFnTGjGzMxG4eBegKRNSY00AfymSj2Ymdl4cHA3M2sg17mbmTWQg7uZWQM5uJuZ\nNZCDu5lZAzm4m5k10P8H3yjyIyBKQJkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110bcf278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def plot_ingredient_histogram(ing=(0, logistic.coef_.shape[1] - 1)):\n",
    "    hist = logistic.coef_[:, ing]\n",
    "    plt.title(cv.get_feature_names()[ing] + \" std: {:.2f}\".format(np.std(hist)))\n",
    "    plt.bar(np.arange(20), hist)\n",
    "    plt.xticks(np.arange(20) + 0.5, enc.inverse_transform(np.arange(20)), rotation='90')\n",
    "    plt.ylim(-2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "775"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.get_feature_names().index('butter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M7XaXNFfS7ZKO6PV4ZmZWXJWS+43APsCvRttA0jTgy8DuwGbAmyS9sMIxzcysgOm97hgR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110b852b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ingredient_histogram(5311)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BY7w8prk3lKRvAFsCF9A1Wjci3lQijTWB55CuGP6YC6TbR8QZpfPTtOCuiUN5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\nW++IAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ba1c7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ingredient_histogram(4254)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EDh19klsiyvVzr5qXfYELWoUNSasAQxFxZok0DiXNYzIzIjbMhYbjI2KXhZLp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\ng7uZWQM5uJuZNdD/Bw4QApRFZUjcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bc62358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ingredient_histogram(775)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mauvaise classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_test_predict = logistic.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    5,    7, ..., 7949, 7950, 7952])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miss = np.where(y_test_predict != y_test)[0]\n",
    "miss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1x6815 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 4 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row = X_test[miss[0], :]\n",
    "row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5663, 4609, 5508, 6530], dtype=int32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nonzero(row)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['smoked pork neck bones', 'pinto beans', 'shortening', 'water']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[cv.get_feature_names()[i] for i in np.nonzero(row)[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 20)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic.predict_proba(row).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1777,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miss.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rice powder;rock sugar;on shrimp;shell;lard;egg whites;granulated sugar;ground black pepper;garlic\n"
     ]
    },
    {
     "data": {
      "image/png": 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ks/YJcoPpV4EFEXF3a3lE3Jb/3inp+6SqnUmBfXR0dPn/IyMjjIyMlP4AZmYPB2NjY4yN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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf802e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def plot_miss(idx=(0, miss.size - 1)):\n",
    "    row = X_test[miss[idx], :] \n",
    "    ingr = [cv.get_feature_names()[i] for i in np.nonzero(row)[1]]\n",
    "    print(\";\".join(ingr))\n",
    "    plt.bar(np.arange(20), logistic.predict_proba(row).ravel())\n",
    "    plt.xticks(np.arange(20) + 0.5, enc.inverse_transform(np.arange(20)), rotation='90');\n",
    "    plt.title(\"predicted = {}, true = {}\".format(enc.inverse_transform(y_test_predict[miss[idx]]), \n",
    "                                                 enc.inverse_transform(y_test[miss[idx]])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On compte les cas où il y a interversion des deux premiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "count = 0\n",
    "mixed_up_top_2 = []\n",
    "for idx in miss:\n",
    "    row = X_test[idx, :] \n",
    "    proba = [(p, cuisine) for p, cuisine in zip(logistic.predict_proba(row)[0, :], enc.classes_)]\n",
    "    proba.sort(key=lambda s: s[0], reverse=True)\n",
    "    true_label = y_test[idx]\n",
    "    predicted = enc.transform(proba[0][1])\n",
    "    second_predicted = enc.transform(proba[1][1])\n",
    "    if second_predicted == true_label:\n",
    "        count += 1\n",
    "        mixed_up_top_2.append(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "854"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mixed_up_top_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1777"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(miss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "On explore graphiquement les cas où il y a interversion du top 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seafood stock;bottled clam juice;dry vermouth;jumbo shrimp;puff pastry;red potato;heavy whipping cream;dried thyme;leeks;large garlic cloves;andouille sausage;purpose flour;all;butter\n"
     ]
    },
    {
     "data": {
      "image/png": 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5W+kfgNYXgcIPVyvNCmn8Gvgo6UrtaTmAVv0/Wvm4un1ehTQuzX8vJ/0Yi3SL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\ng7eZWQM5eJuZNdD/B3Had36g7InBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b9f8f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def plot_miss_mixed_up(idx=(0, len(mixed_up_top_2) - 1)):\n",
    "    new_idx = miss.tolist().index(mixed_up_top_2[idx])\n",
    "    plot_miss(new_idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prochaine chose à faire : entraîner des classifieurs pour les duels et voir dans combien de cas ces classifieurs arrivent à gagner le duel!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La documentation pour le one-vs-one est ici : [http://scikit-learn.org/stable/modules/multiclass.html#one-vs-one](http://scikit-learn.org/stable/modules/multiclass.html#one-vs-one)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.multiclass import OneVsOneClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ovo = OneVsOneClassifier(LogisticRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneVsOneClassifier(estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "          n_jobs=1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ovo.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dried red chile peppers;gingerroot;chinese cabbage;green onions;salt;garlic\n"
     ]
    },
    {
     "data": {
      "image/png": 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sdFp8L1+okL7zWT4saSNS7XDlimV4SNKypM74kyX9DnisYh5192lT5TiCBR24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\nLePAbmbWMg7sZmYt8/8BsoU0TTsscGYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b9943c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = 10\n",
    "new_idx = miss.tolist().index(mixed_up_top_2[idx])\n",
    "plot_miss(new_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ovo.predict(X_test[new_idx, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PDa3S/euxkPTl8bz80DLgH3Iba0k5zycd3/cCO0TBvDhNyAH6xaSJ2d4fEZeV\nNLHkoF3UcTlNOZ392n0FdkVUHB3aBElXkPq7rie1aXeavBqfN2UYZ9oARJo05XmkCd5L28meRsqf\n3Y30BlxEuowr8Z/5NkiHaK0BJdHQ6NDczPOxiNi/RjGbkiZjfzCXuYSUW7w76ZJ21qBNGp5be26G\nBrwsIj5GugpaCiDpHaQMhirqzhJIfs666wmeTPpw70s6Lg8kndz8ZUEdjiR1bD+X9Pm6QNK7o+Lc\nOJI2JZ0MdFZn+TZpKPcdVesA/AOpr+jiHLC3JI3urCQiQtKVknaOiMsKnrdXEzMW1rUgIv52FE80\nkjxtSU+JsiWIvkwaBdjJC96fdCm2b0EZ65ByR/+Q7y8gzTg4Y5uTpC9HxL7TXOZVamtTA6NDu8q6\nCHjhoG3IStPLPjMmz1FxXURsXbWvQQ3MzdCEfvUtbNOuPUugmllPsN8cKEWpoJI+Crw3VkwYtRmp\nTXuPitt/k9Ss0FlE9wDS7I+Vtm+KpFtJbdo/YvIVcUmb9pzPQinpQ6TXcCaT+zoaT/kbRvPImqQR\nY53Onk6+YqWJmnIZN0XENrM9NksZ3wVe1HWGuS4p4f05s2z35Ij4mWosWqo0IGba0aFRtnjq50hX\nHmcCv1lRjcr5uEeSzuDOyM+/dy7rn4BPR4UBBLnjbHugztwMA5P0atIX93NJH86OdYE/xAhn2FMD\n6wnmY/PvIuLCfH830tzxuxTWZU3SLJS3lmyXt23ii+MU+o+ULfmsL+73eJXP2Thpokm3qmE0j5xG\nOpt5CSlv8a8p6zgDuErSLhHxHaAzEKB0jog1omuUWEQ8oJQGOKPO2WSdgyYamHNE0mkRcSBpHb5/\nJs1ZXDxBT0R8QNLXScPwg5TjfUX+c9URX0tLn7dhl5AWbtiA9GXz2CAI0qoplUj6U9JAkMVMHhhT\n0nz3UP75G6XBV78gjRYt8TbgZKWBGJDmOf9KSQGqP03DL5QWwf0C6f18FWVzl0AazNIJVGuSRs4W\nXY1FxHJNndK06DhXM9NF1BIRi0f1XAMveTPdjRpLKZHa+a4nBflHSZcby/PvNxfW42Jgx677OwHf\nKdj+r0jtc/eTgsMDwP0Vtz0s/zy+z63SWoCkQT1PJq3Dtwh4Qvet6f22MtyA20hfgluQAvdiYHFh\nGd3rCd7NYOsJXkWazOjx+bY/hesJ5jIWMnkZuRsKtt+M1Dl9b759lXTWXuf9XaXkM5a3WZrrcVu+\nvwmpjbwbS1Q3AAAI/UlEQVSkjEtJ/QKr5dtfV405DR9fzyCtuvOazm0YzzOMM+06SyntPcPfBpnS\n9EuSOktrbUzquKnqWGCviCi9SoCU/nMsaT6D++i5fKxYxidJaVhbMPUqYygjrXppTBYwaLAe90bN\nds6I+ED+9T8kfY10RVc62dMrSGfWnSaf15CWqSrxcKSl+Lofe3S6f+6WO7g/FBEzfd4GsRXly6Y1\nMaXpmhHRPRLz85KKliesKzeJ7k5qFj6blIJ5EStG4zZmGEH700qjo95Pajtdh3R2MqtosB0rIi6X\n9DRg6/zQpPkdJO0RM0/octeAARtSL/aTScN8JxggaEfEx4GPS/pkRLx5wHrUEmMyb3KD9ThK0knA\nN5mcS1syp82apEmGdiPtywslnRAVR9rmJ/xhbqc/g3Q1+ZKI+M0sm/UaeJqGSOMYNlPBCMpeSiOX\nH6Gr85B05XF4YVFNTGlae7qIBrwC2I60YPJBkjYkdfQ2rtGOSDU0BH0UpsucUBoQAymHdiPSB6vo\nAy7p7aQRZlswtY0vomAEnjVH0umkL/Eb6TorjbK8+YEzm/pkJD2JNFnS7ynPmFib1D7fO01D1WHs\np5FGuA7awd1UjvXfkbJHXgz8P9KJzhfySUvVMpZTc7qIuiRdHhF/qjTVwgtIx8gtEbH1LJsWa/RM\nO39jHsaK+YrbaG9WHAC/YcWHgvz4rEF7HM6Sra+dgD+JemcqT4/JWUzfklR1StMmmyO2ybdV820f\nUnt91cD/g3zrdHCXjmNoJMc6Io5TmtL0AVLzypGzXAH3cxgpg+d+Sf+X1Nzywag431FDLldasu0z\nwBWkK5DKE9SVGEbK39GkXuiBhqCPymw5yjnV7h2xYg29RaQ19Ea61qU1J6eo/VNE3FijjM8Dn4jJ\nmU1vjZTpMzJKE5q9hzRTYPdVw/LCctbN2xXPsdNEjnUTutIwdwM+SMowOjJqTuxVoz6bkxb1Hcok\nWMMI2ssZUb5iHRWCdr9Z0IoWgbXxkgcabUm9yYluIZ0R/oR0nD+FNN/OI6Vl1dHpnK2x/bakTrLO\nCjr3Aq+NiMrTxTaRY52bI48mre/Y6fsp6uTufC7zCeP1EXH6bJ/vYcgpoJuRrnw6YzJKpnKuZBgd\nkU0MQR+F22f5u1RzDT0bO3uOSRlNqNup+mngbyPifACllYU+Dcw4+KxbQ4kDdbK0On6qNMXrHsDR\nSjNSrtJA3SpTM/PvV3uuIZxp1x6C3mBddmXqQIpKKTiSXkNaLXvSGnpVt7fxpTSVZ/e8IcWzzDVR\nRh11O1WbGBHZhLpXDLmMtUlfptdFmvNoY9Jc+Oc0Uslqdag9/37l5xpC0K49BL2hetSemU4119Cz\n8ZJHEX6YNGjpHtKl7M1RcUGJpspoQm5PHrhTVdIZpNzo00gnJQeQBqO9vLlazvj8tbO0xolGOP/+\nMJpHmhiC3oTaM9PlDquBO61s7HyQNNT53EjTBz+fshXMmyqjCZeQskcGPT4PJo1G7MyEeWF+bFS6\ns7QeYnKWFpQtLTgOBp5/v1RjQbsrB3VV4GJJvR01o3YDaRTknM5MZ2Pl4Yj4uaRVJC2IiPMlVZ3W\ntckymrALKUgM2qm6JWlWvFVIfTUvJF1VjqQjNfK0xZJ2i4iLuv+Ws0Da5quknPeBpoIu0eSZdpND\n0JuwAXCT0hqLI5+ZzsbSfTnF7ULgdEn3AA/Oss0wymhC3Q7R00kpg9czN5/Pjo+TVkWa7bFxt35E\nfLT7AUnvHMYTjWQ+7bmQe8OniIhlo62JjYvcYfUQ6ezyANJkTadHxVWAmipjHDTRAVjz+XchZaq8\ni7T8Wyfdb13SWosj7RCtq1+K4bBShOdt0Dbr0CwTTgH/Q5rP+hPDLGOc5FGIr6TGPCw1n3930rqa\nh5AmR+t4ADgrIiqvgDOXNAdzvc/boN3z4VqdNGXjgyVJ+7ZykPQE4JI680Q0UcYoNTEPS0P12Cwi\nfjTK52yS0opBm5MGCB3OiiuG+0kpiI80/pzzNWh3yxNZvRR4dkQcMdf1sfGjvGLRXJcxKnVTBhus\nx/l9Ho6IeMHIK1NTHiH61Ij4ptKCKwuGkQK4UgTtDg9DN0uamIeloXp0r5i+BmlxiUciYqTzYdcl\n6U3AG4FFEbGlpK2AE4bRPDK01djnWlfyPqROox1ZsVSU2cqubspgI2LF0ncdF0m6fJR1aMhbgZ2B\n7wJExG151Gzj5m3QZnLy/iOkZcv2mbPamI2XsZhDJc/p07EKafrcNvY7/S4ifqcVizmsSgvytMdK\nJ3nfzKZqaLKnJlzF1JOr189ZbQZ3gaT3AWtJ2oO0utFZw3iiedemLenwiDhG0vF9/hyk1bM/HxE/\nGHHVzGyekrSA9GXTvZLQicPo6J2PQXvviDhL0utIQVo9//IE0irJrUreN5uPJK1OWprveaTP6wXA\nJyPi4Tmt2Bibd0G7CklvjohPzv6fZjZMeU7wVYFTSSdYB5KyR94wpxWrSNKXI2JfTV3/E4bUsTtv\ng3buuT2MNBPamvnhVuZ/ms1Xkq7rDWz9HhtXkjaOiDslvRu4lLSiEeQr/GH0HYx0dYcROx24hTSn\n9lJSB0dvepGZza1HJD21c0fSlqQOyVaIiDvzr+sCnyLFnb2Bh4bV2Tufz7SviohndX9rS7oiInaa\nbVszGw1JLwROYcXyf5sBB0fEt+auVoOTtB2wH/AK4A4PrinTmQTnLkl7kebVXn8O62NmUz0B2Ja0\nLOA+pEE/v5zLCtV0D3AXKUttg2E8wXxuHvmgpIXAu0nzBp9ImgbSzMbHkRHxK1LzwguATzCeC4HP\nSNLfSFoGnAc8EXjDsNrl53PQ3o/U/HN9REwALwJGsv6dmVXWWb91L+AzEfE10qycbbMp8M6I2CYi\nlgxzPdn53KY9ZXIoTxhlNl4knQ38FNgD2AH4LXCpx1FMbz6faat7XoP8+4I5rI+ZTbUfafTgiyPi\nl6R+p1bN8Ddq8/lM+zXA+4AvkXIm9wX+MSI+N6cVMzOrYd4GbQBJTyd1bgTwrWG2M5mZjcK8Dtpm\nZvPNfG7TNjObdxy0zcxaxEHbzKxFHLTNzFrEQdvMrEX+F1zPQ2XNS7d4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd95eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(ovo.classes_, ovo.decision_function(X_test[new_idx, :]).T)\n",
    "plt.xticks(ovo.classes_, enc.inverse_transform(ovo.classes_), rotation=90);"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "self.estimators_ = Parallel(n_jobs=self.n_jobs)(\n",
    "            delayed(_fit_ovo_binary)(\n",
    "                self.estimator, X, y, self.classes_[i], self.classes_[j])\n",
    "            for i in range(n_classes) for j in range(i + 1, n_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 9), (0, 10), (0, 11), (0, 12), (0, 13), (0, 14), (0, 15), (0, 16), (0, 17), (0, 18), (0, 19), (1, 2), (1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (1, 11), (1, 12), (1, 13), (1, 14), (1, 15), (1, 16), (1, 17), (1, 18), (1, 19), (2, 3), (2, 4), (2, 5), (2, 6), (2, 7), (2, 8), (2, 9), (2, 10), (2, 11), (2, 12), (2, 13), (2, 14), (2, 15), (2, 16), (2, 17), (2, 18), (2, 19), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8), (3, 9), (3, 10), (3, 11), (3, 12), (3, 13), (3, 14), (3, 15), (3, 16), (3, 17), (3, 18), (3, 19), (4, 5), (4, 6), (4, 7), (4, 8), (4, 9), (4, 10), (4, 11), (4, 12), (4, 13), (4, 14), (4, 15), (4, 16), (4, 17), (4, 18), (4, 19), (5, 6), (5, 7), (5, 8), (5, 9), (5, 10), (5, 11), (5, 12), (5, 13), (5, 14), (5, 15), (5, 16), (5, 17), (5, 18), (5, 19), (6, 7), (6, 8), (6, 9), (6, 10), (6, 11), (6, 12), (6, 13), (6, 14), (6, 15), (6, 16), (6, 17), (6, 18), (6, 19), (7, 8), (7, 9), (7, 10), (7, 11), (7, 12), (7, 13), (7, 14), (7, 15), (7, 16), (7, 17), (7, 18), (7, 19), (8, 9), (8, 10), (8, 11), (8, 12), (8, 13), (8, 14), (8, 15), (8, 16), (8, 17), (8, 18), (8, 19), (9, 10), (9, 11), (9, 12), (9, 13), (9, 14), (9, 15), (9, 16), (9, 17), (9, 18), (9, 19), (10, 11), (10, 12), (10, 13), (10, 14), (10, 15), (10, 16), (10, 17), (10, 18), (10, 19), (11, 12), (11, 13), (11, 14), (11, 15), (11, 16), (11, 17), (11, 18), (11, 19), (12, 13), (12, 14), (12, 15), (12, 16), (12, 17), (12, 18), (12, 19), (13, 14), (13, 15), (13, 16), (13, 17), (13, 18), (13, 19), (14, 15), (14, 16), (14, 17), (14, 18), (14, 19), (15, 16), (15, 17), (15, 18), (15, 19), (16, 17), (16, 18), (16, 19), (17, 18), (17, 19), (18, 19)]\n"
     ]
    }
   ],
   "source": [
    "binary_estimators = []\n",
    "n_classes = ovo.classes_.shape[0]\n",
    "for i in range(n_classes): \n",
    "    for j in range(i + 1, n_classes):\n",
    "        binary_estimators.append((ovo.classes_[i], ovo.classes_[j]))\n",
    "print(binary_estimators)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut maintenant revenir aux classes initiales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seafood stock;bottled clam juice;dry vermouth;jumbo shrimp;puff pastry;red potato;heavy whipping cream;dried thyme;leeks;large garlic cloves;andouille sausage;purpose flour;all;butter\n"
     ]
    },
    {
     "data": {
      "image/png": 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UTaW4ctM6AbxS0pWSfidp/SlLnZnZgKp7Poduxs29DFg1Ih6X9EbgNOCFk5ssM7PBVndw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\nMzMrcXAwM7MSBwczMytxcDAzs5L/D1i3WoXMIpGGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be78cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def two_step_strategy(idx=(0, len(mixed_up_top_2) - 1)):\n",
    "    # find index in test set\n",
    "    new_idx = miss.tolist().index(mixed_up_top_2[idx])\n",
    "    # compute probas\n",
    "    row = X_test[miss[new_idx], :] \n",
    "    proba = [(p, cuisine) for p, cuisine in zip(logistic.predict_proba(row)[0, :], \n",
    "                                               enc.transform(enc.classes_))]\n",
    "    proba.sort(key=lambda s: s[0], reverse=True)\n",
    "    # extract predictions\n",
    "    true_label = y_test[new_idx]\n",
    "    predicted = proba[0][1]\n",
    "    second_predicted = proba[1][1]\n",
    "    # showdown\n",
    "    pair = (min(predicted, second_predicted),\n",
    "            max(predicted, second_predicted))\n",
    "    binary_classifier = ovo.estimators_[binary_estimators.index(pair)]\n",
    "    binary_proba = binary_classifier.predict_proba(row)\n",
    "    # output\n",
    "    ingr = [cv.get_feature_names()[i] for i in np.nonzero(row)[1]]\n",
    "    print(\";\".join(ingr))\n",
    "    plt.subplot(121)\n",
    "    plt.bar(np.arange(20), logistic.predict_proba(row).ravel())\n",
    "    plt.xticks(np.arange(20) + 0.5, enc.inverse_transform(np.arange(20)), rotation='90');\n",
    "    plt.title(\"predicted = {}, true = {}\".format(enc.inverse_transform(y_test_predict[miss[new_idx]]), \n",
    "                                                 enc.inverse_transform(y_test[miss[new_idx]])))\n",
    "\n",
    "    plt.subplot(122)\n",
    "    plt.bar(np.arange(2), binary_proba.ravel())\n",
    "    plt.xticks(np.arange(2) + 0.4, enc.inverse_transform(np.array(pair)), rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "(16, 5) is not in list",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-6b14cf76da7f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0movo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbinary_estimators\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msecond_predicted\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: (16, 5) is not in list"
     ]
    }
   ],
   "source": [
    "ovo.estimators_[binary_estimators.index((second_predicted, predicted))].predict(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut essayer de quantifier le gain possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class TwoStageClassifier():\n",
    "    def __init__(self):\n",
    "        self.logistic = LogisticRegression()\n",
    "        self.ovo = OneVsOneClassifier(LogisticRegression())\n",
    "    \n",
    "    def fit(self, X_train, y_train):\n",
    "        self.logistic.fit(X_train, y_train)\n",
    "        self.ovo.fit(X_train, y_train)\n",
    "        \n",
    "    def bookkeeping(self):\n",
    "        binary_estimators = []\n",
    "        n_classes = self.ovo.classes_.shape[0]\n",
    "        for i in range(n_classes): \n",
    "            for j in range(i + 1, n_classes):\n",
    "                binary_estimators.append((ovo.classes_[i], ovo.classes_[j]))\n",
    "        self.binary_estimators = binary_estimators\n",
    "        \n",
    "    def predict(self, row, thresh):\n",
    "        \"\"\"two stage prediction: logistic first, then binary classifiers between top 2\"\"\"\n",
    "        proba = [(p, cuisine) for p, cuisine in zip(self.logistic.predict_proba(row)[0, :], \n",
    "                                               enc.transform(enc.classes_))]\n",
    "        proba.sort(key=lambda s: s[0], reverse=True)\n",
    "        # extract predictions\n",
    "\n",
    "        predicted_label = proba[0][1]\n",
    "        predicted_label_prob = proba[0][0]\n",
    "        second_predicted_label = proba[1][1]\n",
    "        second_predicted_prob = proba[1][0]\n",
    "        # showdown\n",
    "        pair = (min(predicted_label, second_predicted_label),\n",
    "                max(predicted_label, second_predicted_label))\n",
    "        binary_classifier = self.ovo.estimators_[self.binary_estimators.index(pair)]\n",
    "        #binary_proba = binary_classifier.predict_proba(row)\n",
    "        if predicted_label_prob > thresh:\n",
    "            return np.array(predicted_label)\n",
    "        else:\n",
    "            return np.array(pair[binary_classifier.predict(row)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tsc = TwoStageClassifier()\n",
    "tsc.fit(X_train, y_train)\n",
    "tsc.bookkeeping()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for thresh in [0.3, 0.4, 0.5, 0.6, 0.7, 0.8]:\n",
    "    y_pred = np.array([])\n",
    "    for row, true_label in zip(X_test, y_test):\n",
    "        y_pred = np.append(y_pred, tsc.predict(row, thresh))\n",
    "    print('========= thresh {} =========='.format(thresh))\n",
    "    print(classification_report(y_test, y_pred, target_names=enc.classes_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut regarder les vraies probas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "probs = np.zeros((y_test.size, 2))\n",
    "for ind, row, true_label in zip(np.arange(X_test.size), X_test, y_test):\n",
    "    proba = [(p, cuisine) for p, cuisine in zip(tsc.logistic.predict_proba(row)[0, :], \n",
    "                                           enc.transform(enc.classes_))]\n",
    "    proba.sort(key=lambda s: s[0], reverse=True)\n",
    "    # extract predictions\n",
    "    predicted = proba[0][0]\n",
    "    second_predicted = proba[1][0]\n",
    "    probs[ind, :] = np.array([predicted, second_predicted])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "probs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(probs, columns=['first_prob', 'second_prob'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(121)\n",
    "df.first_prob.hist(bins=50)\n",
    "plt.subplot(122)\n",
    "df.second_prob.hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Et si on regardait plutôt le delta entre les deux probas ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "(df.first_prob - df.second_prob).hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "6 * 44100 / 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "6 * 44100 / 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "44100 / 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "4 * 44100 / 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "4 * 44100 / 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "4 * 44100 / 1601"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "4 * 44100 / 2001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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