MDI343 - Data Challenge - Camille COCHENER.ipynb

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    "<div align='center'><h2>Challenge Large Scale Machine Learning</h2></div>\n",
    "<div align='center'>Theme: fusion of algorithms for face recognition<h4></h4></div>\n",
    "\n"
   ]
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    "### **1. Introduction**\n",
    "\n",
    "<b><i>Context</i></b>\n",
    "<div align='justify'>Facial recognition is the process of identifying or verifying the identity of a person using their face. Today, it’s considered to be the most natural of all biometric measurements. Biometric means identity verification of persons according to their physical and behavioral characteristics [1]. In the case of facial biometrics, a sensor captures a face, then transforms it into a vector of features and compares this transformed image to those held in a database. The biometric system confirms the correct match. </div>\n",
    "\n",
    "<div align='justify'>The increasingly ubiquitous presence of biometric solutions and face recognition in particular in everyday life requires their adaptation for practical scenario. In the presence of several possible solutions, and if global decisions are to be made, each such single solution can be far less efficient than tailoring them to the complexity of an image. </div>\n",
    "\n",
    "<b><i>Objective</i></b>\n",
    "<div align='justify'>The goal is to build a fusion of algorithms in order to construct the best suited solution for comparison of a pair of images. This fusion will be driven by qualities computed on each image.</div>\n",
    "\n",
    "<b><i>Technical environment</i></b>\n",
    "\n",
    "<div align='justify'>The analyses were performed on the Google Collaboratory platform (GPU, 25GB RAM) with Python 3.6.9.</div>"
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   "source": [
    "# To get 25 Go RAM on Google Collaboratory\n",
    "#a = []\n",
    "#while(1):\n",
    " #  a.append('1')"
   ]
  },
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     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "# Drive connection\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
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     "text": [
      "Requirement already satisfied: catboost in /usr/local/lib/python3.6/dist-packages (0.21)\n",
      "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from catboost) (4.4.1)\n",
      "Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1)\n",
      "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.17.5)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from catboost) (1.4.1)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.12.0)\n",
      "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (0.25.3)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from catboost) (3.1.3)\n",
      "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->catboost) (1.3.3)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2.6.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.4.6)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (1.1.0)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->catboost) (45.1.0)\n"
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   ],
   "source": [
    "!pip install catboost"
   ]
  },
  {
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   "source": [
    "# Library importation\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "import random\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from platform import python_version\n",
    "import math\n",
    "#from catboost import CatBoostClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn import preprocessing \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier"
   ]
  },
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    "colab_type": "code",
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     "text": [
      "3.6.9\n"
     ]
    }
   ],
   "source": [
    "# Python version\n",
    "print(python_version())"
   ]
  },
  {
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    "### Summary of the analysis\n",
    "\n",
    "My analyses took place in several stages.\n",
    "\n",
    "***--- Data exploration***\n",
    "\n",
    "First, I explored the data. I checked that there were no <b>missing values</b>. If there were, an imputation technique for missing values should have been implemented because the algorithms do not handle the presence of missing values well.  \n",
    "Next, since one line represented the comparison between two images, and we had groups of variables specific to each of the two images, it seemed interesting to me to <b>assign identifiers to the images</b> in order to understand the structure of the dataset and to check whether some images in the training dataset were common to the images in the test dataset. In the end, it turned out that very few images were common to both datasets. Only the different quality and match scores would be useful in learning the model.   \n",
    "Then, the different variables in the dataset were analysed in a descriptive way. The <b>response variable</b>, a binary variable indicating whether the two images match or not, was very <b>unbalanced</b> (about 6% of the images match). It was therefore relevant to choose an appropriate metric for this case, such as TPR (also called Recall or Sensitivity).  \n",
    "In addition, each <b>explanatory variable</b> was analyzed, again in relation to the response variable, in order to determine which ones could characterize certain classes. It appeared that quality scores did not seem to play an obvious role in the discrimination of the two classes, but that matching scores clearly had a different distribution depending on whether or not the images matched.   \n",
    "Finally, <b>correlation analysis</b> showed that quality scores were not correlated with each other. On the other hand, the match scores were highly correlated with each other when the images were matched. The correlation structure of the matching scores depended on the class. \n",
    "    \n",
    "In conclusion, little preparation or data transformation was required. \n",
    "Once I understood the dataset, I was able to start building a classifier.\n",
    "\n",
    "**--- Modeling**\n",
    "\n",
    "In the case of unbalanced data, <b>ensemble methods</b> can often give good results because they avoid overfitting and solve often complicated problems. <br>\n",
    "In this category of algorithms, I have chosen boosted based techniques because they are simple to use, provide models that are often very generalizable and are not subject to overfitting. These methods are not very sensitive to noise and outliers. <br>\n",
    "I chose to use two powerful algorithms: the <b>XGBoost</b> and the <b>CatBoost algorithm</b>. The best performance was obtained with XGBoost.\n",
    " \n",
    "  \n",
    "***What is XGBoost and CatBoost?***\n",
    "  \n",
    "XGBoost is a scalable machine learning system for tree boosting. It has been recognized in a number of machine learning and data mining challenges, on a wide range of problems. The most important factor behind the success of XGBoost is its scalability in all scenarios due to several important systems and algorithm optimizations from gradient boosting. It is an ensemble of decision trees algorithm where new trees fix errors of those trees that are already part of the model. Trees are added until no further improvements can be made to the model.<br>\n",
    "All the mathematical details is in this <a href=\"https://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf\">paper</a>.   \n",
    "  \n",
    "But more recently (2018), an other algorithm called CatBoost (an open-source library) got a lot of attention. As all existing implementations of gradient boosting face a statistical issue, a <b>prediction shift</b> of the learned model caused by a special kind of target leakage, CatBoost brings a solution to this problem, the <b>ordered boosting</b>, which is a modification of the standard gradient boosting algorithm avoiding target leakage. It also allows to process categorical features.    \n",
    "This algorithm <b>outperforms the existing state-of-the-art implementations of gradient boosted decision trees</b> like XGBoost and LightGBM on multiple machine learning tasks. <br>\n",
    "For mathematical details, please refer to the following paper <a href=\"https://arxiv.org/abs/1706.09516\">here</a>.\n",
    "\n",
    "  \n",
    "***XGBoost training***\n",
    "  \n",
    "For training, I chose to split the Xtrain dataset into two parts: a training dataset and a validation dataset. <br>\n",
    "In order to avoid biasing the generalization error, I used a cross-validation. Nevertheless, the results on the different folds were very stable (more or less $10^{-4}$). Considering the time taken by the cross-validation, I considered that a simple cut was sufficient and gave a good estimate of the generalization error. <br>\n",
    "I have been careful to <b>keep the proportions of each class</b> in each of the training and validation parts.\n",
    "  \n",
    "***Hyperparameters choice***\n",
    "  \n",
    "Building a grid search being too computationally intensive on this dataset, I set the model parameters by hand. First, I left the number of iterations at 1000 and the learning rate at 0.1, then I varied the main parameters such as the depth of the tree, the regularization parameter, the number of sample and features to build a tree. Once an optimum was found, I started to decrease the learning rate and to increase the number of iterations in order to obtain the best possible quality.\n",
    "<br>\n",
    "\n",
    "***Evaluation***\n",
    "\n",
    "The evaluation metric is the best true positive rate when the false positive rate is lower than $10^{-4}$. \n",
    "With my algorithm, I got a TPR of 0.9873114924596984 on the validation set, and a TPR of 0.878006370505 on the test set.   \n",
    "The difference between the two scores is 0.10762771. I suppose this is due to the fact that part of the distributions of the positive and negative classes in the test set is not captured or is not present in the training set.\n"
   ]
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   "source": [
    "### **2. Presentation of the data**\n"
   ]
  },
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   "cell_type": "markdown",
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    "id": "VVLSdSe1Kfrf"
   },
   "source": [
    "<div align='justify'>First, the data was imported and stored in pandas DataFrames.</div>"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Load training data\n",
    "Xtrain = pd.read_csv('/content/drive/My Drive/Camille/data/xtrain_challenge.csv', delimiter=',', header=0)\n",
    "Xtest = pd.read_csv('/content/drive/My Drive/Camille/data/xtest_challenge.csv', delimiter=',', header=0)\n",
    "Ytrain = pd.read_csv('/content/drive/My Drive/Camille/data/ytrain_challenge.csv', delimiter=',', header=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<div align='justify'>The training set (Xtrain + Ytrain) is the set on which we will build the model while the test set (Xtest) is the data that the model will never have seen and that we want to predict. </div>"
   ]
  },
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     "timestamp": 1582909337466,
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the train set: (9800713, 37)\n",
      "Shape of the test set: (3768311, 37)\n"
     ]
    }
   ],
   "source": [
    "# Number of observations for the train and test set\n",
    "print(\"Shape of the train set: {}\".format(Xtrain.shape))\n",
    "print(\"Shape of the test set: {}\".format(Xtest.shape))"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "<div align='justify'>The training set includes 9.800.713 observations and 37 variables explicatives. The variables are as follows: </div>\n",
    "<ul>\n",
    "<li><font size=2pt>13 variables relating to the quality of the first image (qs1 to qs13)</li>\n",
    "<li>13 variables relating to the quality of the second image (qr1 to qr13)</li>\n",
    "<li>11 matching scores between the two images (s1 to s11)</font></li>\n",
    "</ul>\n",
    "\n",
    "<div align='justify'>The match score is a measure of similarity between the first and second biometric feature vectors. When match scores output by different biometric matchers are consolidated in order to arrive at a final recognition decision, fusion is said to be done at the match score level.</div>\n",
    "<br>\n",
    "<div align='justify'>\n",
    "The variable to be explained is a binary variable and is defined as follows: \n",
    "<br>\n",
    "<br>\n",
    "$$f(x)= \\left\\{\n",
    "    \\begin{array}{ll}\n",
    "        1 & \\mbox{if the pair of images belongs to the same person} \\\\\n",
    "        0 & \\mbox{otherwise.}\n",
    "    \\end{array}\n",
    "\\right.$$\n",
    "</div>\n",
    "\n",
    "<br>\n",
    "The test set includes 3.768.311 predicted observations which is one-third of the training set.\n",
    "<br>\n",
    "<br>\n",
    "<div align='justify'>For the rest of the analyses, it is assumed that the learning sample and the test sample are from the same population and have the same distribution for all variables.</div>"
   ]
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   "source": [
    "<div align='justify'>For some exploratory analyses, we will combine the training data set and the test set.</div> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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    "colab_type": "code",
    "id": "T3pJLh_MnLEO"
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   "outputs": [],
   "source": [
    "# Combine the train and test set\n",
    "combined = pd.concat([Xtrain, Xtest], sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 86749,
     "status": "ok",
     "timestamp": 1582909338617,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the combined set: (13569024, 37)\n"
     ]
    }
   ],
   "source": [
    "# Number of observations for the combined set\n",
    "print(\"Shape of the combined set: {}\".format(combined.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<div align='justify'>The combined data set includes 13.569.024 observations.</div>"
   ]
  },
  {
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   "execution_count": 10,
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    "colab_type": "code",
    "executionInfo": {
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       "      <td>2.59</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.03</td>\n",
       "      <td>276.02</td>\n",
       "      <td>58.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2405.94</td>\n",
       "      <td>1981.09</td>\n",
       "      <td>2677.84</td>\n",
       "      <td>2470.44</td>\n",
       "      <td>1579.39</td>\n",
       "      <td>2185.79</td>\n",
       "      <td>2118.77</td>\n",
       "      <td>2580.99</td>\n",
       "      <td>2498.04</td>\n",
       "      <td>3180.58</td>\n",
       "      <td>2718.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>-4.31</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.76</td>\n",
       "      <td>347.06</td>\n",
       "      <td>28.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-18.97</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.34</td>\n",
       "      <td>263.84</td>\n",
       "      <td>30.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3241.37</td>\n",
       "      <td>2015.24</td>\n",
       "      <td>3987.19</td>\n",
       "      <td>3343.53</td>\n",
       "      <td>2894.69</td>\n",
       "      <td>2947.34</td>\n",
       "      <td>2689.42</td>\n",
       "      <td>3763.51</td>\n",
       "      <td>2544.22</td>\n",
       "      <td>3515.58</td>\n",
       "      <td>3247.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.76</td>\n",
       "      <td>-19.29</td>\n",
       "      <td>2.71</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.53</td>\n",
       "      <td>135.94</td>\n",
       "      <td>57.65</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-12.58</td>\n",
       "      <td>-9.99</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.60</td>\n",
       "      <td>357.67</td>\n",
       "      <td>51.98</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2199.22</td>\n",
       "      <td>2549.59</td>\n",
       "      <td>2164.41</td>\n",
       "      <td>2100.69</td>\n",
       "      <td>1294.35</td>\n",
       "      <td>2136.04</td>\n",
       "      <td>1644.05</td>\n",
       "      <td>2308.05</td>\n",
       "      <td>1774.88</td>\n",
       "      <td>3005.79</td>\n",
       "      <td>2507.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>-8.04</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.09</td>\n",
       "      <td>288.07</td>\n",
       "      <td>21.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.57</td>\n",
       "      <td>-13.22</td>\n",
       "      <td>-2.66</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.38</td>\n",
       "      <td>268.44</td>\n",
       "      <td>24.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2390.40</td>\n",
       "      <td>2141.75</td>\n",
       "      <td>2086.02</td>\n",
       "      <td>2154.69</td>\n",
       "      <td>1247.48</td>\n",
       "      <td>1903.36</td>\n",
       "      <td>2232.54</td>\n",
       "      <td>2146.34</td>\n",
       "      <td>2201.53</td>\n",
       "      <td>3315.03</td>\n",
       "      <td>2274.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.56</td>\n",
       "      <td>-10.61</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.10</td>\n",
       "      <td>166.12</td>\n",
       "      <td>37.36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.03</td>\n",
       "      <td>-15.53</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.25</td>\n",
       "      <td>172.19</td>\n",
       "      <td>26.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1822.14</td>\n",
       "      <td>2202.60</td>\n",
       "      <td>2150.07</td>\n",
       "      <td>2194.25</td>\n",
       "      <td>1575.43</td>\n",
       "      <td>2345.59</td>\n",
       "      <td>1976.82</td>\n",
       "      <td>1440.78</td>\n",
       "      <td>2061.06</td>\n",
       "      <td>2927.96</td>\n",
       "      <td>2073.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   qs1  qs2  qs3    qs4    qs5  ...       s7       s8       s9      s10      s11\n",
       "0  1.0  0.0  0.0  -6.24  -5.27  ...  2118.77  2580.99  2498.04  3180.58  2718.29\n",
       "1  1.0  0.0  0.0  -0.42  -4.50  ...  2689.42  3763.51  2544.22  3515.58  3247.49\n",
       "2  1.0  0.0  0.0  33.76 -19.29  ...  1644.05  2308.05  1774.88  3005.79  2507.02\n",
       "3  1.0  0.0  0.0  -2.82  -8.04  ...  2232.54  2146.34  2201.53  3315.03  2274.76\n",
       "4  1.0  0.0  0.0   3.56 -10.61  ...  1976.82  1440.78  2061.06  2927.96  2073.16\n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7NvVg6dBdkw_"
   },
   "source": [
    "### **3. Data exploration**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "REQtFeLgTBmY"
   },
   "source": [
    "<div align='justify'>In this part, we will analyze the dataset in an exploratory way in order to better understand it and to apprehend its specificities. In particular, we will verify the presence of missing values and outliers, which can have a strong impact on predictive models and statistical tests. We will also analyze the distributions of each variable and their relationship with the response variable. </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JgvFRRyknWue"
   },
   "source": [
    "#### 3.1 Missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Iv8DRxwmn8oa"
   },
   "source": [
    "<div align='justify'>The following code is used to check whether or not there are values in the dataset.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 87674,
     "status": "ok",
     "timestamp": 1582909339548,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "y7uM_gkqoV6A",
    "outputId": "c6333b03-1f82-49a9-a041-98004d73cb9b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No missing values\n"
     ]
    }
   ],
   "source": [
    "missing_values = pd.DataFrame(combined.apply(lambda x: x.isna().sum()))\n",
    "missing_values = missing_values.rename(columns={0: \"Number of missing values\"})\n",
    "where_na       = missing_values.iloc[np.where(missing_values[\"Number of missing values\"] != 0)]\n",
    "\n",
    "if where_na.shape[0] == 0:\n",
    "  print(\"No missing values\")\n",
    "else:\n",
    "  where_na"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CUEvDDHAn_za"
   },
   "source": [
    "<div align='justify'>There are no missing values in the dataset. </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rSno3SOl_E2g"
   },
   "source": [
    "#### 3.2 Image encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zQgPBOfK_NTj"
   },
   "source": [
    "<div align='justify'>Since each row represents the comparison between two images, we imagine that an image is associated with only one set of quality scores. It is therefore possible to identify the images in this data set.</div> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 91855,
     "status": "ok",
     "timestamp": 1582909343732,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "XOavnzDM_MAV",
    "outputId": "cbb45142-b045-4ede-fc2f-32d1c2a81015"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(213195, 13)"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of distinct rows for the first group of quality scores\n",
    "distinct_first_qs = combined.loc[:,\"qs1\":\"qs13\"].drop_duplicates(inplace=False)\n",
    "distinct_first_qs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J2KqQs06DgpY"
   },
   "source": [
    "<div align='justify'>There are 213.195 distinct images in the first set of quality scores. Those images are compared to the images of the second set of quality scores : </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 96969,
     "status": "ok",
     "timestamp": 1582909348849,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "wEKq1F3RDLgU",
    "outputId": "dc165615-3bf7-4b3f-d14e-2053b0429715"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12726635, 13)"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the numer of distinct rows in the second group of quality scores\n",
    "distinct_sec_qs = combined.loc[:,\"qr1\":\"qr13\"].drop_duplicates(inplace=False)\n",
    "distinct_sec_qs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pUfKdF7wD86B"
   },
   "source": [
    "<div align='justify'>There is a lot of images in the second group of quality scores (12.726.635). To make it clearer, we can create image ids.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9M88fc-BJFXB"
   },
   "outputs": [],
   "source": [
    "# Create an object containing all the scores in a concatanated form for the first group of quality scores\n",
    "score_1 = (combined['qs1'].astype(str)+'_'+combined['qs2'].astype(str)+ \n",
    "          '_'+combined['qs3'].astype(str)+'_'+combined['qs4'].astype(str)+'_'+combined['qs5'].astype(str)+\n",
    "          '_'+combined['qs6'].astype(str)+'_'+combined['qs7'].astype(str)+'_'+combined['qs8'].astype(str)+\n",
    "          '_'+combined['qs9'].astype(str)+'_'+combined['qs10'].astype(str) +'_'+combined['qs11'].astype(str)+\n",
    "          '_'+combined['qs12'].astype(str)+'_'+combined['qs13'].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "emcNFNagKBun"
   },
   "outputs": [],
   "source": [
    "# Create an object containing all the scores in a concatanated form for the second group of quality scores\n",
    "score_2 = (combined['qr1'].astype(str)+'_'+combined['qr2'].astype(str)+ \n",
    "          '_'+combined['qr3'].astype(str)+'_'+combined['qr4'].astype(str)+'_'+combined['qr5'].astype(str)+\n",
    "          '_'+combined['qr6'].astype(str)+'_'+combined['qr7'].astype(str)+'_'+combined['qr8'].astype(str)+\n",
    "          '_'+combined['qr9'].astype(str)+'_'+combined['qr10'].astype(str) +'_'+combined['qr11'].astype(str)+\n",
    "          '_'+combined['qr12'].astype(str)+'_'+combined['qr13'].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 292581,
     "status": "ok",
     "timestamp": 1582909544480,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "PnSNoJFxLDJV",
    "outputId": "7a5ebd74-7bae-442a-8cab-087cb2fbb91f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12936517,)"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the total number of distinct images through both columns\n",
    "df_scores = pd.DataFrame({\"score1\": score_1, \"score2\": score_2})\n",
    "scores_concat = pd.concat([df_scores[\"score1\"], df_scores[\"score2\"]], axis = 0)\n",
    "list_images = scores_concat.drop_duplicates(inplace=False)\n",
    "list_images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3ana5ZN-QVtC"
   },
   "source": [
    "<div align='justify'>There are 12.936.517 distinct images in total. Now, we can encode the list of images as follows:</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 318692,
     "status": "ok",
     "timestamp": 1582909570594,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "HHFt849oNnAY",
    "outputId": "317420f0-6f7e-4365-f84e-a5e46b1373ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode the image by an id \n",
    "encoder = preprocessing.LabelEncoder()\n",
    "encoder.fit(list_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1QevkFXCOAGD"
   },
   "outputs": [],
   "source": [
    "# Add the ids of the first group of images\n",
    "id_first_img = encoder.transform(df_scores[\"score1\"])\n",
    "combined.insert(loc=0, column='id_first_img', value=id_first_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CMSEAxDgSoRX"
   },
   "outputs": [],
   "source": [
    "# Add the ids of the second group of images\n",
    "id_sec_img = encoder.transform(df_scores[\"score2\"])\n",
    "combined.insert(loc=1, column='id_sec_img', value=id_sec_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 355099,
     "status": "ok",
     "timestamp": 1582909607020,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "2C67JSrpOO3w",
    "outputId": "db6d4649-9564-46a7-b8e5-4336da77ace1"
   },
   "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_first_img</th>\n",
       "      <th>id_sec_img</th>\n",
       "      <th>qs1</th>\n",
       "      <th>qs2</th>\n",
       "      <th>qs3</th>\n",
       "      <th>qs4</th>\n",
       "      <th>qs5</th>\n",
       "      <th>qs6</th>\n",
       "      <th>qs7</th>\n",
       "      <th>qs8</th>\n",
       "      <th>qs9</th>\n",
       "      <th>qs10</th>\n",
       "      <th>qs11</th>\n",
       "      <th>qs12</th>\n",
       "      <th>qs13</th>\n",
       "      <th>qr1</th>\n",
       "      <th>qr2</th>\n",
       "      <th>qr3</th>\n",
       "      <th>qr4</th>\n",
       "      <th>qr5</th>\n",
       "      <th>qr6</th>\n",
       "      <th>qr7</th>\n",
       "      <th>qr8</th>\n",
       "      <th>qr9</th>\n",
       "      <th>qr10</th>\n",
       "      <th>qr11</th>\n",
       "      <th>qr12</th>\n",
       "      <th>qr13</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "      <th>s7</th>\n",
       "      <th>s8</th>\n",
       "      <th>s9</th>\n",
       "      <th>s10</th>\n",
       "      <th>s11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6173309</td>\n",
       "      <td>6637503</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.24</td>\n",
       "      <td>-5.27</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>0.63</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.89</td>\n",
       "      <td>350.98</td>\n",
       "      <td>64.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.44</td>\n",
       "      <td>-12.53</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.59</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.03</td>\n",
       "      <td>276.02</td>\n",
       "      <td>58.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2405.94</td>\n",
       "      <td>1981.09</td>\n",
       "      <td>2677.84</td>\n",
       "      <td>2470.44</td>\n",
       "      <td>1579.39</td>\n",
       "      <td>2185.79</td>\n",
       "      <td>2118.77</td>\n",
       "      <td>2580.99</td>\n",
       "      <td>2498.04</td>\n",
       "      <td>3180.58</td>\n",
       "      <td>2718.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1305530</td>\n",
       "      <td>1201925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>-4.31</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.76</td>\n",
       "      <td>347.06</td>\n",
       "      <td>28.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-18.97</td>\n",
       "      <td>-1.31</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.34</td>\n",
       "      <td>263.84</td>\n",
       "      <td>30.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3241.37</td>\n",
       "      <td>2015.24</td>\n",
       "      <td>3987.19</td>\n",
       "      <td>3343.53</td>\n",
       "      <td>2894.69</td>\n",
       "      <td>2947.34</td>\n",
       "      <td>2689.42</td>\n",
       "      <td>3763.51</td>\n",
       "      <td>2544.22</td>\n",
       "      <td>3515.58</td>\n",
       "      <td>3247.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11691707</td>\n",
       "      <td>3498297</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.76</td>\n",
       "      <td>-19.29</td>\n",
       "      <td>2.71</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.53</td>\n",
       "      <td>135.94</td>\n",
       "      <td>57.65</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-12.58</td>\n",
       "      <td>-9.99</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.60</td>\n",
       "      <td>357.67</td>\n",
       "      <td>51.98</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2199.22</td>\n",
       "      <td>2549.59</td>\n",
       "      <td>2164.41</td>\n",
       "      <td>2100.69</td>\n",
       "      <td>1294.35</td>\n",
       "      <td>2136.04</td>\n",
       "      <td>1644.05</td>\n",
       "      <td>2308.05</td>\n",
       "      <td>1774.88</td>\n",
       "      <td>3005.79</td>\n",
       "      <td>2507.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4664413</td>\n",
       "      <td>8854323</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>-8.04</td>\n",
       "      <td>3.04</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.09</td>\n",
       "      <td>288.07</td>\n",
       "      <td>21.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.57</td>\n",
       "      <td>-13.22</td>\n",
       "      <td>-2.66</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.38</td>\n",
       "      <td>268.44</td>\n",
       "      <td>24.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>2390.40</td>\n",
       "      <td>2141.75</td>\n",
       "      <td>2086.02</td>\n",
       "      <td>2154.69</td>\n",
       "      <td>1247.48</td>\n",
       "      <td>1903.36</td>\n",
       "      <td>2232.54</td>\n",
       "      <td>2146.34</td>\n",
       "      <td>2201.53</td>\n",
       "      <td>3315.03</td>\n",
       "      <td>2274.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11466218</td>\n",
       "      <td>10914021</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.56</td>\n",
       "      <td>-10.61</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.10</td>\n",
       "      <td>166.12</td>\n",
       "      <td>37.36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.03</td>\n",
       "      <td>-15.53</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.25</td>\n",
       "      <td>172.19</td>\n",
       "      <td>26.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1822.14</td>\n",
       "      <td>2202.60</td>\n",
       "      <td>2150.07</td>\n",
       "      <td>2194.25</td>\n",
       "      <td>1575.43</td>\n",
       "      <td>2345.59</td>\n",
       "      <td>1976.82</td>\n",
       "      <td>1440.78</td>\n",
       "      <td>2061.06</td>\n",
       "      <td>2927.96</td>\n",
       "      <td>2073.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_first_img  id_sec_img  qs1  qs2  ...       s8       s9      s10      s11\n",
       "0       6173309     6637503  1.0  0.0  ...  2580.99  2498.04  3180.58  2718.29\n",
       "1       1305530     1201925  1.0  0.0  ...  3763.51  2544.22  3515.58  3247.49\n",
       "2      11691707     3498297  1.0  0.0  ...  2308.05  1774.88  3005.79  2507.02\n",
       "3       4664413     8854323  1.0  0.0  ...  2146.34  2201.53  3315.03  2274.76\n",
       "4      11466218    10914021  1.0  0.0  ...  1440.78  2061.06  2927.96  2073.16\n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overview of the result on the combined dataset\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pkjQ5rt8S3v3"
   },
   "outputs": [],
   "source": [
    "# Update the train and test set\n",
    "Xtrain_new = combined.iloc[:Xtrain.shape[0], [0, 1]].join(Xtrain)\n",
    "Xtest_new = combined.iloc[(Xtrain.shape[0]+1):Xtrain.shape[0]+1 + Xtest.shape[0]-1, [0, 1]].join(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Fu_r2ZhlaIet"
   },
   "source": [
    "<div align='justify'>Now let's check if there are common images in the train and test set</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 365259,
     "status": "ok",
     "timestamp": 1582909617189,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "whK8cnZfY0Vi",
    "outputId": "c60a845f-b7f6-4e57-b765-899aca812315"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of common image in the first id columns : 0\n",
      "Number of common image in the second id columns : 241\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of common image in the first id columns : {}\".format(len(set(Xtrain_new[\"id_first_img\"]) & set(Xtest_new[\"id_first_img\"]))))\n",
    "print(\"Number of common image in the second id columns : {}\".format(len(set(Xtrain_new[\"id_sec_img\"]) & set(Xtest_new[\"id_sec_img\"]))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4-ZNNisFauI2"
   },
   "source": [
    "<div align='justify'>As we can see, there is not a lot of common images in the train and test set, meaning that we should train our pair matching algorithm with the intrinsec properties of the images, i.e. with the given numbers, the relationships between images won't help us. <br>\n",
    "<br>\n",
    "<b>Conclusions</b><br>\n",
    "As the direct information about the relationships between the images doesn't contain usefull information, we will try to identify a structure in the given features proper to the matching pairs and see if this structure is common to the train and test set.</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NWhF7VeKd-0R"
   },
   "source": [
    "#### 3.3 Descriptive analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-r3-piAMouG-"
   },
   "source": [
    "<div align='justify'>First, we will study the variable to be explained, then the explanatory variables, and finally the links between all the variables. </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EZEacZ4QnrrX"
   },
   "source": [
    "##### <b><i>The response variable in the train set</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0OzQX0RJpIjV"
   },
   "source": [
    "<div align='justify'>The response variable is binary. It takes its values in $\\{0, 1\\}$. As a reminder, the variable takes the value 1 if both images belong to the same person. Its distribution is as follows:</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 366136,
     "status": "ok",
     "timestamp": 1582909618070,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "BRrDn7LfnvDB",
    "outputId": "258f87b0-787d-40fe-8135-43ff8118cf32"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9800713.00000\n",
       "mean           0.06867\n",
       "std            0.25290\n",
       "min            0.00000\n",
       "25%            0.00000\n",
       "50%            0.00000\n",
       "75%            0.00000\n",
       "max            1.00000\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ytrain.describe().apply(lambda x: '%.5f' % x, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vBLny3PootYT"
   },
   "source": [
    "<div align='justify'>6.8% of the data have the label 1, i.e. the images were detected as belonging to the same person. The data are therefore <b>unbalanced</b>. Specific techniques will have to be adopted to deal with this particular case. Indeed, if we learn a model as it is and measure its accuracy, the model will appear excellent because it will have learned to predict \"the images do not belong to the same person\" (label 0).</div> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 366133,
     "status": "ok",
     "timestamp": 1582909618071,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "fCHdMPEdtuM_",
    "outputId": "9383832d-18e7-4281-f232-dee1e958fa1b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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le41XE9lr9NqRXb7nnurvKHsK60ZE50+MJyRJT6b8P4/I8cMpX4bfRMTLhp3ZxsSq7Dpt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\nqucgNLPqOQjNrHoOQjOrnoPQzKr3v9oCfHDALpLdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Classe proportions\n",
    "plt.figure(figsize=(4, 4))\n",
    "sns.barplot(x=\"index\", y=\"y\", data=Ytrain[\"y\"].value_counts().reset_index(), palette=\"Set2\")\n",
    "plt.ylabel(None)\n",
    "plt.xlabel(None)\n",
    "plt.title(\"Classe proportions:\\nDo the images belong to the same person?\\n{1:Yes, 0:No}\", fontweight='bold', fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fJNaKCU5KGqZ"
   },
   "source": [
    "<div align='justify'>Let's try to figure out what are the features that are specific to the matching images</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AyWu4bXenii3"
   },
   "source": [
    "##### <b><i>Explanatory variables</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PkVY1v9Vsb01"
   },
   "source": [
    "<ul>\n",
    "<li>Quality scores of the first image</li>\n",
    "</ul>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 370875,
     "status": "ok",
     "timestamp": 1582909622818,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "KiXRKl9wrS47",
    "outputId": "93c033a1-7c67-4033-d808-080e30df9fb3"
   },
   "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>qs1</th>\n",
       "      <th>qs2</th>\n",
       "      <th>qs3</th>\n",
       "      <th>qs4</th>\n",
       "      <th>qs5</th>\n",
       "      <th>qs6</th>\n",
       "      <th>qs7</th>\n",
       "      <th>qs8</th>\n",
       "      <th>qs9</th>\n",
       "      <th>qs10</th>\n",
       "      <th>qs11</th>\n",
       "      <th>qs12</th>\n",
       "      <th>qs13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-5.77</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.86</td>\n",
       "      <td>256.10</td>\n",
       "      <td>37.65</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>9.95</td>\n",
       "      <td>8.87</td>\n",
       "      <td>4.57</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.77</td>\n",
       "      <td>75.13</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-78.20</td>\n",
       "      <td>-67.71</td>\n",
       "      <td>-34.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-298.00</td>\n",
       "      <td>18.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.22</td>\n",
       "      <td>-12.63</td>\n",
       "      <td>-2.85</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.31</td>\n",
       "      <td>211.26</td>\n",
       "      <td>25.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-4.87</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.67</td>\n",
       "      <td>264.15</td>\n",
       "      <td>34.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.19</td>\n",
       "      <td>309.14</td>\n",
       "      <td>48.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>59.34</td>\n",
       "      <td>26.68</td>\n",
       "      <td>64.82</td>\n",
       "      <td>38.73</td>\n",
       "      <td>15.68</td>\n",
       "      <td>24.42</td>\n",
       "      <td>470.47</td>\n",
       "      <td>76.77</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              qs1         qs2         qs3  ...        qs11        qs12        qs13\n",
       "count  9800713.00  9800713.00  9800713.00  ...  9800713.00  9800713.00  9800713.00\n",
       "mean         0.99        0.01        0.00  ...       37.65        0.19        0.70\n",
       "std          0.06        0.05        0.04  ...       13.40        0.39        0.45\n",
       "min          0.00        0.00        0.00  ...       18.42        0.00        0.00\n",
       "25%          1.00        0.00        0.00  ...       25.80        0.00        0.01\n",
       "50%          1.00        0.00        0.00  ...       34.49        0.00        1.00\n",
       "75%          1.00        0.00        0.00  ...       48.07        0.00        1.00\n",
       "max          1.00        1.00        1.00  ...       76.77        1.00        1.00\n",
       "\n",
       "[8 rows x 13 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics\n",
    "Xtrain.loc[:,\"qs1\":\"qs13\"].describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7FT99Rs5eu0c"
   },
   "source": [
    "<div align='justify'>In the first group of quality scores, there are different types of values :\n",
    "\n",
    "- some scores range from 0 to 1, sometimes with averages of 0.99 or 0.01\n",
    "- some scores have negative and positive values\n",
    "- some scores have a scale of about ten or even a hundred\n",
    "</div>\n",
    "<div align='justify'>\n",
    "The following boxplot allows to represent these different observations\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 398547,
     "status": "ok",
     "timestamp": 1582909650496,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "7e1Lwdx11qq9",
    "outputId": "cc3c223b-c200-481b-df72-e942b12c2b2d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplots\n",
    "plt.figure(figsize=(10, 4))\n",
    "sns.boxplot(data=Xtrain.loc[:,\"qs1\":\"qs13\"], palette=\"Set2\")\n",
    "plt.title(\"Distribution of the quality scores of the first image\", fontweight=\"bold\", fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "p84OnTc0gV3W"
   },
   "source": [
    "<div align='justify'>Now let's look at the relationships with the response variable. I will not use the \"pairplot\" method from seaborn because it crashes the colab session. I analyzed the histogram of each quality scores for the first image and for the matching and unmatching scores.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 400947,
     "status": "ok",
     "timestamp": 1582909652901,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "7--_QE6lpyiO",
    "outputId": "b0ccd855-ae79-49f5-892a-fd9d3fec1020"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tk3eT/PDybPIz8lm4bWGiwzPGmISL6YrXtsLjDzbaXNPvw7U8sTmLp7sup3JU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\nF63sRHLzjwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of the quality scores of the first image\n",
    "col = \"qs8\"\n",
    "sns.distplot(Xtrain.loc[np.where(Ytrain[\"y\"] == 0)[0], col] , color=\"green\", label=\"Unmatching pair\", bins=20)\n",
    "sns.distplot(Xtrain.loc[np.where(Ytrain[\"y\"] == 1)[0], col] , color=\"red\", label=\"Matching pair\")\n",
    "plt.title(\"Distribution of the quality scores of the first image\", fontweight=\"bold\", fontsize=12);\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8GPiVr2mn20M"
   },
   "source": [
    "<div align='justify'>\n",
    "<b>Conclusions</b><br>\n",
    "Overall, the quality scores of the first picture have the same distribution and they do not seem really useful to differentiate between matching and non-matching pairs.</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yZ45UNkzJi7Z"
   },
   "source": [
    "<ul>\n",
    "<li>Quality scores of the second image</li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 404806,
     "status": "ok",
     "timestamp": 1582909656763,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "uzOQHf2oJq6r",
    "outputId": "872d1416-ff70-4fac-dedf-9724fa39fcd1"
   },
   "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>qr1</th>\n",
       "      <th>qr2</th>\n",
       "      <th>qr3</th>\n",
       "      <th>qr4</th>\n",
       "      <th>qr5</th>\n",
       "      <th>qr6</th>\n",
       "      <th>qr7</th>\n",
       "      <th>qr8</th>\n",
       "      <th>qr9</th>\n",
       "      <th>qr10</th>\n",
       "      <th>qr11</th>\n",
       "      <th>qr12</th>\n",
       "      <th>qr13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-5.79</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.82</td>\n",
       "      <td>268.56</td>\n",
       "      <td>38.06</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.02</td>\n",
       "      <td>8.45</td>\n",
       "      <td>8.35</td>\n",
       "      <td>4.39</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.82</td>\n",
       "      <td>74.96</td>\n",
       "      <td>13.60</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-54.76</td>\n",
       "      <td>-42.13</td>\n",
       "      <td>-26.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-298.00</td>\n",
       "      <td>18.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.58</td>\n",
       "      <td>-12.42</td>\n",
       "      <td>-2.75</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.29</td>\n",
       "      <td>224.61</td>\n",
       "      <td>25.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-4.91</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.64</td>\n",
       "      <td>277.41</td>\n",
       "      <td>35.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.13</td>\n",
       "      <td>321.69</td>\n",
       "      <td>48.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>260.25</td>\n",
       "      <td>233.13</td>\n",
       "      <td>0.98</td>\n",
       "      <td>51.08</td>\n",
       "      <td>22.77</td>\n",
       "      <td>24.74</td>\n",
       "      <td>30.01</td>\n",
       "      <td>9.43</td>\n",
       "      <td>145.21</td>\n",
       "      <td>1611.47</td>\n",
       "      <td>76.77</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              qr1         qr2         qr3  ...        qr11        qr12        qr13\n",
       "count  9800713.00  9800713.00  9800713.00  ...  9800713.00  9800713.00  9800713.00\n",
       "mean         1.00        0.00        0.00  ...       38.06        0.18        0.73\n",
       "std          0.68        0.61        0.02  ...       13.60        0.38        0.44\n",
       "min          0.00        0.00        0.00  ...       18.46        0.00        0.00\n",
       "25%          1.00        0.00        0.00  ...       25.78        0.00        0.04\n",
       "50%          1.00        0.00        0.00  ...       35.21        0.00        1.00\n",
       "75%          1.00        0.00        0.00  ...       48.95        0.00        1.00\n",
       "max        260.25      233.13        0.98  ...       76.77        1.00        1.00\n",
       "\n",
       "[8 rows x 13 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics\n",
    "Xtrain.loc[:,\"qr1\":\"qr13\"].describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qZap5lZD0mZM"
   },
   "source": [
    "<div align='justify'>In the second group of quality scores, there are different types of values :\n",
    "\n",
    "- some scores range from 0 to 1 \n",
    "- some scores have negative and positive values\n",
    "- some scores may have very different scales, of the order of several tens, hundreds or even thousands\n",
    "</div>\n",
    "<div align='justify'>\n",
    "The following boxplot allows to represent these different observations\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 430496,
     "status": "ok",
     "timestamp": 1582909682457,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "4dkncRJQJs1Z",
    "outputId": "9d604505-24df-4b7c-f6af-7f195d2dbbcc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JaoiJlyRJUkNMvCRJs0774yN8nISaYuIlSZLUEB+gKkmalWzlUj/Y4iVJktQQEy9JkqSG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\nlyRJUkP+P8n2tT6/baBgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplots\n",
    "plt.figure(figsize=(10, 4))\n",
    "sns.boxplot(data=Xtrain.loc[:,\"qr1\":\"qr13\"], palette=\"Set2\")\n",
    "plt.title(\"Distribution of the quality scores of the second image\", fontweight=\"bold\", fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5u64RB2E1Njy"
   },
   "source": [
    "<div align='justify'>Now let's look at the relationships with the response variable.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 433313,
     "status": "ok",
     "timestamp": 1582909685277,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "o7t7qSXM1Me3",
    "outputId": "b88f6752-3889-42c2-b0c7-a1f67f5e58a0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QVi+35tCaZudHeVnY3NtK0povWLH19Omo+We5bwBN4XkkSQjh4XLLcxmZC5G5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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of the quality scores of the second image\n",
    "col = \"qr5\"\n",
    "sns.distplot(Xtrain.loc[np.where(Ytrain[\"y\"] == 0)[0], col] , color=\"green\", label=\"Unmatching pair\", bins=20)\n",
    "sns.distplot(Xtrain.loc[np.where(Ytrain[\"y\"] == 1)[0], col] , color=\"red\", label=\"Matching pair\")\n",
    "plt.title(\"Distribution of the quality scores of the second image\", fontweight=\"bold\", fontsize=12);\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HsbciE4MMDfE"
   },
   "source": [
    "<div align='justify'>\n",
    "<b>Conclusions</b><br>\n",
    "Overall, the quality scores of the second picture have the same distribution and they do not seem really useful to differentiate between matching and non-matching pairs. We can notice some specificities:\n",
    "\n",
    "- For the variables \"qr2\" and \"qr9\", we can see that for the matching values, the range if larger, from 0 to hundreds\n",
    "- For the variables \"qr12\" and \"qr13\", we can see that when the images are matching, the quality score of the second image is either 0 or 1 whereas for unmatching images, the values could be superior to 0 and inferior to 1<br>\n",
    "</div>\n",
    "<div align='justify'>\n",
    "But like this, it is difficult to see how these variables could be usefull to differenciate the matching and unmatching images. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iuoXPbqZPLnj"
   },
   "source": [
    "<div align='justify'>I also analyzed the distributions of the differences between the two scores to see if a simpler structure emerged. \n",
    "Except for the variables \"qs1\", \"qs2\", \"qs3\", \"qs12\" and \"qs13\", the difference between the two scores has a normal distribution and does not differ whether the images match or not. </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 310
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 436424,
     "status": "ok",
     "timestamp": 1582909688392,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "e8XIJ_hl-YHv",
    "outputId": "9e06cfd9-4400-4c03-9ce1-ed9b8fce0d25"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/ntmzZ3PbbbeRnZ3Nxo0b+eCDDzjjjDOAxtMIRxMrHfLdd99Nnz592LJlC7fd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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of the difference between the quality scores\n",
    "sel = pd.DataFrame({\"diff\": Xtrain[\"qs4\"] - Xtrain[\"qr4\"]})\n",
    "test = pd.concat([sel, Ytrain[\"y\"]], axis=1)\n",
    "sns.distplot(test.loc[np.where(test[\"y\"] == 0)[0], \"diff\"] , color=\"green\", label=\"Unmatching pair\", bins=20)\n",
    "sns.distplot(test.loc[np.where(test[\"y\"] == 1)[0], \"diff\"] , color=\"red\", label=\"Matching pair\")\n",
    "plt.title(\"Distribution of the difference between\\nboth quality scores of the second image\", fontweight=\"bold\", fontsize=12);\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D7nc-VaoKMYT"
   },
   "source": [
    "<div align='justify'>Now, let's analyze the matching scores</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7TQRaAWHJypt"
   },
   "source": [
    "<ul>\n",
    "<li>Matching scores</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 439804,
     "status": "ok",
     "timestamp": 1582909691776,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "qwGCx7OaJ2z3",
    "outputId": "9d96ca72-9f0c-4c3a-cdc2-33be0f2d0547"
   },
   "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>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "      <th>s7</th>\n",
       "      <th>s8</th>\n",
       "      <th>s9</th>\n",
       "      <th>s10</th>\n",
       "      <th>s11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "      <td>9800713.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2421.92</td>\n",
       "      <td>2294.54</td>\n",
       "      <td>2619.50</td>\n",
       "      <td>2646.18</td>\n",
       "      <td>2337.43</td>\n",
       "      <td>2476.21</td>\n",
       "      <td>2364.23</td>\n",
       "      <td>2441.25</td>\n",
       "      <td>2740.13</td>\n",
       "      <td>3306.10</td>\n",
       "      <td>2717.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1036.61</td>\n",
       "      <td>953.51</td>\n",
       "      <td>1260.02</td>\n",
       "      <td>1392.76</td>\n",
       "      <td>1713.47</td>\n",
       "      <td>1206.69</td>\n",
       "      <td>1411.61</td>\n",
       "      <td>1382.01</td>\n",
       "      <td>1361.46</td>\n",
       "      <td>847.41</td>\n",
       "      <td>1207.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>810.02</td>\n",
       "      <td>621.83</td>\n",
       "      <td>983.51</td>\n",
       "      <td>209.37</td>\n",
       "      <td>669.21</td>\n",
       "      <td>328.08</td>\n",
       "      <td>452.83</td>\n",
       "      <td>1238.68</td>\n",
       "      <td>2160.87</td>\n",
       "      <td>1123.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1895.08</td>\n",
       "      <td>1778.46</td>\n",
       "      <td>2021.02</td>\n",
       "      <td>2022.19</td>\n",
       "      <td>1592.70</td>\n",
       "      <td>1900.85</td>\n",
       "      <td>1722.64</td>\n",
       "      <td>1799.49</td>\n",
       "      <td>2162.60</td>\n",
       "      <td>2926.56</td>\n",
       "      <td>2170.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2150.87</td>\n",
       "      <td>2045.51</td>\n",
       "      <td>2278.87</td>\n",
       "      <td>2254.63</td>\n",
       "      <td>1891.18</td>\n",
       "      <td>2143.05</td>\n",
       "      <td>1986.90</td>\n",
       "      <td>2074.42</td>\n",
       "      <td>2367.71</td>\n",
       "      <td>3072.89</td>\n",
       "      <td>2386.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2508.11</td>\n",
       "      <td>2425.35</td>\n",
       "      <td>2650.59</td>\n",
       "      <td>2599.26</td>\n",
       "      <td>2306.54</td>\n",
       "      <td>2500.04</td>\n",
       "      <td>2360.03</td>\n",
       "      <td>2461.89</td>\n",
       "      <td>2659.91</td>\n",
       "      <td>3283.64</td>\n",
       "      <td>2698.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10867.54</td>\n",
       "      <td>10582.65</td>\n",
       "      <td>14978.20</td>\n",
       "      <td>12126.51</td>\n",
       "      <td>19901.92</td>\n",
       "      <td>12649.93</td>\n",
       "      <td>13897.51</td>\n",
       "      <td>14897.88</td>\n",
       "      <td>14110.44</td>\n",
       "      <td>10135.24</td>\n",
       "      <td>11874.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               s1          s2          s3  ...          s9         s10         s11\n",
       "count  9800713.00  9800713.00  9800713.00  ...  9800713.00  9800713.00  9800713.00\n",
       "mean      2421.92     2294.54     2619.50  ...     2740.13     3306.10     2717.58\n",
       "std       1036.61      953.51     1260.02  ...     1361.46      847.41     1207.08\n",
       "min          0.00      810.02      621.83  ...     1238.68     2160.87     1123.05\n",
       "25%       1895.08     1778.46     2021.02  ...     2162.60     2926.56     2170.65\n",
       "50%       2150.87     2045.51     2278.87  ...     2367.71     3072.89     2386.12\n",
       "75%       2508.11     2425.35     2650.59  ...     2659.91     3283.64     2698.51\n",
       "max      10867.54    10582.65    14978.20  ...    14110.44    10135.24    11874.24\n",
       "\n",
       "[8 rows x 11 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics\n",
    "Xtrain.loc[:,\"s1\":\"s11\"].describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CloUClpTKTi6"
   },
   "source": [
    "<div align='justify'>Matching scores range over intervals of the same scale. The first score s1 is the only one to have a minimum of 0. We will study their distributions in the sample of matching images and unmatching images. </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 473388,
     "status": "ok",
     "timestamp": 1582909725385,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "1gMbRi3uJ9EW",
    "outputId": "a1b87451-bc4f-46f0-a878-8216ad2c4d8e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+B3y0UBcRW4At6fWzkl4BPgSsACYVLT4p1QG8KWm/iFiZTmeuGmhMZmZmZo1mMD1jJwIv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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplots\n",
    "plt.figure(figsize=(10, 4))\n",
    "sns.boxplot(data=Xtrain.loc[:,\"s1\":\"s11\"], palette=\"Set2\")\n",
    "plt.title(\"Distribution of the matching scores\", fontweight=\"bold\", fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pvjXnwSzMH7x"
   },
   "source": [
    "<div align='justify'>And let's analyze the link with the response variable.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 476718,
     "status": "ok",
     "timestamp": 1582909728727,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "qIW5K8gPOHMU",
    "outputId": "eb524e55-52d7-47d3-c6b5-ce3f78e68a55"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Cj7nl+FsAGJ0zWmsMynPNDS+Nhscff5wrr7yS6667DoCbb76ZAQMGRCTv2267jfHjx9Oz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\n0etSqXZAawxRZIyhqLKIrJT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CVXoXqtJT6//JV6el1C+ryuhCZVY6xhdfP7IGw7eU8mnC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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Link with the response variable\n",
    "col = \"s11\"\n",
    "sns.distplot(Xtrain.loc[np.where(Ytrain[\"y\"] == 0)[0], col] , color=\"green\", label=\"Unmatching pair\", bins=20)\n",
    "sns.distplot(Xtrain.loc[np.where(Ytrain[\"y\"] == 1)[0], col] , color=\"red\", label=\"Matching pair\")\n",
    "plt.title(\"Distribution of the matching scores\", fontweight=\"bold\", fontsize=12);\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RDR1JazYP9CV"
   },
   "source": [
    "<div align='justify'>The first thing that emerges from the different graphs is that the scores allow us to differentiate between images that match and those that do not. We also notice for each graph that there is \n",
    "an interval where the two classes overlap. <br>\n",
    "<br>\n",
    "<b>Conclusions</b><br>\n",
    "These variables will be very useful to build the model. Since the classes overlap, a linear classifier will not be well suited. It would be better  to accept a certain number of errors or to use a kernel transformation. <br>\n",
    "<br>\n",
    "Now let's look at the correlation between all the variables.</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MY8FfNqB75Sf"
   },
   "source": [
    "#### 3.2 Correlation analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9WhnvPgZ3s5A"
   },
   "source": [
    "<ul>\n",
    "<li>Correlations between the quality scores of the first image</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 499
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 486456,
     "status": "ok",
     "timestamp": 1582909738472,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "QIj6eRUMTCVM",
    "outputId": "e186615e-6dd4-4f95-f1fc-91195c7f6332"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ffvwai8WPZi1a89QzL5CRkcGk6NHs2bUDZbHQ8/m+XNe4qVc73Fm79S9Gz9Ja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ycQBZ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4KN1Nf7so3cjWVFx3Xkg5g0adQZt2ne+p3Mq4byxiUeAyRowex4avN1s8jC/Jo3q/ZumX9bbt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t6ZbUUagKIoZkBDjDt+DVRVVWvKAp6mIi0v5VSRll9MqUjLL5lVpBWEnCrS8ktmFWkFJaeK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\nMzMzMzOzLvwPyLhZL65svsAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1584x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_match = Xtrain.loc[np.where(Ytrain[\"y\"] == 1)[0],\"qs1\":\"qs13\"].corr()\n",
    "corr_unmatch = Xtrain.loc[np.where(Ytrain[\"y\"] == 0)[0],\"qs1\":\"qs13\"].corr()\n",
    "\n",
    "fig = plt.figure(figsize=(22, 8))\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax2 = fig.add_subplot(122)\n",
    "sns.heatmap(corr_match, annot=True, vmin=-1, vmax=1, center=0, cmap= 'coolwarm', ax=ax1)\n",
    "ax1.set_title(\"Correlations between the first quality scores for the matching images\", fontweight='bold', fontsize=12)\n",
    "sns.heatmap(corr_unmatch, annot=True, vmin=-1, vmax=1, center=0, cmap= 'coolwarm', ax=ax2)\n",
    "ax2.set_title(\"Correlations between the first quality scores for the unmatching images\", fontweight='bold', fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VTSuuoSmUQQM"
   },
   "source": [
    "<div align='justify'>There are anti-correlations between \"qs1\" and \"qs2\", \"qs1\" and \"qs3\". The correlation structure remains the same whether the images match or not. </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "l7LUpfa43_I6"
   },
   "source": [
    "<ul>\n",
    "<li>Correlations between the quality scores of the second image</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 499
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 493475,
     "status": "ok",
     "timestamp": 1582909745496,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "gdBjR1qb3eDB",
    "outputId": "e0f53084-3b98-483e-c1bf-0631c16a8f6e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ExWJGMRbbO1LckKi1vhqTl/pY571r+bi0fO+ubnGbl0tQfh1xtS0Ik/fy2aa1Lqg+tFNY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\nMmygsMY5yKUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1584x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_match = Xtrain.loc[np.where(Ytrain[\"y\"] == 1)[0],\"qr1\":\"qr13\"].corr()\n",
    "corr_unmatch = Xtrain.loc[np.where(Ytrain[\"y\"] == 0)[0],\"qr1\":\"qr13\"].corr()\n",
    "\n",
    "fig = plt.figure(figsize=(22, 8))\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax2 = fig.add_subplot(122)\n",
    "sns.heatmap(corr_match, annot=True, vmin=-1, vmax=1, center=0, cmap= 'coolwarm', ax=ax1)\n",
    "ax1.set_title(\"Correlations between the second quality scores for the matching images\", fontweight='bold', fontsize=12)\n",
    "sns.heatmap(corr_unmatch, annot=True, vmin=-1, vmax=1, center=0, cmap= 'coolwarm', ax=ax2)\n",
    "ax2.set_title(\"Correlations between the second quality scores for the unmatching images\", fontweight='bold', fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_YOOQzWXVEdD"
   },
   "source": [
    "<div align='justify'>Identically, there are correlations between \"qr1\" and \"qr2\" (the correlation coefficient is equal to 1 that is due to the fact that the variable is either equal to 0 or 1), \"qr1\" and \"qr9\", \"qr2\" and \"qr9\". The correlation structure is clearly not the same whether the images match or not for the variable \"qr1\", \"qr2\" and \"qr3\".</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ElLW8VdeXUU9"
   },
   "source": [
    "<ul>\n",
    "<li>Correlations between all the quality scores</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 533
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 508094,
     "status": "ok",
     "timestamp": 1582909760167,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "-VjXb_SFXSpu",
    "outputId": "b020fce2-166c-4484-acea-ed296fad89c6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vlCRJc0HV21Ez8zzgPICIWF565fETDPc64HU105IkSe2KoV60SiH1hnmnJEmD\nyZxzjEtCkiRJkiRJ6rGqO+G6ZebytmJJkqTe83XxGlTmnZIkDQ5zzjGtVcL1wv4HPaY6xi/dfOWO\nB9qB7xz20uoYu3/rBo7Z/G9VMe7b9UC2DdWtsqFt29g8tGtVjAW5hUXbNlbF2Lhgd4ZyW1UMgKSd\nnXnLyHB1jJEWbiytXb+RWb1ct0U7h4XIbCXObpsfqBr/4UV7E9SXZSTqt5E2yrEgt1SNvy0WtLJu\nMqKVOFtYVDX+ECNso27dDLO1avyO2vXb1vFsZwzqW6WkXjj0yCdVxzhizRXVMb59+GlV4+96/WqO\n21KXc96/+ClktPDATG5jw9AeVSF2G3moleNj9fxkO7nAghipjtGWyLqyZAzNyrlrIm2Uo5/O4/2y\nXGtzvTbyxbZyzrZknzxMWLuNtPG7ZGeYc47pjy2oR/qlAg6oroCD+goaoLoCDqiugAOsgJtAG+u3\njeXahn6pgIN2TjBzpQIO2ls3/VABB1RXwLWljfU708mQpDFzpQIOqK6AgxYqrBq1FXDQTr7XxvxY\nAfdo/VJR1A8VcG3ql+XaRq7XLzHaMlcq4NqKoenp6zvhJElS7/hogCRJknrNnHNMf1TlSpIkSZIk\nSXOYd8JJkjRP+bp4SZIk9Zo55xgr4SRJmqd8NECSJEm9Zs45ZkaqIyPiZRFxY0TcEBGrIuL4mZiu\nJEmS5hfzTkmS1K9m6k64LwJXZmZGxJHAx4BDZ2jakiRpAr4uXnOUeackSX3EnHNMVSVcRJwLnA6s\nBe4GrgM2A2cBW4E1mbksM3/RNdru0EfvgJYkSVLfM++UJEmDbtqPo0bEscAy4CjgZOCZzVfnAEdn\n5pGUpKgz/Msj4hbg08Dvbifumc2jA6tuuPYfp1s8SZK0AzEUPflIbetF3tmdc15/zYU9Lb8kSfOZ\nOeeYmjbhTgAuz8z1mfkgcGXT/0bg4oh4FeWqJACZeXlmHgqcBrx9sqCZuSIzj8vM44563msriidJ\nkqQ5ovW8szvnPObE1/W4+JIkSb15McMpwAXAMcDKiHjEI6+ZeS3w1IjYtwfTliRJUxRDQz35SDPI\nvFOSpD5nzjmmptTXAqdFxOKI2BM4tYl3QGZeDZwNLAH2iIinRUQARMQxwC7Az+uKLkmSavhogAaI\neackSQPKnHPMtF/MkJnXR8RlwGpKA7krKQ3fXhQRS4AAzs/MdRHxe8DvRMQWYAPwXzPTRnIlSZK0\nQ+adkiRpLqh6O2pmngecBxARy0uvPH6C4d4JvLNmWpIkqV2DegVR85N5pyRJg8mcc8xgPkQrSZIk\nSZIkDZCqO+G6ZebytmJJkoIHeoIAACAASURBVKTe86qkBpV5pyRJg8Occ0xrlXC98IYTb68a/7aN\nB1eX4YR/f291DICvPPv11TGedsu/Vsd44ro11THuX7K0OsZIDFfHyGhnR37CorXVMTbkbtUxtrWw\nOw6ztTpG9FGzOQ8v2rs6RlA/PyMt3DQ8xEh9OYb6+pC909rY1pL640Abx5I2yjEbBvWtUlLbfv/Z\nN1XHuGnjYdUxjv/3C6pj/Nuz/6A6xiG3fLY6BsBuIw9Vx9g0tLg6RkQbuc3C6gh7D62rjpEtPci0\nrU9yiqGsz4/65TzexrwAjET9Om4jx2oj/+0XbeThbdka9ceSdo5pM8ucc4xLQpIkSZIkSeqx/rgE\nIkmSZtzQ8OzcwRcRJwHvAYaBCzPzHeO+/1vg15rO3YDHZebezXfbgG83392VmS+dmVJLkiRpOmYr\n54T+yzuthJMkSTMmIoaBC4AXAfcAKyPiyswcbS8hM/+wa/g3AEd3hdiQmUfNVHklSZI0mPox77QS\nTpKkeWqWGsl9FnBbZt4BEBGXAi8DJmu09BXAW2eobJIkSWrZLL6Yoe/yTtuEkyRpnoqhod58Is6M\niFVdnzO7Jvtk4O6u7nuafo8uX8SBwEHAl7p679rE/EZEnNb6QpEkSVKrZinnhD7MO2f0TriIeCbw\ndWBZZn5iJqctSZJmRmauAFa0EGoZ8InM3NbV78DMvDcingp8KSK+nZl1r1PXnGTeKUnS3NZizgkz\nlHfOWCVc8yzuO4HPz9Q0JUnS5Gbp0YB7gQO6uvdv+k1kGfAH3T0y897m7x0RcQ2l3Q4r4fQI5p2S\nJPWPWXwcte/yzqpKuIg4FzgdWEu5xe86YDNwFrAVWJOZy5rB3wB8EnhmzTQlSdJAWwk8PSIOoiRB\ny4BXjh8oIg4F9qHcydTptw+wPjM3RcS+wHOBv5yRUmvWmXdKkqSd1Hd557TbhIuIYykzcBRwMmNJ\nzjnA0Zl5JCUpIiKeDLwc+PspxB19pvfiyz4+3eJJkqQdiKHoyWd7MnMr8Hrgc8DNwMcy86aIeFtE\ndL/2fRlwaWZmV7/DgFURsRq4GnhH99utNHf1Iu/szjk/8rFP9qzskiTNd7ORc0J/5p01d8KdAFye\nmesBIuLKpv+NwMURcQVwRdPv3cDZmTkSscPkfPSZ3nu++53c7sCSJGnaYmh23s+UmVcBV43r92fj\nupdPMN7XgCN6Wjj1q9bzzu6c88e3fMucU5KkHpmtnBP6L+/sRZtwpwDPA04Fzo2II4DjgEubRGhf\n4OSI2JqZV0weRpIkSdou805JkjQwaqojrwVOi4jFEbEnJfkZAg7IzKuBs4ElwB6ZeVBmLs3MpcAn\ngN83EZIkaXbN1qMB0jSYd0qSNKDMOcdM+064zLw+Ii4DVlMayF0JJHBRRCwBAjg/M9e1UlJJkiTN\nS+adkiRpLqh6HDUzzwPOA4iI5aVXHr+Dcc6omaYkSWrHbLbPIe0s805JkgaTOecYl4QkSZIkSZLU\nY629mGGit0lIkqQ+toM3lkv9yrxTkqQBYs45qhdvR23NxuHdq8Zfuuvd1WX46W4HV8cAeNot/1od\n47ZDX1gdY/iWz1bHWDSysTrGENuqY+z90A+rYwAMjWypjrF+t32rYzy48LHVMdqQfXSAHB7ZWh1j\nayysjvGke1dWx/jxk4+tjhGZ1TGGsn7fayvOnlvuq46xdXhRdYwHFzymOsYiNlXHmA2D2qCt1LaH\nFtYfBw5ecFd1jPt2W1od45AWcr1bDz2pOgbA4bd8ujrGwtxcHWMDdb8pAPbbUL9+h7bV55wPL24n\nX3x4eEl1jF1zfXWMLVF/Hh9uKbeplbRzTt17/Y+rYzyw+PHVMRZk/fY6EsPVMYaz/vdAW/nvotxQ\nHaOVfW+kft+baeacY3wcVZIkSZIkSeqxvr4TTpIk9Y6N5EqSJKnXzDnHuCQkSZIkSZKkHvNOOEmS\n5inb55AkSVKvmXOOmZFKuIg4Efhn4PtNr09l5ttmYtqSJGliPhqguci8U5Kk/mLOOWYm74T7Smb+\n5gxOT5IkSfOTeackSeo7VZVwEXEucDqwFrgbuA7YDJwFbAXWZOay2kJKkqT2+WiABol5pyRJg8mc\nc8y0K+Ei4lhgGXBUE+d6SjJ0DnBQZm6KiL27RnlORKwGfgi8JTNvmn6xJUmSNF+Yd0qSpLmg5sHc\nE4DLM3N9Zj4IXNn0vxG4OCJeRbkqCSVROjAzfxn4O+CKyYJGxJkRsSoiVl166UcriidJkrYnhqIn\nH6kHWs87H5lzXtLj4kuSNH+Zc47pRZtwpwDPA04Fzo2II5pkCYDMvCoi3hcR+2bmz8aPnJkrgBUA\nt93+/exB+SRJEoCN5GrwTTvv7M45v3f7D8w5JUnqFXPOUTVL4lrgtIhYHBF7UpKfIeCAzLwaOBtY\nAuwREU+IiACIiGc1w/28ruiSJEmaJ8w7JUnSwJv2nXCZeX1EXAaspjSQuxJI4KKIWAIEcH5mrouI\n1wP/PSK2AhuAZZnpFUdJkmZRU08h9T3zTkmSBpc555iqx1Ez8zzgPICIWF565fETDPde4L0105Ik\nSdL8Zd4pSZIGXS/ahJMkSQMgbJ9DkiRJPWbOOaa1SrjMXN5WLEmSJGky5p2SJGkQeSecJEnz1KC+\n2l2SJEmDw5xzTF9XwmXVy1thKLdVl2FrLqyOAfDEdWuqYwzf8tnqGLceelJ1jP9w879Ux9gcu1bH\n+PleT6mOAbBxZHF1jIWxpTrGcGytjjHCcHWMfpJRf9vyECPVMX7y5GOqYywY2VwdY8vwLtUxtkU7\nh/1tLZw+Ngzt0UJJ6i1iU3WMHNTGZn00QAIgWjhXtJF3boj6nGS3kYeqYxx+y6erYwCsOfSU6hiH\n3XJVdYwF1OdpDyx+fHWMLSyqjjFMfb4IsJD6vKSNnCKi/p0pmfXn4DbO49HS+1/W7faE6hhDWX9M\n2zbUH1UFW6P+93hm/b4H9fUTAAta+N3YxjKZceaco1wSkiRJkiRJUo/1R/W2JEmacT4aIEmSpF4z\n5xzjnXCSJEmSJElSj3knnCRJ81S00O6iJEmStD3mnGNmrBIuIk4E3g0sBH6Wmc+fqWlLkqQJ+GiA\n5ijzTkmS+og556gZqYSLiL2B9wEnZeZdEfG4mZiuJEmS5hfzTkmS1K+qKuEi4lzgdGAtcDdwHbAZ\nOAvYCqzJzGXAK4FPZeZdAJm5tma6kiSpXvi6eA0Q805JkgaTOeeYaVfCRcSxwDLgqCbO9ZRk6Bzg\noMzc1FyJBHgGsDAirgH2BN6Tmf9UU3BJkiTND+adkiRpLqipjjwBuDwz12fmg8CVTf8bgYsj4lWU\nq5JQkqVjgVOAlwD/X0Q8Y6KgEXFmRKyKiFWXXnpJRfEkSdL2xFD05CP1QOt55yNzzo/2fg4kSZqn\nzDnH9KJNuFOA5wGnAudGxBHAPcDPM/Nh4OGIuBb4ZeC740fOzBXACoDv3f6D7EH5JEkSgG+q0uCb\ndt7ZnXPedvv3zTklSeoVc85RNUviWuC0iFgcEXtSkp8h4IDMvBo4G1gC7AH8M3B8RCyIiN2AZwM3\n1xVdkiRJ84R5pyRJGnjTvhMuM6+PiMuA1ZQGclcCCVwUEUuAAM7PzHXAuoj4LOWRgRHgwsz8TnXp\nJUnStA3qbfyaf8w7JUkaXOacY6oeR83M84DzACJieemVx08y7LuAd9VMT5IkSfOTeackSRp0vWgT\nTpIkDQJfFy9JkqReM+cc1VolXGYubyuWJEmSNBnzTkmSNIj6+k64rZXFWxjD1WXYbetD1TEA7l+y\ntDrGopGN1TH+w83/Uh3jpsNOrY5x7LcvrY6xy6YHq2MA3LzomOoYj1v0sxZKUi+j/ln7yP55QdxQ\nbquOsXVoYX1BWlgkW4Z3qY6xeMsv6gtCO2VJ6re1jSyujrE41lfHaEM/7Tc7I1o4ZkhzweasPy7u\nwobqGItH6o/zm4bqj60Lc3N1DIDDbrmqOsbNh55cHeNZqz9SHWOXDfdXx7hl91+pjvH4BT+ujgHt\nnMezhbcdjmR9jGghURvKkeoY26j/7Qkw3EL+28b6bSPv3DbUH9UND8RjWomzJO+rjjHSQh1FG+t3\npplzjumPvUKSNCPaqIDTHOKjAZIkSeo1c85RLglJkiRJkiSpx7wTTpKkecrXxUuSJKnXzDnHeCec\nJEmSJEmS1GPeCSdJ0nzVQqPakiRJ0naZc46akSUREf8rIm5oPt+JiG0RLb2iRJIkTc9Q9OazAxFx\nUkTcGhG3RcQ5E3x/RkT8tCt3eF3Xd6dHxPeaz+ktLxHNAeadkiT1mVnKOaH/8s4ZuRMuM98FvAsg\nIk4F/jCzhff7SpKkgRIRw8AFwIuAe4CVEXFlZq4ZN+hlmfn6ceM+BngrcByQwHXNuPfPQNE1IMw7\nJUkS9GfeWVUJFxHnAqcDa4G7geuAzcBZwFZgTWYuGzfaK4CP1kxXkiTVi9l5NOBZwG2ZeUcpQ1wK\nvAwYnwxN5CXAFzoVKhHxBeAkzCvmBfNOSZIG0yzlnNCHeee0l0REHAssA44CTgae2Xx1DnB0Zh5J\nSYq6x9mNUuhPbifumRGxKiJWfezSi6ZbPEmSNEu6z+XN58yur59MqUDpuKfpN95/jIgbI+ITEXHA\nTo6rOaYXeac5pyRJg20HOSf0Yd5ZcyfcCcDlmbkeICKubPrfCFwcEVcAV4wb51Tgq9t7JCAzVwAr\nAG6+/d6sKJ8kSdqeHr0uvvtcPk3/Anw0MzdFxO8BHwZe0ErhNKhazzu7t9M1t/3QnFOSpF7p35wT\nZjjv7MU9gadQnrk9hvK8bXdF3zJ8JECSpL4QQ0M9+ezAvcABXd37N/1GZebPM3NT03khcOxUx9W8\nY94pSVKfm6WcE/ow76yphLsWOC0iFkfEnpSrjUPAAZl5NXA2sATYAyAilgDPB/65rsiSJGmArQSe\nHhEHRcQiSkXJld0DRMQTuzpfCtzc/P854MURsU9E7AO8uOmnuc+8U5Ik7ay+yzun/ThqZl4fEZcB\nqykN5K6kvDHioibxCeD8zFzXjPJy4POZ+XBlmSVJUhuiN48GbE9mbo2I11OSmGHgA5l5U0S8DViV\nmVcCb4yIl1Ia278POKMZ976IeDsl5wB4m2+9nB/MOyVJGmCzkHNCf+adVW9HzczzgPMAImJ56ZXH\nTzLsh4AP1UxPkiQNvsy8CrhqXL8/6/r/j4E/nmTcDwAf6GkB1ZfMOyVJ0s7qt7yzqhJOkiQNsKm1\npSFJkiRNnznnqNYq4TJzeVuxJEmSpMmYd0qSpEHU13fCLWBr1fgjMVxdhs3Du1bHgHbKMsS26hib\no35+jv32pdUxrjtiWXWM51x3YXUMgL0W1jcXM9zCutmW9dtIG+VIZud5/Ym0sd+0IcjqGG0s1zaO\nR23MC0BG/dWshWypjjGU9dt8G9tZW8t1xs1S+xxSvxmKkeoYmfX7UxvH1oj649EGdq+OAbCgheP8\ns1Z/pDrGN3/51X1RjkXDm6tjtKWNba1fDOw5eBJt5IzZwvl969DC6hj9YmHUH4ugneN8GwZy/zXn\nHNXXlXCSJKl3pvhqd0mSJGnazDnHuCQkSZIkSZKkHvNOOEmS5qtBfJxBkiRJg8Wcc5RLQpIkSZIk\nSeox74STJGm+GrKRXEmSJPWYOeeoGamEi4glwEXAU5pp/lVmfnAmpi1JkiYWPhqgOci8U5Kk/mLO\nOWamlsQfAGsy85eBE4G/johFMzRtSZIkzR/mnZIkqS9V3QkXEecCpwNrgbuB64DNwFnAVkoCtAxI\nYM+ICGAP4L7me0mSNFt8NEADxLxTkqQBZc45atp3wkXEscAy4CjgZOCZzVfnAEdn5pGUpAjgvcBh\nwA+BbwNvysyRSeKeGRGrImLVpZdeMt3iSZIkaY7oRd7ZnXN+7NKLez0LkiRJVXfCnQBcnpnrASLi\nyqb/jcDFEXEFcEXT7yXADcALgIOBL0TEVzLzwfFBM3MFsALge7f/ICvKJ0mStsf2OTQ4Ws87u3PO\nW26/x5xTkqReMecc1YslcQpwAXAMsDIiFgCvAT6VxW3A94FDezBtSZI0VRG9+Ugzx7xTkqR+Z845\nqqYS7lrgtIhYHBF7Aqc28Q7IzKuBs4EllLY47gJ+HSAiHg8cAtxRU3BJkiTNG+adkiRp4E37cdTM\nvD4iLgNWUxrIXUlpCPei5tXwAZyfmesi4u3AhyLi203/szPzZ/XFlyRJ0zbkowEaDOadkiQNMHPO\nUVVvR83M84DzACJieemVx08w3A+BF9dMS5IkSfOXeackSRp0VZVwkiRpgNlIriRJknrNnHNUa5Vw\nmbm8rViSJEnSZMw7JUnSIOrrO+FGKl/euii3VpchaeeNG9nCmzv2fuiH1TF+vtdTqmPssunB6hjP\nue7C6hhfP/Z11TEAlt58TXWMBSObq2NsGdqjOsaikY315RjepTpGWyJztosAwC5b11fH2LCwfv0G\n/bE8AIZHttQHaeGC2HALx/ktUb/NL8xN1TFmxdBgvlVKatu2HK6O0cbxaIT6csDC6gj7bbirhXLA\nA4sfXx1jlw33V8d41uqPVMf45i+/ujrGE2/6WnWMNn5TQHu/cTQmop08bWhkW3WMdo4l9bKFu5+G\nsn559FMOvS3qq2D6aX6mzJxzVF9XwkmSpB7y0QBJkiT1mjnnKJeEJEmSJEmS1GPeCSdJ0nzV0mNN\nkiRJ0qTMOUd5J5wkSZIkSZLUYzNyJ1xE7AN8ADgY2Aj8bmZ+ZyamLUmSJjHktTjNPeadkiT1GXPO\nUTO1JP4EuCEzjwR+B3jPDE1XkiRNJqI3H2l2mXdKktRPzDlHVd0JFxHnAqcDa4G7geuAzcBZwFZg\nTWYuAw4H3gGQmbdExNKIeHxm/qRm+pIkSZofzDslSdKgm3YlXEQcCywDjmriXE9Jhs4BDsrMTRGx\ndzP4auC3gK9ExLOAA4H9AZMhSZJmi6+L14Aw75QkaYCZc46qWRInAJdn5vrMfBC4sul/I3BxRLyK\nclUSytXIvSPiBuANwLeAbRMFjYgzI2JVRKy67NKLK4onSZKkOaL1vLM75/z4pRf1fg4kSdK814sX\nM5wCPA84FTg3Io5okqXXAEREAN8H7pho5MxcAawAuPX2u7MH5ZMkSWAjuZoLpp13duecN932I3NO\nSZJ6xZxzVM2SuBY4LSIWR8SelORnCDggM68GzgaWAHtExN4RsagZ73XAtU2CJEmSZouN5GpwmHdK\nkjSozDlHTftOuMy8PiIuo7S7sRZYCSRwUUQsAQI4PzPXRcRzgA9HRAI3Aa+tL7okSZLmA/NOSZI0\nF1Q9jpqZ5wHnAUTE8tIrj59guK8Dz6iZliRJapmN5GqAmHdKkjSgzDlHuSQkSZIkSZKkHmvtxQyZ\nubytWJIkaQYMaFsaknmnJEkDxJxzlHfCSZIkSZIkST3W2p1wvfCk+2+qGv/WPZ9dXYYtI8PVMQCe\nsGhtdYyhkS3VMTaOLK6OcfOiY6pj7LXw4eoYS2++pjoGwJ2HnVgd48Qv/0V1jO/td0J1jF1iQ3WM\nfrKR+u11KEeqYyzaUr+9PrDgsdUxNscu1TEWsLU6BsBw1Md5wr9dXF+QpfXNPt36pBdWx3j8yAPV\nMWaFr4uXADjggRurY6zZ/VeqYyyI+nPW3kPrqmMMbavPOQG2sGjHA+3ALS0s10XDm6tjPPGmr1XH\n+NF/+NXqGAd+64PVMQB+uOvB1TH2on5ba+OX1ubYtTpGUn+XzoJsZ79ZOLKpOsaG4T2qYzw8tFd1\njCCrYwzHtuoYB/zgy9UxADbsd1B1jHsXP706xpK8rzrGjDPnHNXXlXCSJKl30kcDJEmS1GPmnGOs\njpQkSZIkSZJ6zDvhJEmar3xdvCRJknrNnHOUS0KSJEmSJEnqsVYr4SLi0Ij4ekRsioi3jPvupIi4\nNSJui4hz2pyuJEmahhjqzUfqMXNOSZIGiDnnqLYfR70PeCNwWnfPiBgGLgBeBNwDrIyIKzNzTcvT\nlyRJU2QjuRpg5pySJA0Ic84xU6qEi4hzgdOBtcDdwHXAZuAsYCuwJjOXZeZaYG1EnDIuxLOA2zLz\njibepcDLABMiSZIkAeackiRpbtvh/XsRcSywDDgKOBl4ZvPVOcDRmXkkJTHanidTEqmOe5p+E03v\nzIhYFRGrPvipq3ZUPEmSNF0+GqA+Ys4pSdIcZc45aiqlPgG4PDPXZ+aDwJVN/xuBiyPiVZQrk63I\nzBWZeVxmHvea3zq5rbCSJKlP7KjNroj4HxGxJiJujIgvRsSBXd9ti4gbms+V48fVQDPnlCRJreq3\nvLOm6vAUSpsbx1Da29jeo633Agd0de/f9JMkSbMlojef7U5ytM2u3wAOB14REYePG+xbwHHNnU+f\nAP6y67sNmXlU83lpewtDfcycU5KkQTYLOWeZbP/lnVOphLsWOC0iFkfEnsCpzXgHZObVwNnAEmCP\n7cRYCTw9Ig6KiEWURw28ei1J0mwaGurNZ/tG2+zKzM1Ap82uUZl5dWaubzq/QalI0dxnzilJ0lw0\nOzkn9GHeucMXM2Tm9RFxGbCa0kjuSiCBiyJiCRDA+Zm5LiKeAKwC9gJGIuLNwOGZ+WBEvB74HDAM\nfCAzb+rNLEmSpD42UZtdz97O8K8FPtPVvWtErKI8lviOzLyi/SJqNphzSpKklvVd3jmlt6Nm5nnA\neQARsbz0yuMnGO7HTFJrmJlXAbZ6K0lSn+jV6+Ij4kzgzK5eKzJzxTTivAo4Dnh+V+8DM/PeiHgq\n8KWI+HZm3l5XYvULc05Jkuaefs85m1gzkndOqRJOkiRpqprkZ7IEaEptdkXEC4Fzgedn5qau2Pc2\nf++IiGuAowEr4SRJkuaZHeSc0Id5505XwmXm8poJSpKkPjE7r3YfbbOLkgQtA175iGJFHA28Hzgp\nM9d29d8HWJ+ZmyJiX+C5PLLxXM0h5pySJM0Rs5NzQh/mnX19J9wDe9W1h7fX8EPVZRipeoHsmA25\nW3WM9bvtWx1jYWypjvG4RT+rjjHMtuoYC0Y2V8cAOPHLf1Ed45rn/3F1jOdc/4/VMXa795bqGPc+\n4wXVMTLbud24je0kqS/L5oW7V8cYYqQ6xi65oTpGkNUx2rLt8OOqY4x8+XPVMR5z2tHVMfb5UUtN\nTh1yZDtx+lhmbp2oza6IeBuwKjOvBN5FaXz/41EeX7ireSPVYcD7I2KE0mD/OzJzzazMiOaU+5cs\nrY7x2FhXX5AWZAu568OLH9tCSWCYrdUxHr/gxy2UpF4bj1Id+K0PVsf4xtGvqY4B8NyV76+OsXBD\n/Tb/48fXn/eGs347a2P9jmQ7vxvbyF0j6vO9vbbeVx1jJIarY0TW59D37X9UdQyAvT73T9Ux9nvR\nK3c80A7s/tCPqmMUh7UUp3/1Y97Z15VwkiSpd3KWrkpO1GZXZv5Z1/8vnGS8rwFH9LZ0kiRJatNs\n5ZzQf3mnlXCSJM1XPWokV5IkSRplzjlq9qojJUmSJEmSpHnCO+EkSZqnZvPRAEmSJM0P5pxjXBKS\nJEmSJElSj7VaCRcRh0bE1yNiU0S8Zdx3H4iItRHxnTanKUmSpimiNx+px8w5JUkaIOaco9q+E+4+\n4I3AX03w3YeAk1qeniRJmq4Y6s1H6j1zTkmSBoU556gptQkXEecCpwNrgbuB64DNwFnAVmBNZi7L\nzLXA2og4ZXyMzLw2Ipa2VG5JkiTNMeackiRpLtth1WFEHAssA44CTgae2Xx1DnB0Zh5JSYxaERFn\nRsSqiFh18WUfbyusJEkaJyN68pGmYzZzzksuvaytsJIkaRxzzjFTuRPuBODyzFwPEBFXNv1vBC6O\niCuAK9oqUGauAFYA3PPd72RbcSVJktTXZi3n/MFtt5pzSpKknqt5iPYU4ALgGGBlREzp0VZJktQn\nbJ9Dg8GcU5KkQWbOOWoqpb4WOC0iFkfEnsCpzXgHZObVwNnAEmCP3hVTkiS1LYmefKRpMueUJGkO\nMuccs8MriZl5fURcBqymNJK7EkjgoohYAgRwfmaui4gnAKuAvYCRiHgzcHhmPhgRHwVOBPaNiHuA\nt2bmP/ZkriRJkjRQzDklSdJcN6Xb+TPzPOA8gIhYXnrl8RMM92Ng/0livGL6xZQkSW3LAb2NX3OX\nOackSXOPOecYl4QkSZIkSZLUYzvdsG1mLu9BOSRJ0kzzqqT6mDmnJElzhDnnqL5+u9TG4d2rxl+U\nG6vLsG2onUW0rYVF/eDCx1bHGI6t1THasC2Hq2NsGWqnXebv7XdCdYznXF/f1MzXj3ltdYznrnx/\ndYzIrI9BfQyAiBbK0sL8PLxgSXWMRWyqjjES9ftNRv80YPqDfY6pjrHXyw6qjvH47365OsZDS4+q\njgGwuJUoknbW5ti1OsYCtlTHiBypjtFG7vrwcP15D2Ahm6tjtNHwdhuPQbVRjh/uenB1jDZyPYCv\nPvP3qmM864Z/qo7RRp7WRm7TRjnaaiO+jf1vYdbve1uGdqmO0ca+N9JCjI0jLWVYL/md6hCPvXNV\ndYx1T2kn79yzlSjaWX1dCSdJknqnnyplJUmSNDeZc46xEk6SpHnKRnIlSZLUa+acY1wSkiRJkiRJ\nUo95J5wkSfOVjwZIkiSp18w5R3knnCRJkiRJktRjrVbCRcShEfH1iNgUEW/p6n9ARFwdEWsi4qaI\neFOb05UkSTsvY6gnH6nXzDklSRoc5pxj2n4c9T7gjcBp4/pvBf5nZl4fEXsC10XEFzJzTcvTlyRJ\nU5T4aIAGljmnJEkDwpxzzJQq4SLiXOB0YC1wN3AdsBk4i5LsrMnMZZm5FlgbEad0j5+ZPwJ+1Pz/\nUETcDDwZMCGSJEkSYM4pSZLmth3evxcRxwLLgKOAk4FnNl+dAxydmUdSEqMpiYilwNHAv0/y/ZkR\nsSoiVl166UenGlaSJO0kHw1QP5ndnPOSipJLkqTtMeccM5U74U4ALs/M9QARcWXT/0bg4oi4Arhi\nKhOLiD2ATwJvzswHX1DFsQAAIABJREFUJxomM1cAKwBuu/37OZW4kiRJGnizlnN+7/YfmHNKkqSe\nq6k6PAW4ADgGWBkR263Qi4iFlGTo4sz8VMV0JUlSGyJ685HaZc4pSdIgM+ccNZVKuGuB0yJicdPA\n7anNeAdk5tXA2cASYI/JAkREAP8I3JyZf1NfbEmSVCsZ6slHmiZzTkmS5iBzzjE7fBy1ebvUZcBq\nSiO5K4EELoqIJUAA52fmuoh4wv/P3r2Hy1XXd99/f/dONgmQAAICCharKIdKgwSqT0GpYqsISuuh\nsVdt6EOb+lxtbetNL7X0vu+oTatWqo/Vp3fTWrUekigtNlYpVYpSW5GESDifEhREDoqAkgRIMt/n\nj1lxhu3eyez81tp7Zvb7dV1zZbJmzWf9ZvbM7M9esw7AemAh0IqIPwSOB04E3ghcHxHXVtF/kplf\nrP0RSZIkaeDYOSVJ0rDr6eyombkCWAEQEcvbk/K0Cea7DzhygoivgeeklSSpn+SAbsav4WXnlCRp\n+Ng5OwZz+z1JkiRJkiRpgPS0JVy3zFzewDgkSdI0G9RTu2t2sHNKkjQc7JwdU14JN51KD7TXitHi\nMYy2dhRnAIxST06pFuXPSR2bko6yszhjrPVYcQbAPrGtOGPfe24pzvj5dX9bnPFfp/xOccaxt15a\nnFGXyJzpIQD1vH9bffKLp1+eU4CF+VBxxsHfu7k440dHLyrOiNXl718A3nlqPTk9SvfakwAYifJe\nQg0fr3V01zrMy6215Ozc/YlsezJMf7gt5OHijLnbyjMATr32H4szrl70G8UZx91SfrjGOrpNP+0q\nV8f7r473Xr8YyVZxxr6xpYaRwIJH7yvOeOSoE8sH8pGLyjMA/vKT9eT0wM7ZMTy/1SRJkiRJkqQ+\nNTyryCVJ0pQM0xYmkiRJ6k92zg6fCUmSJEmSJKlhbgknSdIs1U/HwJEkSdJwsnN21LolXEQcGxFf\nj4jHI+KCrunzIuLqiNgYETdGxDvqXK4kSZJmDzunJEkaRHVvCfcD4M3AueOmPw68JDMfjYi5wNci\n4tLMvKrm5UuSpB55pioNMDunJEkDws7Z0dNKuIi4EFgKPADcDVwDPAG8CdgB3JSZSzLzAeCBiHhl\n9/0zM4FHq//OrS41nMhdkiTtLQ+Sq35j55QkafjYOTv2uBIuIk4GlgCLqvk30C5EbwOemZmPR8SB\nPeSMVvd7NvDhzPxGycAlSZI0POyckiRp2PWyOvJ04JLM3JqZPwTWVtOvAz4VEb9O+5vJ3crMnZm5\nCDgSODUifmai+SJiWUSsj4j1q1d/urdHIUmSpiyJRi7SXpq5zrlqVU0PQZIkjWfn7Cg5JtwrgRcB\n5wAXRsTzMrOXYvRwRFwBvBy4YYLbVwIrAW7f9G13H5AkSZrdGu+cmzZvtnNKkqTG9bIl3JXAuREx\nPyIW0C5AI8BRmXkF8FbgAGD/yQIi4tBduw9ExHzgZcAtpYOXJEl7L2OkkYu0l+yckiQNITtnxx63\nhMvMDRGxBthI+yC562gf4PaTEXEAEMAHq28bDwfWAwuBVkT8IXA8cATw8eoYHSPAZzLzXxt5RJIk\nqSeDuhm/hpOdU5Kk4WTn7Ohpd9TMXAGsAIiI5e1JedoE891H+/gb410HnLT3w5QkSdKws3NKkqRh\nNpjb70mSpGIztWtARLw8Im6NiDsi4m0T3L5PRKypbv9GRBzdddvbq+m3RsQv1fqESJIkqXYzuTtq\nv/XOKZ+YITOX17FgSZI0+1S7CX6Y9rG6vgOsi4i1mXlT12znAw9l5rMjYgnwHuBXI+J4YAlwAvA0\n4MsR8ZzM3Dm9j0LTwc4pSZJK9GPvLDk7auNG93wW+t3KKN/veGfU8xRFlp90q47HU4daHksN+4Tv\nGBnrm+fknue8pDijjuf12FsvLc4AuOW5r6glp9Rzb/m34ox5O7cUZzwxOr84o46fL0CrcAPmuo7H\n8NDSNxRnHPPXf16cce9h5XudjdAqzmDpn9TyeXRI+UimZIaOz3EqcEdmbgaIiNXAq4HuMvRqYHl1\n/WLgQxER1fTVmfk4cGdE3FHlfX2axq4hNZLlnwM7Ga1hJOVqeSwxp/j3DUBE+e++Vg7Pjjt1vEK+\nf+hx7BiZW5xTRy857pYvFmfcfOxZxRmfeftXizOefVw9v4GXLr69OGPLyMLijHlsK87YFvsVZ8wp\n/HseyrvvLodu+VZxxvcP+OnijDr6YrzpHcUZ020GjwnXd71zeH6radbplxVww6ZfVsDpJ9VVQlS/\nQf08yohGLhGxLCLWd12WdS326cDdXf//TjWNiebJzB3AI8DBPd5XUiF/3/SvOlbAqRl1rIBTM+pY\nAacyM9Q5oQ97Z19vCSdJkgZPZq4EVs70OCRJkjS8BrFzuhJOkqRZKnNGtuC7Bziq6/9HVtMmmuc7\nETEHOAB4sMf7SpIkqY/MUOeEPuydbmsuSZKm0zrgmIh4ZkSM0T7g7dpx86wFllbXXwv8R2ZmNX1J\ndRarZwLHAFdP07glSZI0WPqud7olnCRJs1TOwHdxmbkjIn4PuIz2ccr/ITNvjIh3Auszcy3wEeAT\n1QFwf0C7MFHN9xnaB9PdAfyuZ0aVJEnqbzPROaE/e+e0rISLiBcBHwBOBJZk5sXTsVxJktR/MvOL\nwBfHTftfXdcfA143yX1XACsaHaAGmr1TkiTt0m+9s/GVcNU+tXcB5wEXNL08SZLUmxk8XbzUCHun\nJEn9x87ZUbwSLiIupL3/7AO0T996DXA2cC1wGrAqMy+q5m2VLk+SJNXDQqRBY++UJGnw2Dk7inbM\njYiTae8vuwg4Czil6+axzFy8qwhNIXNZRKyPiPWrVq8uGZ4kSZKGRN29084pSZKmW+mWcKcDl2Tm\nVoCI6D7LxJq9CczMlcBKgM2bNmXh+CRJ0iT8VlIDptbe2d0579x0h51TkqSG2Dk7mjxFxZYGsyVJ\nkqRd7J2SJKnvla6EuxI4NyLmR8QC4JwaxiRJkqZBEo1cpIbYOyVJGkB2zo6i3VEzc0NErAE20j5A\n7rqJ5ouIU4BLgIOAcyLiHZl5QsmyJUlSmczBLC+aneydkiQNJjtnR/HZUTNzBbACICKWV9POGDfP\nOuDI0mVJkiRp9rJ3SpKkQVa8Ek6SJA2mQd2MX5IkSYPDztlR60q4zFxeZ54kSZI0EXunJEkaNG4J\ntweRnrFee1bHPu6Br7XxIsqfk4wmTwKtIv5sZpzfSkptOxktzhhlZ3FGHe/JjPKM0Sx/LNA//ahf\nOtYTMa84YzR31DCSel4ndfyd9Jm3f7U44/V/8eLijBNvuqQ444E8vDgD4NDW/cUZT4yUv9b2bz1S\nnLEzylc31PGav2v+scUZAAv5YXHGbF2/YOfscCWcJEmzlIVIkiRJTbNzdrgZgiRJkiRJktQwt4ST\nJGmW8nTxkiRJapqds8Mt4SRJkiRJkqSGuSWcJEmzVMvjc0iSJKlhds6OadkSLiLeEhE3RcR1EXF5\nRPzUdCxXkiRJs4u9U5Ik9avGV8JFxBzgm8DizDwRuBh4b9PLlSRJu5dEIxdpptg7JUnqP3bOjuLd\nUSPiQmAp8ABwN3ANcDZwLXAasCozL+q6y1XAr5cuV5IklfEguRo09k5JkgaPnbOjaEu4iDgZWAIs\nAs4CTum6eSwzF48rQgDnA5fuJnNZRKyPiPWrVq8uGZ4kSZKGRN29s7tzrl69qpExS5IkdSvdEu50\n4JLM3AoQEWu7blszfuaI+HVgMfDiyQIzcyWwEmDzpk1ZOD5JkjSJQd2MX7NWrb2zu3PeselOO6ck\nSQ2xc3Y0eXbULd3/iYgzgQuBF2fm4w0uV5IkSbOLvVOSJPW90hMzXAmcGxHzI2IBcM5EM0XEScDf\nAq/KzAcKlylJkmqQGY1cpIbYOyVJGkB2zo6iLeEyc0NErAE20j5A7rpJZv1LYH/gsxEBcFdmvqpk\n2ZIkqYy7BmiQ2DslSRpMds6O4t1RM3MFsAIgIpZX084YN8+ZpcuRJEnS7GbvlCRJg6zJY8JJkqQ+\nNqib8UuSJGlw2Dk7al0Jl5nL68yTJEmSJmLvlCRJg6avt4TblvsW3X/fJ58oa+8ynnikOANgy9iB\nxRmjrR3FGRml5+KAkdxZnNGK0eKMyCzOAHiM+cUZo5Q/JxHlj6eO5+S5t/xb+ThqeCwAtzz3FcUZ\nL/jmR4sztuxzUHHGjpGx4oz9tz9cnPHYnP2KMwAO/vinijNu33FwccYB/LA4Y4RWccagmr2PXKpf\nHce7Ccp/f/bTcXcyyscyksPzSVXHz6aO5xTq6Yx1jOXZxx1SnHHiTZcUZ1x3/C8XZ5y68RPFGQDb\n5i4ozqiri5eq4zOtjr8baxhGO6aG13y/vPem2/B8kpfr65VwkiSpOe4aIEmSpKbZOTvKN4uSJEmS\nJEmStFtuCSdJ0izVT7utSZIkaTjZOTvcEk6SJEmSJElq2LRsCRcRbwJ+F9gJPAosy8ybpmPZkiRp\nYh6fQ8PI3ilJUn+xc3Y0viVcRMwBPp2Zz8vMRcB7gb9qermSJEmaXeydkiSpnxVvCRcRFwJLgQeA\nu4FrgLOBa4HTgFWZeVHXXfajtpMES5KkveXxOTRo7J2SJA0eO2dH0Uq4iDgZWAIsqrI20C5DAGOZ\nubhr3t8F3gKMAS8pWa4kSSrXctWEBoi9U5KkwWTn7CjdHfV04JLM3JqZPwTWdt22pnvGzPxwZj4L\neCvwp5MFRsSyiFgfEes/u/qThcOTJEnSkKi1d3Z3ztWrVzU2aEmSpF2aPDHDlkmmrwb+ZrI7ZeZK\nYCXAjXfc6/pSSZIa4q4BGiJT7p3dnfOOTXfaOSVJaoids6N0S7grgXMjYn5ELADOmWimiDim67+v\nBG4vXK4kSZJmF3unJEkaaEVbwmXmhohYA2ykfYDcdZPM+nsRcSawHXiI9gF1JUnSDPJ08Rok9k5J\nkgaTnbOjeHfUzFwBrACIiOXVtDPGzfMHpcuRJEn1SnfA04Cxd0qSNHjsnB2lu6NKkiRJkiRJ2oNa\nT8yQmcvrzJMkSc1peZBcDTB7pyRJg8HO2eGWcJIkSZIkSVLDat0Srm77xGNF96/jNLhbxw4ozgAI\nyneC3hFzizNGaJWPY6R8HP1kJMufkzpea9EnO8rP27mlOCOjnvX7L/jmR4szrjrpN4szjrnlS8UZ\nT79/fXHGg4ceV5yxc6Sej/2xnWWfzwBPHb2/OOMx9i3OmM08SK7UVkc/yih/P9XRJ/ql10A93WYn\nozWMpFxE+WOZk9uLM1pZ0zYUffLxv3Rx+cmLH8jDizNO3fiJ4oyrf/aNxRkAx956aXHGwm3fK854\nZN5TizP6xQHxSC05O7P886iWz9f++LNxSuycHW4JJ0nSLJXZzKVERDwlIr4UEbdX/x40wTyLIuLr\nEXFjRFwXEb/addvHIuLOiLi2uiwqG5EkSZJK2Dk7XAknSZL6yduAyzPzGODy6v/jbQV+IzNPAF4O\nfCAiDuy6/Y8zc1F1ubb5IUuSJGnAzEjndCWcJEmzVBKNXAq9Gvh4df3jwLk/Me7M2zLz9ur6d4EH\ngENLFyxJkqT62Tk7XAknSZJqFRHLImJ912XZFO5+WGbeW12/DzhsD8s6FRgDNnVNXlHtMvD+iNhn\naqOXJEnSIBjEzjmtJ2aIiNcAFwOnZGb5UcolSdJeazV0YN/MXAmsnOz2iPgyMNGRtC8cl5Oxm6Oh\nR8QRwCeApZk/Phr922kXqbFqDG8F3jmlB6ChYO+UJKk/2Dk7Gl8JFxFzMnNHRCwA/gD4RtPLlCRJ\n/Sszz5zstoi4PyKOyMx7q8LzwCTzLQS+AFyYmVd1Ze/6RvPxiPgocEGNQ1efs3dKkqRd+rFzFu+O\nGhEXRsRtEfG1iFgVERdExFci4gMRsZ52AQJ4F/Ae4LHSZUqSpHKZ0cil0FpgaXV9KfAv42eIiDHg\nEuAfM/PicbcdUf0btI/tcUPpgNQ/7J2SJA0eO2dH0Uq4iDgZWAIsAs4CTum6eSwzF2fmRRHxfOCo\nzPxCyfIkSVJ9+vF08cC7gZdFxO3AmdX/iYjFEfH31TyvB14EnDfBaeE/FRHXA9cDhwB/Vjwi9QV7\npyRJg8nO2VG6O+rpwCWZubUa7Nqu29ZU00aAvwLO6yWwOpDeMoB3/dkKlix5Q+EQJUnSoMjMB4GX\nTjB9PfBb1fVPAp+c5P4vaXSAmkm19s7uzvlnf/ZnvGHJkrrHK0mS+tRMdc4mjwm3pfp3AfAzwFfa\nW+lxOLA2Il410UFyuw+sd8emOxs6fJ8kSWqVn9pd6hdT7p3dnXPzpk12TkmSGmLn7Cg9JtyVwLkR\nMb86AO4542fIzEcy85DMPDozjwauAiZcASdJkiRNwt4pSZIGWtGWcJm5ISLWABtpn0liXS2jkiRJ\njavhWBrStLF3SpI0mOycHcW7o2bmCmAFQEQsr6adsZv5J71NkiRNnxrOKiVNK3unJEmDx87ZUbo7\nqiRJkiRJkqQ9qPXEDJm5vM48SZLUnJa7BmiA2TslSRoMds6OJs+OWiwLN9Qbye3FY2jFaHEGQKuG\njQ6fdk/5oU/uf/rzizOo4Q0UNYTss2Nr+UCAse1b9jzTHjwxd7/ijC1zDijOGGVHccYTo/OLM+qy\nZZ+DijOOueVLxRm3H/uy4oy45d+LM+bm4+XjqOmADHN3lo9l3raHijNG93tqccajowcWZ8zlieIM\nSTOnjp42mjuLM7KGs8e1ovyxHLj1vuIMgIf3Pbw4o1+e15FW+Tjmtsp/d9bxWAC2jJb3znlZ3sW3\njCwszji0dX9xxra5C4ozjr310uIMgFue+4rijBNu/nxxxpwa/pZ+LPYtzjhk293FGSM1fI4APDZW\n/np9dE5575zXKv/7VTOnr1fCSZKk5niQXEmSJDXNztnhSjhJkmapuraokCRJkiZj5+zwxAySJEmS\nJElSw9wSTpKkWcqD5EqSJKlpds4Ot4STJEmSJEmSGjYtK+Ei4ryI+F5EXFtdfms6litJkiaX2cxF\nmkn2TkmS+ouds6Px3VEjYtcy1mTm7zW9PEmSJM1O9k5JktTPilfCRcSFwFLgAeBu4BrgbOBa4DRg\nFfBg6XIkSVK9BvUbRM1e9k5JkgaPnbOjaHfUiDgZWAIsAs4CTum6eSwzF2fmRdX/XxMR10XExRFx\n1G4yl0XE+ohYv3r1p0uGJ0mSdqOV0chFakLdvfPJnXNVs4OXJGkWs3N2lG4JdzpwSWZuBYiItV23\nrem6/nlgVWY+HhG/A3wceMlEgZm5ElgJcPumb7u+VJIkSVBz7+zunHdsutPOKUmSGtfkiRm27LqS\nmQ9m5uPVf/8eOLnB5UqSpB54kFwNEXunJEl9ys7ZUboS7krg3IiYHxELgHMmmikijuj676uAmwuX\nK0mSpNnF3ilJkgZa0e6ombkhItYAG2kfIHfdJLO+OSJeBewAfgCcV7JcSZJUblC/QdTsZO+UJGkw\n2Tk7is+OmpkrgBUAEbG8mnbGuHneDry9dFmSJKk+LQuRBoy9U5KkwWPn7GjymHCSJEmSJEmSqGFL\nuG6ZubzOPEmS1Jwc0FO7S2DvlCRpUNg5O2pdCVe3oFV0/1aM1jCGerabHCl8LAD3Pb385F5zWk8U\nZ2wf3ac4Iyl/E26bu39xBsAjcw4uzqjj5zvG43ueaQ9aUb5xa/TRDvs7RsaKM55+//rijLjl34sz\nbjv2F4szTrj588UZ2yl/TgG2jB1QnPHgnMOLM0ZjZ3HGPjxWnLEzy3/fSJo5EeW/+/rlD4w6fo8/\nMv+wGkYCI1nej+rojBnlGS3KP+e3jZZ31zpeqwBzs/xvgp1R/qfkPLYVZzwxMq84o47ndeG27xVn\nQD1978bjJjxvzZQcf8sXijNGKe9pP5p/aHHG45S/RgDmRvn7Zk5uL86o428kzZy+XgknSZKa00fr\n3CVJkjSk7JwdroSTJGmW8iC5kiRJapqds8MTM0iSJEmSJEkNc0s4SZJmKXcNkCRJUtPsnB1uCSdJ\nkiRJkiQ1bNpWwkXE6yPipoi4MSI+PV3LlSRJE8ts5iLNNHunJEn9w87Z0fjuqBExB3gm8Hbg5zPz\noYh4atPLlSRJ0uxi75QkSf2seCVcRFwILAUeAO4GrgHOBq4FTgNWAYcBH87MhwAy84HS5UqSpDKe\nqUqDxt4pSdLgsXN2FO2OGhEnA0uARcBZwCldN49l5uLMvAh4DvCciPiviLgqIl6+m8xlEbE+Itav\nXr2qZHiSJGk33DVAg6Tu3vmkzrnKzilJUlPsnB2lW8KdDlySmVsBImJt121rxi3nGOAM4Ejgyoh4\nXmY+PD4wM1cCKwHu2HTngD6tkiRJqlmtvbO7c27avNnOKUmSGtfkMeG2dF3/DvCNzNwO3BkRt9Eu\nR+saXL4kSdqNVmumRyDVxt4pSVKfsnN2lJ4d9Urg3IiYHxELgHMmme9ztL+NJCIOob2bwObCZUuS\nJGn2sHdKkqSBVrQlXGZuiIg1wEbaB8id7BvGy4BfjIibgJ3AH2fmgyXLliRJZQb1WBqaneydkiQN\nJjtnR/HuqJm5AlgBEBHLq2lnjJsngbdUF0mS1AcsRBo09k5JkgaPnbOjdHdUSZIkSZIkSXtQ64kZ\nMnN5nXmSJKk5Lb+V1ACzd0qSNBjsnB1Nnh21WETZT6qV5Rv6zcntxRkArZHypzpq2IZz++g+xRnz\ntz9anPHE6LzijKCed/ITUf6c7JPbijNaMVqcUYdWH20gu//2h4szHjz0uOKMufl4ccYJN3++OOPG\n4yY7BnnvTr5+dXEGwM4aPtPu3XFYccaRY/cUZ9Tx3hvBUz5Jg6yOjpURfTGOOn6P19V/6/hdUUfv\n3DEytzijDltGFhZnLNzxgxpGAttHyvtvHbbFfsUZ+7ceqWEk5R6Z99Racup4/x1/yxeKM2469pXF\nGadu/ERxxtzHyz8Dbp63uDgD4OgoP8dPHX+Pa7D19Uo4SZLUnGzsAB3lKyMkSZI0HOycHa6EkyRp\nlvIguZIkSWqanbOjf/Y7kyRJkiRJkoaUW8JJkjRLtTyUnSRJkhpm5+yYli3hIuL9EXFtdbktIsqP\nti5JkoZORDwlIr4UEbdX/x40yXw7u7rF2q7pz4yIb0TEHRGxJiLGpm/06gf2TkmStCcz1TkbXwkX\nEXMy848yc1FmLgL+GvjnppcrSZJ2L7OZS6G3AZdn5jHA5dX/J7JtV7fIzFd1TX8P8P7MfDbwEHB+\n8Yg0MOydkiT1HztnR/FKuIi4sPqW8WsRsSoiLoiIr0TEByJiPfAH4+7yBmBV6XIlSdJQejXw8er6\nx4Fze71jRATwEuDivbm/+p+9U5Ik1WRGOmfRMeEi4mRgCbCoytoAXFPdPJaZi8fN/1PAM4H/KFmu\nJEkq1+rPM1Udlpn3VtfvAw6bZL551UqXHcC7M/NzwMHAw5m5o5rnO8DTGx2tpo29U5KkwWTn7Cjd\nEu504JLM3JqZPwTWdt22ZoL5lwAXZ+bOyQIjYllErI+I9atX+cWlJElNaWrXgO7f5dVlWfdyI+LL\nEXHDBJdXP3l8mcBkte2nqpUuvwZ8ICKe1cyzpD5Sa+/sfp2uWr26geFKkiSwc3Zr8uyoWyaYtgT4\n3d3dKTNXAisBNm3e3J/rSyVJ0qS6f5dPcvuZk90WEfdHxBGZeW9EHAE8MEnGPdW/myPiK8BJwD8B\nB1bHBdsBHAncs/ePRANkyr2z+3W6edMmO6ckSQNmEDtn6ZZwVwLnRsT8iFgAnDPZjBFxLHAQ8PXC\nZUqSpBpkKxu5FFoLLK2uLwX+ZfwMEXFQROxTXT8E+HngpupbzCuA1+7u/hpY9k5JkgaQnbOjaCVc\nZm6gvfn/RuBSYN1uZl8CrK4GK0mSNJF3Ay+LiNuBM6v/ExGLI+Lvq3mOA9ZHxEbaBejdmXlTddtb\ngbdExB20j9fxkWkdvRpj75QkSTWakc5ZvDtqZq4AVlSDXV5NO2OC+ZaXLkuSJNWnHw+Sm5kPAi+d\nYPp64Leq6/8NPG+S+28GTm1yjJo59k5JkgaPnbOjyWPCSZKkPuY2QpIkSWqanbOj1pVwfusoSZKk\n6WDvlCRJg8Yt4SRJmqVa/bhvgCRJkoaKnbNjqFfCRZT/oFuM1jCSeozkzuKMnVH+I98+uk9xRtA/\nb8I57CjOqOPxZET5OGrYzjcpH0ddHpuzX3HGzpHy13wdz+t2xoozTr5+dXHGNc9bUpwBcNINny3O\n2H/u1uKMWj4Xa3iNjGSrOEOS+kUr+qf/1vEZXYeMovPZAfX0xbp+NnU8njrU0cPr+Pumn/42eSz2\nLc4YpbwfnbrxE8UZV//sG4szfu6bHyvO2Gd0e3EGwM4aVp/U8R7ul/ev9k5//FaTJEnTzuNzSJIk\nqWl2zg5XwkmSNEtZiCRJktQ0O2eH2zFKkiRJkiRJDXNLOEmSZqmWX0tKkiSpYXbOjmnZEi4inhER\nV0TENyPiuog4azqWK0mSpNnF3ilJkvpV41vCRcQc4E+Bz2Tm30TE8cAXgaObXrYkSZqcJ3XVsLF3\nSpLUf+ycHcUr4SLiQmAp8ABwN3ANcDZwLXAasApIYGF1lwOA75YuV5IklUl3DdCAsXdKkjR47Jwd\nRbujRsTJwBJgEXAWcErXzWOZuTgzLwKWA78eEd+h/W3k7+8mc1lErI+I9atXrSoZniRJkoZE3b2z\nu3OuWr262cFLkiRRviXc6cAlmbkVICLWdt22puv6G4CPZeZFEfFC4BMR8TOZP7lRYmauBFYCbNq8\n2dWlkiQ1pOWuARostfbO7s65edMmO6ckSQ2xc3Y0eWKGLV3Xzwc+A5CZXwfmAYc0uGxJkiTNHvZO\nSZLU90pXwl0JnBsR8yNiAXDOJPPdBbwUICKOo12Gvle4bEmSVCAzG7lIDbF3SpI0gOycHUW7o2bm\nhohYA2ykfYDcdZPM+j+Av4uIP6J9sNzzclCfMUmSJE07e6ckSRp0xWdHzcwVwAqAiFheTTtj3Dw3\nAT9fuixJklT66iK2AAAgAElEQVSflqslNGDsnZIkDR47Z0fxSjhJkjSY0kYkSZKkhtk5O2pdCZeZ\ny+vMkyRJkiZi75QkSYPGLeEGyEjuLM7YWcOPPInyjCg/Me9oa3txBsBo7KglZ1g8tPQNMz2EHzv4\n458qzhjb+VhxxtydjxdnbBk7oDhj50j5+/ekGz5bnAHwzZ95XXHGqRs/UZyxZezA4ow61PG5OBM8\nSpakiYxmPd1oR8ytJacf1NHDR6M8I7JVnAHQqqGLj9QwllbxeQLreb22YrQ4oy6HbLu7OONH8w8t\nzpj7+KPFGT/3zY8VZ3zjpPOKMw6/8evFGVDPa76Ov4Pr+hyYTnbOjvJXgCRJkiRJkqTdcks4SZJm\nqZbH55AkSVLD7JwdroSTJGmWSvcNkCRJUsPsnB3ujipJkiRJkiQ1zC3hJEmapQbwuL6SJEkaMHbO\njmnZEi4ifioiLo+I6yLiKxFx5HQsV5IkSbOLvVOSJPWrxlfCRcQc4H3AP2bmicA7gb9oermSJGn3\nWpmNXKSZYu+UJKn/2Dk7indHjYgLgaXAA8DdwDXA2cC1wGnAKuB44C3VXa4APle6XEmSVMaD5GrQ\n2DslSRo8ds6Ooi3hIuJkYAmwCDgLOKXr5rHMXJyZFwEbgV+ppv8ysCAiDp4kc1lErI+I9atXrSoZ\nniRJkoZE3b2zu3OuWr264dFLkiSVbwl3OnBJZm4FiIi1Xbet6bp+AfChiDgPuBK4B9g5UWBmrgRW\nAmzavNnVpZIkNaTV8tesBkqtvbO7c27etMk3gyRJDbFzdjR5dtQtu65k5nepvpGMiP2B12Tmww0u\nW5IkSbOHvVOSJPW90hMzXAmcGxHzI2IBcM5EM0XEIRGxa1lvB/6hcLmSJKlQZjMXqSH2TkmSBpCd\ns6NoJVxmbqC9+f9G4FJg3SSzngHcGhG3AYcBK0qWK0mSpNnF3ilJkgZd8e6ombmCqtxExPJq2hnj\n5rkYuLh0WZIkqT7p8Tk0YOydkiQNHjtnR5PHhJMkSX2sNajb8UuSJGlg2Dk7al0Jl5nL68yTJEmS\nJmLvlCRJg6avt4Qbbe0oun8rRovHsJ2x4gyAqGHN74LtPyjO2Dayf3HGY8wvzpjL9uKM4tOKVA7/\n2qeKM3Yev7g449sHPb84Y2E+VJxxzF//eXEGUc8P5/YdBxdnPHX0/uKMedvKn9cH5xxenHHvjsOK\nM/afu7U4A+DUjZ8ozrj6Z99YnPHir72vOOOOp7ygOOOprXuLM2aCuwZIbRlRnFFH16vDCK3yjNxZ\nw0ggs7xHPxJPKc6YG+W9Myj/+R717a8WZ/zgyEXFGQCPtcr7/L6xZc8z7cGhW75VnHHX/GOLM2r4\n8XJAPFIeQj3vv8eZV5xx87zyv2/2GS1/7x1+49eLM+474YXFGQBH3PDZ4owftRYWZyyMwTvht52z\no6bVGJIkSZIkSZIm09dbwkmSpOb4raQkSZKaZufscCWcJEmzlH1IkiRJTbNzdrg7qiRJkiRJktQw\nt4STJGmWctcASZIkNc3O2VHrlnAR8aKI2BAROyLiteNuWxoRt1eXpXUuV5IkSbOLvVOSJA2a2raE\ni4g5wF3AecAF4257CvC/gcW0TwB9TUSszcyH6lq+JEmamky/ldRgsndKkjQ47JwdPa+Ei4gLgaXA\nA8DdwDXA2cC1wGnAqsy8qJq3Ne7uvwR8KTN/UN3+JeDlwKrSByBJkvZOy10D1KfsnZIkDQ87Z0dP\nu6NGxMnAEmARcBZwStfNY5m5eFcRmsTTaReoXb5TTZtoWcsiYn1ErP/06jW9DE+SJElDYrp6Z3fn\nXL3K9XOSJKl5vW4JdzpwSWZuBYiItV231bqmLDNXAisBvnXHba4ulSSpIf24a0C1K+Ea4GjgW8Dr\nx+9GGBG/ALy/a9KxwJLM/FxEfAx4MfBIddt5mXltw8NWvaald3Z3zk2bN/ffm0GSpCFh5+yo48QM\nW3qY5x7gqK7/H1lNkyRJ6vY24PLMPAa4vPr/k2TmFZm5KDMXAS8BtgL/3jXLH++63RVwQ8feKUmS\n6jAjnbPXlXBXAudGxPyIWACc0+P9drkM+MWIOCgiDgJ+sZomSZJmSLaykUuhVwMfr65/HDh3D/O/\nFrh011ZTGgr2TkmShoids6OnlXCZuYH2ZnobgUuBdRPNFxGnRMR3gNcBfxsRN1b3/wHwrup+64B3\n7jpYriRJGi7dx9qqLsumcPfDMvPe6vp9wGF7mH8JP3nA/RURcV1EvD8i9pnCstUH7J2SJKkXg9g5\nez47amauAFYARMTyatoZ4+ZZR3uT/4nu/w/AP/S6PEmS1KwavkGcOLfrWFsTiYgvA4dPcNOF43Iy\nIiYdZEQcATyPJ2/l9HbaRWqsGsNbgXf2PHj1BXunJEnDw87Z0fNKOEmSNFxaM3SQ3Mw8c7LbIuL+\niDgiM++tCs8Du4l6Pe0D+G/vyt71jebjEfFR4IJaBi1JkqS9Yufs2KsTM2Tm8sx8397cV5IkaTfW\nAkur60uBf9nNvG9g3G4BVYkiIoL2sT1uaGCMmkb2TkmS1IAZ6Zx9vSXcaO4ouv/2Gg4Dk0RxRl05\nO0bHahhJuflRfuzrkdxZnFH6+vixo59THNH6avnxnhe++pnFGQd/7+bijHsPO6k4oy4H8MPijMfY\ntzhjdL+nlmdE+Wv+yLHyk/vV8d4D2DJ2YHHGi79W/jf1V08r38jphRs+Upyx750bizMAeO6J9eT0\nqKldAwq9G/hMRJwPfJv2N49ExGLgTZn5W9X/j6Z9Bsyvjrv/pyLiUCCAa4E3Tc+wNchGW+WdYkfM\nLc7Ivft+vHZjua2WnDoezwE1HM4vs54+X2rboeVdb+Fl/1jDSIBf+o3iiAWP3lec8f0Dfro4Y2EN\nfTGj/DWyM0eLMwAeG1tYnDE3nijOODo2F2fsrGF1w0i2ijOOuOGzxRkA3/yZ1xVnnH7VB4sz5jz2\naHEGAM8u/zu4V3bOjr5eCSdJkmaXzHwQeOkE09cDv9X1/28BT59gvpc0OT5JkiQNvpnqnK6EkyRp\nlsoZOj6HJEmSZg87Z4cr4SRJmqVa/blrgCRJkoaInbOjPw48IUmSJEmSJA2xWlfCRcSLImJDROyI\niNeOu+3fIuLhiPjXOpcpSZL2TraykYvUNDunJEmDw87ZUdtKuIiYA9wFnAd8eoJZ/hJ4Y13LkyRJ\n0uxj55QkSYOq52PCRcSFwFLgAeBu4BrgbNqnYj0NWJWZF1Xz/sR5hDPz8og4o4YxS5KkGniQXPUj\nO6ckScPFztnR00q4iDgZWAIsqu6zgXYhAhjLzMXNDE+SJDUlWz+x/kKaUXZOSZKGj52zo9fdUU8H\nLsnMrZn5Q2Bt121r6hxQRCyLiPURsf5Tqz9TZ7QkSZL624x0zk+vrjVakiRpQj3vjrobW2rI+LHM\nXAmsBLj79pvcZlGSpIZ4ungNmMY657fuuM03gyRJDbFzdvS6JdyVwLkRMT8iFgDnNDgmSZIkzU52\nTkmSNLR62hIuMzdExBpgI+2D5K6baL6IOAW4BDgIOCci3pGZJ1S3/SdwLLB/RHwHOD8zL6vhMUiS\npL3gQXLVb+yckiQNHztnR8+7o2bmCmAFQEQsr6adMW6edcCRk9z/9L0dpCRJkmYHO6ckSRpWdRwT\nTpIkDaD0+BySJElqmJ2zY69WwmXm8prHIUmSppmFSP3OzilJ0uCzc3b09ZZwT4zOK7r/aO4oHsMo\n5RkAGVGc8cM5TynOGOPx4ow6tGK0loydNbyEb33amcUZTzn3pOKMw277anHGj45eVJwxQqs4oy79\nMpZHRw8sztiHx4oz6nrf1PF5VIc7nvKC4owXbvhIccbXn39+X4wDoOy3nqS99USUv/vGsvxzvg47\nYm5xxpbRA2oYCcyJ7cUZdfzuq8POKO+c98w/pnwgr7iAQ3d8tzjm4G+tL8545KgTizPq6CRRw7Gm\naskgafV83sPJPTqnvHfOyfL33vbRfYoz6nj/ZpQ/pwA/ai0szjj9qg8WZ/znC95cnPGCb360OANg\n31pSNFV9vRJO2p06VsBJs02/rIBTf2hlf6zoliT1po4VcGpGHSvg1Iw6VsCpjJ2zw08KSZIkSZIk\nqWFuSiRJ0izl8TkkSZLUNDtnhyvhJEmapSxEkiRJapqds8PdUSVJkiRJkqSG1boSLiJeFBEbImJH\nRLy2a/qiiPh6RNwYEddFxK/WuVxJkjR1mdnIRWqanVOSpMFh5+yobXfUiJgD3AWcB1ww7uatwG9k\n5u0R8TTgmoi4LDMfrmv5kiRJGn52TkmSNKh6XgkXERcCS4EHgLuBa4CzgWuB04BVmXlRNe+Tzj+b\nmbd1Xf9uRDwAHApYiCRJmiGtlqeLV/+xc0qSNFzsnB097Y4aEScDS4BFwFnAKV03j2Xm4l1lqIes\nU4ExYNMkty+LiPURsX71qlW9REqSpL2QrWzkIu2tmeqca1Z/unDkkiRpMnbOjl63hDsduCQztwJE\nxNqu29b0urCIOAL4BLA0MydcFZqZK4GVAJs2bx7MZ1WSJEl7Y0Y6522b7rJzSpKkxtVxTLgtvcwU\nEQuBLwAXZuZVNSxXkiQVmGTdhNSv7JySJA0gO2dHr2dHvRI4NyLmR8QC4JypLCQixoBLgH/MzIun\nOEZJkiTNDnZOSZI0tHraEi4zN0TEGmAj7YPkrptovog4hXbxOQg4JyLekZknAK8HXgQcHBHnVbOf\nl5nXFo5fkiTtpUE9loaGl51TkqThY+fs6Hl31MxcAawAiIjl1bQzxs2zDjhygvt+EvhkwTglSZI0\nC9g5JUnSsKrjmHCSJGkA+a2kJEmSmmbn7NirlXCZubzmcUiSpGnW8iC56nN2TkmSBp+ds8Mt4fYg\nqGeNbRLFGWM8Xj6OKB9HZPlzUsfzOjfLnw+Aw1qPFGccdO+NxRk/OnpRcUas/tvijDzvwuKMYTOX\nJ4ozduZoccYI5b+8Rmr6BVjHZ9pTW/cWZ+x758bijBdu+Ehxxteff35xBsArt99aS46kqRlhZ3FG\nRq/nO9tNRg2frRHlHWtea2txBsCOmFucUcdzUsfPpo7uekD+oDhjvx+V/+4EePgZ5b2Tj1xUHBFv\nekf5OGpQx99INf3ZyLxWTyeC3q0dI2M1jKRcLe+9Grrrwni4OANgzmOPFme84JsfLc646qTfLM4A\ne+dMcSWcJEmzlLsGSJIkqWl2zo7yVdOSJEmSJEmSdsst4SRJmqWy5fE5JEmS1Cw7Z4cr4SRJmqXc\nNUCSJElNs3N2uDuqJEmSJEmS1LBaV8JFxIsiYkNE7IiI13ZN/6lq+rURcWNEvKnO5UqSpKnLbDVy\nkaaDvVOSpMFg5+yobXfUiJgD3AWcB1ww7uZ7gRdm5uMRsT9wQ0Sszczv1rV8SZIkzQ72TkmSNIh6\nXgkXERcCS4EHgLuBa4CzgWuB04BVmXlRNe+TVklm5hNd/90Hd4OVJGnGtTw+h/qUvVOSpOFh5+zo\nqZRExMnAEmARcBZwStfNY5m5eFcR2k3GURFxHe0i9Z7Jvo2MiGURsT4i1q9etaqnByFJkqYuW61G\nLlKJ6eqdT+qcq+2ckiQ1xc7Z0euWcKcDl2TmVoCIWNt125peAjLzbuDEiHga8LmIuDgz759gvpXA\nSoBNmze7ulSSJGl2mZbe2d0579h0p51TkiQ1ro7N87dMZebqm8gbaBcsSZI0Q7KVjVykBtk7JUka\nMHbOjl5Xwl0JnBsR8yNiAXDOVBYSEUdGxPzq+kG0j+Vx65RGKkmSpNnA3ilJkoZST7ujZuaGiFgD\nbKR9gNx1E80XEacAlwAHAedExDsy8wTgOOCiiEgggPdl5vV1PABJkrR3BvXU7hpu9k5JkoaLnbOj\n591RM3NFZj4nM08DbqumnZGZ67vmWZeZR2bmfpl5cFWEyMwvZeaJmfmz1b8ra38kkiRp4EXE6yLi\nxohoRcTi3cz38oi4NSLuiIi3dU1/ZkR8o5q+JiLGpmfkqpO9U5IkNWmmOqenbJckaZbq0+Nz3AD8\nCu1dEicUEaPAh4FXAMcDb4iI46ub3wO8PzOfDTwEnF86IEmSJO09O2dHr2dHfZLMXL4395MkSf2j\nH0/tnpk3A0TE7mY7FbgjMzdX864GXh0RNwMvAX6tmu/jwHLgb5oar5pn75QkabDZOZ+84IG9AMuG\nJaOfxjJMGf00FjP6dyxm9O9YzBjMC7AMWN91mfJjB74CLJ7kttcCf9/1/zcCHwIOqYrSrulHATfM\n9PPhZTgu/fI5MEwZ/TQWM/p3LGb071jM6N+xzJbeOYidc9B3R102RBl15ZjRTI4Z9WfUlWNG/Rl1\n5ZhRf8ZAyMyVmbm46/KkY3JFxJcj4oYJLq+eqTFLPeiXz4Fhyqgrx4z6M+rKMaP+jLpyzKg/o66c\nfsnoe4PYOfdqd1RJkqS9lZlnFkbcQ/sbx12OrKY9CBwYEXMyc0fXdEmSJM0y/dg5B31LOEmSNPus\nA46pzko1BiwB1mZ7f4AraO86ALAU+JcZGqMkSZIGW+2dc9BXwtVxyvl+yagrx4xmcsyoP6OuHDPq\nz6grx4z6M4ZeRPxyRHwHeCHwhYi4rJr+tIj4IkD1jePvAZcBNwOfycwbq4i3Am+JiDuAg4GPTPdj\n0NDql8+BYcqoK8eM+jPqyjGj/oy6csyoP6OunH7JGGoz1TmjOoicJEmSJEmSpIYM+pZwkiRJkiRJ\nUt9zJZwkSZIkSZLUMFfCSZIkSZIkSQ1zJZwkSZIkSZLUsKFYCRcR+8/w8p9SQ8ar+mQcz46I10TE\n8VO4z4Gly61y5nRd3z8iFu/NY4qIQyPipIg4cW9eGxFxbES8NSI+WF3eGhHHTTVnkuzf3IuxvHT8\n44iIl08h49SIOKW6fnxEvCUizprKOIZZRDx/pscAEBELI+LkiDioD8ZySMF9D4qIhXt53wMi4ler\n1+hbquu1fL5U+S+bwrwLI+JZE0w/cQoZh0fE4dX1QyPiVyLihF7vL6n/zHTnrMYwFL1zGDpndf+h\n6J12zunRD72znzonDGfvtHNqd4ZiJRxwUy8zRcTzIuKqiLg7IlZ2f/BExNU9Zvx8RNwcETdGxM9F\nxJeAdVXmC3vM+JVxl9cAK3f9v8eMP+26fnxE3AZcExHfioif6yWjuu8Vuz74IuKNwBeBVwBrIuL3\ne4z5fkR8OSLO39sProg4D7g/Im6LiFcA1wHvATZGxBt6zDg+Ir4MfB34BvB3wPUR8bGIOKDHjLcC\nq4EArq4uAayKiLdN8WFN5B29zhgRbwb+Bfh94IaIeHXXzX/eY8b/Bj4I/E1E/AXwIWA/4G0RcWHP\no548//oe5zsqIlZHxH9GxJ9ExNyu2z7XY8axEXFpRHwhIp5V/Vwfjoirey2rEfH8cZeTgbVVee65\nFEXE/911/ciIuLway39HxHN6zPhk13vvl4AbaL/mr42I1/WY8YOI+PuqNEev4x+X8YqIuDMivlY9\nDzcC34iI70TES3vMeFpE/GNEPAJ8n/br9a6IWN79s95Dxm8AG4AzgH2ryy/Q/lz7jb15bBPo7bTh\nEa8HbgH+qfqsP6Xr5o/1mPE7tD+LroqI/wf4V+CVwD9HxPlTGrWkfjJtnbOad2h65zB1zipnaHrn\nMHXOat6h6Z3D1DmrnNnSO+2cmlxmDsQFeMskl/8B/KDHjK8BLwcOBC4AbgSeVd32zR4zrgaeB7yQ\n9pv+tGr684H/6jFjO+03xz8AH60uP6r+/YceMzZ0Xf8C8Irq+qnAf0/heb2h6/o64ODq+r7AdT1m\nXA+cDXwKeJD2L/ElwPwpjON64BDgmcAPu34uh01hHFcBz+16Hj5eXf9t4OIeM24D5k4wfQy4vceM\n6ya5XA88PsXnZP/q+tHAeuAPpvh6vR4YrX6ePwQWVtPnT+F5/ZVJLq8BvtdjxpeANwGLgL8G/rvr\ntdbrY7kSOAd4A/Dt6jUW1bTLe8xoVcu+ouuyrfr3P6bws+l+/30GWEb7S41fnsJYru+6/t/A0dX1\nQ4CNPWbcCvwe8F/APcD/C7yg18dRZVwLHEf7M+3BXfevpm3oMeM/gDO6Xi/vp128/wxYOYXHcuAE\n0w8CbpvC41k7yeXzwJYpPCdHVNdPpV2OfnmKr9frq/fdwcCjwOFdj+faqfyMvHjxMr0X+qRzVvMO\nTe9kiDpnNf/Q9E6GqHNWOUPTOxmizlnlDE3vxM7pZS8vP94UewD8OfCXwI4Jbut1i74Fmflv1fX3\nRcQ1wL9V38ZljxlzM/N6gIj4XmZ+DSAzN0TE/B4z/i/g3cC6zPybKuuMzJzSropdnpaZl1bjuHoK\n4wDYHhFPz8x7aL9pt1TTH6f9i7SnjMz8V+Bfq2WfQ/sX1Ycj4rLM/LUeMnZm5vdpf8P5aGZuqh7P\n/VP4smV+Zt5a3e/qiPg/1fW/i4i39JjRAp5G+5dttyOq23pxGPBLwEPjpgftX3y9GsnMRwEy81sR\ncQZwcUT8VJXVix2ZuRPYGhGbMvOHVd62iOj18ayhXXYneo/M6zHj0Mz8P9X134+IXweujPbuML2+\n9xZk5ucBIuJdmbm6mv75iOh1C8PXAW8G3rvrPRMRd2bmL/R4/4k8JzNfX12/JCL+V4/3G4mIhdXP\npAXcBZCZ34+u3WT2YEtmfgj4UEQ8g/b77v+rtg5YnZl/0kNGKzNvBoiIrZl5VTWOmyOi18/WgzPz\nK9X9/jkiLszMLcCfRsQtPWYEE78WWvT+egc4Hfh12p9n4/NP7TFjNDPvhR9/lvwC7c+3oyYZ40S2\nZ+ZWOu+9+6q8hyKi1wxJM6NfOicMV+8cps4Jw9U7h6lzwvD2zkHvnDBcvdPOqb0ySCvhNgCfy8xr\nxt8QEb/Va0hEHJCZjwBk5hXR3iT/n4BejwPR/eHw9nG3jfUSkJnror2f+O9HxBXAW5laIQP46YhY\nS/tNfmRE7Fu9+QB62gy38kfAv0fEP9H+lvY/IuIy4DTa35D24scfVJm5jfa3NJ+J9qb45/aYcVe0\nN11fANwSERcB/wycCdzbY8amiPiftL8d+RXa3yxQbZbc64f6HwKXR8TtwN3VtGcAz6b97U8v/pX2\nt4nXjr8hIr7SYwa0d5VYtCsnMx+NiLNpf5P9vB4znuh6bZzcNY4D6L3cXQe8LzNvGH9DRJzZY8bc\niJiXmY8BZOYnI+I+4DLa31z1orug/9W423p97/1T9fp+V7Q37/8fTP29B+333Adpv/YPjYi5mbm9\nuq3X9987gCsi4sO0v1X8bPWe/gXg33Z7z47u995dwHuB90bEscCv9pjxcLUZ+0LgoYj4I9rv4TP5\nyVIxme9VBfcK2u+9bwFUuyv0+t5bAWyIiH/nye+9lwHv6jED2lsmbM3Mr46/ISJu7THjRxHxrK4/\nzO6t/iD5HNDr8TWy63Xxyq4xzGN4DgUhDat+6ZwwXL1zmDonDFfvHKbOCcPVO4epc8Jw9U47p/ZK\nZA7GytGIeC7wYPXN1fjbDsvM+3vI+DVg86417l3TnwH8z8z87R4yXgV8uat47Jr+LOA1mfnePWWM\nu9/TgA8AizPzp6dwvxePm3RN9QvzMOC1mfnhKWQdAPwa8BzaK2a/A/xLZvb0TUJEXJCZ7+t1eZNk\nLAR+l/Yvpg/R/kbvN2l/U/OuXd8Q7CHjQOBPgOOBjcC7M/NH1eM7bvzPfTc5I7S/vXh6Neke2t8e\n75zaoyoTEUfS/lbxvglu+/nM/K8eMvbJzMcnmH4I7U2f93h8jYg4Hfh29Qt3/G2LM3N9Dxl/RHsT\n86+Om34S7W8H93jw0uoX9qd2fVPbNf3ZwO9l5h/uKWOCZf8VcEJmPnWK9106btLa6tumw4E39/pt\nYDX23+bJ773PZeZlPd7/rzKz12/bJ8s4CvhT2gX5HbR3uzif9rfyF+z6tnIPGc8A3kd7V4KNwB9X\nJeJg2rsL/FOPYzmI9nu/+713WWaO/3a/URHxs7S/8b1j3PS5wOsz81M9ZDwD+G5m7hg3/em0P4++\nXOeYJdWnXzpnNf9Q9c5h6ZxVztD0zmHqnNW8Q9M7h6lzVjn2ziePwc45Cw3MSriJVL+09s9qc+cB\nzxgF9pvpcdSVM2zPSZW1//hfxDORMRNjqX4Wb87M9xcsr+8yqm/MFuzN66MfH08fZPxBZo7/tliS\nBl6/dKMax9IXHauPMoqfj7rG0pXVF71zEDtnXTn90jv78bHsbUbNY7F3aiAN3OaJEfHpaJ/Gdz/a\nZ3a5KSL+eAgyrp+pcdSVM2zPyQR6OiPaNGTUldNzRvVtbM9nDBuAjF+rrufeFuU+ezz9krGkJGMy\nMYUzojWd0y8ZkprXL92oobHMWMf6/9u7exa7qiiM489SJIUvrRZBe8sklmlsRQgWNtr4ASxEBMFG\ni5AvkMJSRAJWoiDpR20GVFIOtn4BtYvgsjj3Zi7JXL0v6+z9nD3/H1zkjjP/rMlwJ4szydmmjYN+\nP6pm2cJl71zczlnVcdk7zT4Xp6+N7d45UgP1lnRPuLVXM/PPiHhH0n1JH0v6WdMNdGkc1nCapWsj\ntt9INyQ9t8svXtFwm2Xlp4i4q+mmueubKSszf1lg48eCRtUsNCRFxFvb/pekl3YdoKLj0gDQncte\n4zQLjcKOy67n0thQsZNUdVz2TpfPZYivjcuu59JAW0u8CPdMTP9G+paku5n5d+x3khEN71l6NypO\nRKtouM0iTce8S9P9GyQ9OlXo9UvacJplhEbViWgVHZcGgL567ySOs9Co7bjsei6NNfa0sRsOs7js\nei4NNLTEi3CfazoB5YGmo6ZfkfQHjaMaTrP0blSciFZyqprZLNJ0+lZKj05Hyikz/eRzx3syjNRw\nmmWERtWJaBUdlwaAvnrvJI6z0KjtuOx6Lo019rSxGw6zuOx6Lg00tLiDGSLiw42nqfOfrPwj7faC\npeE7S+9G1JyIdnTDbZbV+9+T9JqkbzX9YfempFNJv0lSZn62/aPHazjNMkIj6k5EqzjN16IBoK/e\nO4njLLcr6YMAAALuSURBVDRqOy67nktj4/3Z0wZuOMzisuu5NNDWEv8m3HVJNyR9pwtebDQOajjN\n0rWRmWePvy3OT7raaXmoaLjNsnJV0rXM/GvV+VTS95n57iVtOM2y+EZm/hART0fEB/nYaVn7LA8V\nHZcGgO5c9hqnWWgUdlx2PZfGBva0sRvdZ3HZ9VwaaCwzF/WQdKLpaOf18+clndA4vOE0i1HjnqQX\nJD2r6USn3yV91LrhNIukM0lXNp5fkXR2WRtOswzWON336zBXx6XBgwePPg+XncRpFhqzzeKy67k0\n2NMGbjjNIpNdz6XBo81jiX8T7kVJDzeeP1y9jcbhDadZXBpDnLpV3PhS0mlEfLN6fkvSF3t8/GgN\np1lGagxx6lZxA0AfLjuJ0yw05um47HouDfa0sRtOs7jsei4NNLDEi3AuL9iRGk6zuDRGOXWrrJGZ\ntyPivqSbqze9l5m/XtaG0ywjNTTOqVuVDQB9uOwkTrPQmKdjseu5NNjTxm6YzeKy67k00MDiDmaQ\npIi4pvMX28khL3wavrM4NCLifU0/uXsg6Q1JL0v6KjNv/ucHFjfcZgHmFtMNrp846Wr1X+V+N8o+\nquPSANCPw07iNguN+o7LrufSAFpx2fVcGmhjkRfhgLnFIKduVc8CzC0GOXWr+vMBAIzLZddzaQCt\nuOx6Lg20scR/jgq0MMSpWzPMAsxtiFO3ihsAgLG57HouDaAVl13PpYEGuAgHXOyqpOsF3wiPbbjN\nAszN6UbZLg0AwNhcdj2XBtCKy67n0kADXIQDLub0jdBpFmBuTjfKdmkAAMbmsuu5NIBWXHY9lwYa\n4J5wwAUi4hNJb0va/Cb2dWbeadlwmwVoweVG2U4NAMC4XHY9lwbQksuu59LA/LgIB2zh9I3QaRYA\nAADUctn1XBoAMCouwgEAAAAAAAAze+r/3wUAAAAAAADAMbgIBwAAAAAAAMyMi3AAAAAAAADAzLgI\nBwAAAAAAAMyMi3AAAAAAAADAzP4FOr4AnK7WI6cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1584x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_match = Xtrain.loc[np.where(Ytrain[\"y\"] == 1)[0],\"qs1\":\"qr13\"].corr()\n",
    "corr_unmatch = Xtrain.loc[np.where(Ytrain[\"y\"] == 0)[0],\"qs1\":\"qr13\"].corr()\n",
    "\n",
    "fig = plt.figure(figsize=(22, 8))\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax2 = fig.add_subplot(122)\n",
    "sns.heatmap(corr_match, annot=False, vmin=-1, vmax=1, center=0, cmap= 'coolwarm', ax=ax1)\n",
    "ax1.set_title(\"Correlations between all the quality scores for the matching images\", fontweight='bold', fontsize=12)\n",
    "sns.heatmap(corr_unmatch, annot=False, vmin=-1, vmax=1, center=0, cmap= 'coolwarm', ax=ax2)\n",
    "ax2.set_title(\"Correlations between all the quality scores for the unmatching images\", fontweight='bold', fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "njmCdCGBXq8f"
   },
   "source": [
    "<div align='justify'>No strong correlations appear between the quality scores of the two images.<br>\n",
    "<br>\n",
    "<b>Conclusions</b><br>\n",
    "All these observations corroborate the fact that quality scores are not very informative. </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XsOSwJLs4Pql"
   },
   "source": [
    "<ul>\n",
    "<li>Correlations between the matching scores</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 499
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 513742,
     "status": "ok",
     "timestamp": 1582909765836,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "3ezrzbJy3gQO",
    "outputId": "16e22076-fdcf-4b2f-d19c-6567e0a0b3c4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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g+7/vpyQ9jc7vfkTOkkUU7dlVFiXh2htNOv/yI8Gt29Lh+VfZcMMVABQdSGbz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\ncFbsTgHgcLGDNcnptI3xvd4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a+atX4Dxc4FuNykaza28l5Y1ncGRl0OLJsRT8tZySA+Uv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\nN1GZmkrJihWEDR1C8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4H3Pgwcfj6l8x9Hr61GWUO8fkXQmEtx7NvZSnmPrt34J5NnH5a8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\noQOGYAvWXWme5wremsX+qdex/94bKP7iXSpWL6b4qw98mq9qW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63DpetcOKg7157Q/6Dt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s5ZyuHbzu7x4fxcq9uTjdbnLLqthpKaWgwrcfrlgrqkmMDGu4bY4MxVJZ7Z0vr4iC8irOPbHz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\nl9IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1584x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_match = Xtrain.loc[np.where(Ytrain[\"y\"] == 1)[0],\"s1\":\"s11\"].corr()\n",
    "corr_unmatch = Xtrain.loc[np.where(Ytrain[\"y\"] == 0)[0],\"s1\":\"s11\"].corr()\n",
    "\n",
    "fig = plt.figure(figsize=(22, 8))\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax2 = fig.add_subplot(122)\n",
    "sns.heatmap(corr_match, annot=True, vmin=-1, vmax=1, center=0, cmap= 'coolwarm', ax=ax1)\n",
    "ax1.set_title(\"Correlations between the matching scores for the matching images\", fontweight='bold', fontsize=12)\n",
    "sns.heatmap(corr_unmatch, annot=True, vmin=-1, vmax=1, center=0, cmap= 'coolwarm', ax=ax2)\n",
    "ax2.set_title(\"Correlations between the matching scores for the unmatching images\", fontweight='bold', fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NULwp2KCYcCy"
   },
   "source": [
    "<div align='justify'>\n",
    "<b>Conclusions</b><br>\n",
    "When the images match, the correlation between the matching scores is very high, while when the images do not match, the correlation is lower. The correlation structure therefore depends on whether or not the images match, which is an interesting observation for the next step.  </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XIuo1cMYhh-J"
   },
   "source": [
    "### **4. Modeling**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "s7Lzo6tVt3bn"
   },
   "source": [
    "#### 4.1 Preparation of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LhcXqZuXh2FL"
   },
   "source": [
    "##### <b><i>Splitting in train and validation set</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6ujgpRWrDWpm"
   },
   "outputs": [],
   "source": [
    "# Split in train and validation set\n",
    "X_train, X_test, y_train, y_test = train_test_split(Xtrain, Ytrain, random_state=12345, stratify=Ytrain, shuffle=True, test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 9314,
     "status": "ok",
     "timestamp": 1582814871216,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "Hxa9AdJnEHoe",
    "outputId": "533e8b5e-2e22-461a-a502-a7f52bce30ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the subtraining data : (8820641, 37)\n",
      "Shape of the subtest data : (980072, 37)\n"
     ]
    }
   ],
   "source": [
    "# Print the shape of the sub datasets\n",
    "print(\"Shape of the subtraining data : {}\".format(X_train.shape))\n",
    "print(\"Shape of the subtest data : {}\".format(X_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 9303,
     "status": "ok",
     "timestamp": 1582814871216,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "ATV6SnZ5ESqS",
    "outputId": "5b7fc29c-5bd7-4bac-b875-f12d7e6a0b42"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1B28DIF9UfS7p5MLevW3Nuueeite/ZwE35d2DJ0i7cpBOuxcRdNk+pC7nLyPtGlUH1U/r\nXZNsOHPYrX9PknYJ/oF0oHcB6RjGV3vYpl64m3Qc65OkA+B/BL4aEd9vO5fZGvJurJkVYUj3emJm\n1imH3TAmaVdJv5P0hKSV/c9h7dR631kiqb+fmbRBxmE3vH0G+BtSBwanV4WSxki6VNLyTvrvkzRa\n0v152mNq5W/OZSsl/c06exert+U9km6R9KSkOyW1/QU0SW+p9Xv4lVr5N3LZ2V1Ufw3pspbRwImS\n1vSOH+sBh93wVt2a9s8RcVStfBvSnQtzOllIRCyl72cVT5D0PEmj6Put19Mi4rqBaHA7kl5PvoYu\nP28EfEXSlA4X8RFJzX5ztyMRcXlEvJd0MoXcDhsiHHbDW3UL0CP1woj4Y6RbeT7dasZaB5dH53nO\nI/Xm8izSxdufIt3mswD4bJ5nQ0nHSZor6VFJN9eDSNIESb+R9FDu3PVGSc3uI27lWNIFqZ+N9BOO\n1eUqx9fqWJTb/Y6GeYPUXdFxbd7zmyRdLelBSYslfVvS9k0mrT7PDZuMs0HKYTdMSRoPvCwPLhug\nxf4T6Yr3g4HPVWXVBdKki4KrH+f+Lilczpb0vlz2DeBvgVmkG8IfInUH1KmqE4k5Dc8vlLR5k+nr\nlpPuWPhwswCTtBupd5zXkn6YfBHwfuCyJp1RLs/Pb5e0URfttx5y2A1Dkk4ldX2zBemHrLvt9ADg\nMOAlpG6zAIiIO+nr2GEU8J2I+FmucwNSLxSQOnx4lHR/KqRbgyBtCQWp661TgDfW5unEtvm52rJ6\ntDbuufn5jbndVzaZfxqpC6HPNBn3EdJ1p9Mj4lBSh6DLSN0PvaFh2pPIN/UDT3QQtDYIOOyGp6tI\nu5wAB0h6drcLiIi7I+LWiHigYdQptden1l5vR+q7ENI9oUcBb8/DVXdAnyDdDzk9Py8ndeTYqfvy\n8+YNz5DusyYi7sjtfpQGEXE16XP5EH3HMyvj8nPVVdSTpBM78MyuovYn9aW2hNRrSb89bljvOeyG\noYj4UUTsS+qpditg126XIen5knaRtFXDsuvdC9VfLyHd/gbw0ujrDmgEUP3g0rUR8YrcpjeTtrK+\nnLcKO/GH/Lx7fq7OAM+PiEdyu1+Y273ZM+ZOPkc6sTGpoXxBfq66ihpFXwA2dhVV3bv7iYj4cHTQ\nvZD1nsNueFuUnzeuF0raRumX2k6slc3Q6r/edj5pK2cyHYjUEWt1dvYXkr6Vu3GaTz6BQTr+9XNS\np44fIe3WPhQRqyS9qHaJSKvdwpPz84m5rVWXYF+qTXNVbvebWrTzGlJPII39CH2TFN5TJH2X1BPI\nNqQeWX7dMG11nG4RNmQ47Ia36kLiLRrKNyeF2DtrZZPpMNjaOD4/VpAO7r+JFDzV/a5Xki7XeD+p\nr79rgUPyuCp8qu65nyEifkXq2n8R8F7S7uOnI+JbXbbzc40FEXE9fb938g5Sr7jfBfaPvl58K9Xn\n2Vhug5jvjR3GJH2L1F/e7aQfevl4j5vUkqS/J/VxeFrDNYGDhqSJpD7g3kPqs3FM9HUbboOct+yG\nty+RfjXq+XR3IqAX9iGdQW55HdwgsCcp6O4lbVE66IYQb9mZWRG8ZWdmRXDYmVkRhl1Pxdtss02M\nGzeu180wK871119/f0Q0+93bQWHYhd24ceOYM6ejzjzMbABJavU7vYOCd2PNrAgOOzMrgsPOzIrg\nsDOzIjjszKwIDjszK4LDzsyK4LAzsyI47MysCMPuDopufPKy83vdhGHvq/sd1usmmAHesjOzQjjs\nzKwIDjszK4LDzsyK4LAzsyI47MysCA47MyuCw87MiuCwM7MiOOzMrAgOOzMrgsPOzIrgsDOzIjjs\nzKwIDjszK4LDzsyK4LAzsyI47MysCA47MyuCw87MiuCwM7MidBR2kj4h6WZJN0m6UNLGknaSdK2k\neZK+J2lUnnajPDwvjx9XW87xufw2SW+rlU/MZfMkHVcrb1qHmVm3+g07SWOAjwMTIuJlwAjgEOBk\n4JSIeBHwADAlzzIFeCCXn5KnQ9Kueb6XAhOBMySNkDQCOB3YD9gVODRPS5s6zMy60ulu7EhgE0kj\ngU2Be4A3Axfn8ecBB+bXk/Iwefw+kpTLL4qIJyPiTmAesHt+zIuI+RHxFHARMCnP06oOM7Ou9Bt2\nEbEY+ApwNynkHgSuB1ZExMo82SJgTH49BliY512Zp9+6Xt4wT6vyrdvUsRpJUyXNkTRn6dKl/b0l\nMytQJ7uxW5G2ynYCngdsRtoNHTQi4qyImBARE0aPHt3r5pjZINTJbuxbgDsjYmlE/AX4IbAXsGXe\nrQUYCyzOrxcDOwDk8VsAy+rlDfO0Kl/Wpg4zs650EnZ3A3tK2jQfR9sHuAW4EjgoTzMZ+HF+fUke\nJo//ZURELj8kn63dCRgP/A64Dhifz7yOIp3EuCTP06oOM7OudHLM7lrSSYIbgBvzPGcBxwLHSJpH\nOr52Tp7lHGDrXH4McFxezs3ATFJQXg4cERFP52NyRwKzgLnAzDwtbeowM+vKyP4ngYiYBkxrKJ5P\nOpPaOO0TwMEtlnMScFKT8kuBS5uUN63DzKxbvoPCzIrgsDOzIjjszKwIDjszK4LDzsyK4LAzsyI4\n7MysCA47MyuCw87MiuCwM7MiOOzMrAgOOzMrgsPOzIrgsDOzIjjszKwIDjszK4LDzsyK4LAzsyI4\n7MysCA47MyuCw87MiuCwM7MiOOzMrAgOOzMrgsPOzIrgsDOzIjjszKwIDjszK4LDzsyK4LAzsyI4\n7MysCA47MyuCw87MiuCwM7MiOOzMrAgOOzMrgsPOzIrQUdhJ2lLSxZJulTRX0mslPUfSFZJuz89b\n5Wkl6TRJ8yT9SdKra8uZnKe/XdLkWvlrJN2Y5zlNknJ50zrMzLrV6Zbd14DLI2IX4JXAXOA44BcR\nMR74RR4G2A8Ynx9TgTMhBRcwDdgD2B2YVguvM4EP1eabmMtb1WFm1pV+w07SFsAbgHMAIuKpiFgB\nTALOy5OdBxyYX08Czo/kGmBLSdsDbwOuiIjlEfEAcAUwMY97dkRcExEBnN+wrGZ1mJl1pZMtu52A\npcC5kn4v6WxJmwHbRcQ9eZp7ge3y6zHAwtr8i3JZu/JFTcppU8dqJE2VNEfSnKVLl3bwlsysNJ2E\n3Ujg1cCZEbEb8CgNu5N5iywGvnmd1RERZ0XEhIiYMHr06HXZDDMbojoJu0XAooi4Ng9fTAq/JXkX\nlPx8Xx6/GNihNv/YXNaufGyTctrUYWbWlX7DLiLuBRZK2jkX7QPcAlwCVGdUJwM/zq8vAQ7LZ2X3\nBB7Mu6KzgH0lbZVPTOwLzMrjHpK0Zz4Le1jDsprVYWbWlZEdTvcx4AJJo4D5wAdJQTlT0hTgLuDd\nedpLgf2BecBjeVoiYrmkLwDX5elOjIjl+fVHgRnAJsBl+QHwpRZ1mJl1paOwi4g/ABOajNqnybQB\nHNFiOdOB6U3K5wAva1K+rFkdZmbd8h0UZlYEh52ZFcFhZ2ZFcNiZWREcdmZWBIedmRXBYWdmRXDY\nmVkRHHZmVgSHnZkVwWFnZkVw2JlZERx2ZlYEh52ZFcFhZ2ZFcNiZWREcdmZWBIedmRXBYWdmRXDY\nmVkRHHZmVgSHnZkVwWFnZkVw2JlZERx2ZlYEh52ZFcFhZ2ZFcNiZWREcdmZWBIedmRXBYWdmRXDY\nmVkRHHZmVgSHnZkVwWFnZkVw2JlZEToOO0kjJP1e0v/k4Z0kXStpnqTvSRqVyzfKw/Py+HG1ZRyf\ny2+T9LZa+cRcNk/ScbXypnWYmXWrmy27o4C5teGTgVMi4kXAA8CUXD4FeCCXn5KnQ9KuwCHAS4GJ\nwBk5QEcApwP7AbsCh+Zp29VhZtaVjsJO0ljg7cDZeVjAm4GL8yTnAQfm15PyMHn8Pnn6ScBFEfFk\nRNwJzAN2z495ETE/Ip4CLgIm9VOHmVlXOt2yOxX4NLAqD28NrIiIlXl4ETAmvx4DLATI4x/M0/+1\nvGGeVuXt6liNpKmS5kias3Tp0g7fkpmVpN+wk/QO4L6IuH49tGeNRMRZETEhIiaMHj26180xs0Fo\nZAfT7AUcIGl/YGPg2cDXgC0ljcxbXmOBxXn6xcAOwCJJI4EtgGW18kp9nmbly9rUYWbWlX637CLi\n+IgYGxHjSCcYfhkR7wOuBA7Kk00GfpxfX5KHyeN/GRGRyw/JZ2t3AsYDvwOuA8bnM6+jch2X5Hla\n1WFm1pW1uc7uWOAYSfNIx9fOyeXnAFvn8mOA4wAi4mZgJnALcDlwREQ8nbfajgRmkc72zszTtqvD\nzKwrnezG/lVEzAZm59fzSWdSG6d5Aji4xfwnASc1Kb8UuLRJedM6zMy65TsozKwIDjszK4LDzsyK\n4LAzsyI47MysCA47MyuCw87MiuCwM7MiOOzMrAgOOzMrgsPOzIrgsDOzIjjszKwIDjszK4LDzsyK\n4LAzsyI47MysCA47MyuCw87MiuCwM7MiOOzMrAgOOzMrgsPOzIrgsDOzIjjszKwIDjszK4LDzsyK\n4LAzsyI47MysCA47MyuCw87MiuCwM7MiOOzMrAgOOzMrgsPOzIrgsDOzIvQbdpJ2kHSlpFsk3Szp\nqFz+HElXSLo9P2+VyyXpNEnzJP1J0qtry5qcp79d0uRa+Wsk3ZjnOU2S2tVhZtatTrbsVgKfjIhd\ngT2BIyTtChwH/CIixgO/yMMA+wHj82MqcCak4AKmAXsAuwPTauF1JvCh2nwTc3mrOszMutJv2EXE\nPRFxQ379MDAXGANMAs7Lk50HHJhfTwLOj+QaYEtJ2wNvA66IiOUR8QBwBTAxj3t2RFwTEQGc37Cs\nZnWYmXWlq2N2ksYBuwHXAttFxD151L3Advn1GGBhbbZFuaxd+aIm5bSpo7FdUyXNkTRn6dKl3bwl\nMytEx2EnaXPgB8DREfFQfVzeIosBbttq2tUREWdFxISImDB69Oh12QwzG6I6CjtJG5KC7oKI+GEu\nXpJ3QcnP9+XyxcAOtdnH5rJ25WOblLerw8ysK52cjRVwDjA3Iv6zNuoSoDqjOhn4ca38sHxWdk/g\nwbwrOgvYV9JW+cTEvsCsPO4hSXvmug5rWFazOszMujKyg2n2Aj4A3CjpD7nsM8CXgJmSpgB3Ae/O\n4y4F9gfmAY8BHwSIiOWSvgBcl6c7MSKW59cfBWYAmwCX5Qdt6jAz60q/YRcRVwNqMXqfJtMHcESL\nZU0HpjcpnwO8rEn5smZ1mJl1y3dQmFkRHHZmVgSHnZkVwWFnZkVw2JlZERx2ZlYEh52ZFcFhZ2ZF\ncNiZWREcdmZWBIedmRXBYWdmRXDYmVkRHHZmVgSHnZkVwWFnZkVw2JlZERx2ZlYEh52ZFcFhZ2ZF\ncNiZWREcdmZWBIedmRXBYWdmRXDYmVkRRva6AWZr6r4zP93rJgx7237ky71uwoDxlp2ZFcFhZ2ZF\ncNiZWREcdmZWBIedmRXBYWdmRXDYmVkRHHZmVgSHnZkVwWFnZkVw2JlZEQZ92EmaKOk2SfMkHdfr\n9pjZ0DSow07SCOB0YD9gV+BQSbv2tlVmNhQN6rADdgfmRcT8iHgKuAiY1OM2mdkQNNi7eBoDLKwN\nLwL2aJxI0lRgah58RNJt66FtvbINcH+vG9Gp/2Ryr5swmAyp7w6Aj/5HN1PvuK6aMRAGe9h1JCLO\nAs7qdTvWB0lzImJCr9th3fN311uDfTd2MbBDbXhsLjMz68pgD7vrgPGSdpI0CjgEuKTHbTKzIWhQ\n78ZGxEpJRwKzgBHA9Ii4ucfN6rUidteHKX93PaSI6HUbzMzWucG+G2tmNiAcdmZWBIfdEOHb5oYu\nSdMl3Sfppl63pWQOuyHAt80NeTOAib1uROkcdkODb5sbwiLiV8DyXrejdA67oaHZbXNjetQWsyHJ\nYWdmRXDYDQ2+bc5sLTnshgbfNme2lhx2Q0BErASq2+bmAjN929zQIelC4H+BnSUtkjSl120qkW8X\nM7MieMvOzIrgsDOzIjjszKwIDjszK4LDzsyK4LAzsyI47MysCP8Pu8QUtgK1byMAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the proportions of the class in the validation set\n",
    "plt.figure(figsize=(4, 4))\n",
    "sns.barplot(x=\"index\", y=\"y\", data=y_test[\"y\"].value_counts().reset_index(), palette=\"Set2\")\n",
    "plt.ylabel(None)\n",
    "plt.xlabel(None)\n",
    "plt.title(\"Class proportions:\\nDo the images belong to the same person?\\n{1:Yes, 0:No}\", fontweight='bold', fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MxFtdYNGuUXn"
   },
   "outputs": [],
   "source": [
    "# Convert the dataset to numpy\n",
    "train_data = X_train.to_numpy()\n",
    "train_labels = y_train.to_numpy().ravel()\n",
    "valid_data = X_test.to_numpy()\n",
    "valid_labels = y_test.to_numpy().ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0I-rqlcdiLf_"
   },
   "source": [
    "#### 4.2 Model training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "P6SCh0R3uQVd"
   },
   "source": [
    "##### <b><i>Catboost</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i-i6OdDU2Nu7"
   },
   "outputs": [],
   "source": [
    "# ---- Best hyperparameters\n",
    "# iterations=2000\n",
    "# learning_rate=0.4\n",
    "# depth=12\n",
    "# l2_leaf_reg=2\n",
    "# bagging_temperature=0\n",
    "# random_strength=4\n",
    "# loss_function='Logloss'\n",
    "# task_type='GPU'\n",
    "# random_seed=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WKQp3dlM417R"
   },
   "outputs": [],
   "source": [
    "# Define the model\n",
    "model_cat = CatBoostClassifier(\n",
    "    iterations=5000, # Number of iterations\n",
    "    learning_rate=0.4, # Learning rate \n",
    "    depth=12, # Depth of the trees\n",
    "    l2_leaf_reg=2, # Regularization parameter\n",
    "    bagging_temperature=0,\n",
    "    random_strength=4,\n",
    "    loss_function='Logloss', # The loss function\n",
    "    task_type='GPU',\n",
    "    random_seed=0,\n",
    "    early_stopping_rounds=40,\n",
    "    use_best_model=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eF7fL14ORaGl"
   },
   "outputs": [],
   "source": [
    "# Train the model on the validation set\n",
    "model_cat.fit(\n",
    "      train_data, \n",
    "      train_labels, \n",
    "      verbose=True, \n",
    "      eval_set=(valid_data, valid_labels)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 17988,
     "status": "ok",
     "timestamp": 1582755641260,
     "user": {
      "displayName": "Camille Zoé",
      "photoUrl": "https://lh5.googleusercontent.com/-WidQdk_RFAk/AAAAAAAAAAI/AAAAAAAAAUY/s0MMoWnovSE/s64/photo.jpg",
      "userId": "04764807775006410983"
     },
     "user_tz": -60
    },
    "id": "zLBkLEHUEkZn",
    "outputId": "778f37e3-35a5-46be-cac6-784ac8d160db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.77383477e-05 3.89562266e-10 3.21040600e-10 ... 2.12561312e-11\n",
      " 4.32142934e-06 9.99996485e-01]\n"
     ]
    }
   ],
   "source": [
    "# Predictions on the validation set\n",
    "y_pred = model_cat.predict_proba(valid_data)[:, model_cat.classes_ == 1][:, 0]\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r1APOO1aFHGj"
   },
   "outputs": [],
   "source": [
    "# Compute the performance metric\n",
    "ypred_scoreordered = valid_labels[np.argsort(y_pred)]\n",
    "N = np.sum(valid_labels == 0)\n",
    "P = np.sum(valid_labels == 1)\n",
    "FP = 0\n",
    "TP = 0\n",
    "for i in range(len(y_pred) - 1, -1, -1):\n",
    "    if (ypred_scoreordered[i] == 1):\n",
    "        TP = TP + 1\n",
    "    else:\n",
    "        FP = FP + 1\n",
    "    if (FP / N > 10**-4):\n",
    "        FP = FP - 1\n",
    "        break\n",
    "        \n",
    "print(\"CATBOOST RESULTS ----\")\n",
    "print(\"Number of true positive : {}\".format(TP))\n",
    "print(\"Number of false positive : {}\".format(FP))\n",
    "print(\"Number of total positive : {}\".format(P))\n",
    "print(\"Number of total negative : {}\".format(N))\n",
    "print(\"For the smallest FPR <= 10^-4 (i.e., \", FP / N, \") TPR = \", TP / P, \".\", sep = \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7VqPm1dQn23B"
   },
   "outputs": [],
   "source": [
    "# Get features importances\n",
    "features_imp = model_cat.get_feature_importance(prettified=True)\n",
    "\n",
    "new_column = []\n",
    "column_name = list(Xtraincopy.columns)\n",
    "feat_imp_idx = list(features_imp['Feature Id'].values.astype(int))\n",
    "for i in range(0, len(feat_imp_idx)):\n",
    "  sel = column_name[feat_imp_idx[i]]\n",
    "  new_column.append(sel)\n",
    "\n",
    "features_imp[\"Feature names\"] = new_column\n",
    "\n",
    "plt.figure(figsize=(5,8))\n",
    "sns.barplot(x=\"Importances\", y=\"Feature names\", data=features_imp, color='lightblue', orient='h')\n",
    "plt.title(\"Importances of the features\", fontweight='bold', fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vpAbZJdZS3GA"
   },
   "outputs": [],
   "source": [
    "# To save the model \n",
    "# model_cat.save_model(\"/content/drive/My Drive/MDI343 - Data challenge/model/catboost_985201693782037.cbm\", format=\"cbm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2_hWugUoP0CD"
   },
   "source": [
    "##### <b><i>Xgboost</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eJXzFJsDNAdc"
   },
   "outputs": [],
   "source": [
    "# ---- Best hyperparameters\n",
    "# learning_rate=0.06\n",
    "# n_estimators=2200\n",
    "# max_depth=9\n",
    "# subsample=0.8\n",
    "# colsample_bytree=0.7\n",
    "# gamma=5\n",
    "# min_child_weight=7\n",
    "# reg_lambda=6\n",
    "# objective='binary:logistic'\n",
    "# task_type='GPU'\n",
    "# random_seed=0\n",
    "# n_jobs=-1\n",
    "# tree_method='gpu_hist'\n",
    "# random_state=12345"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PQ4WgFcvwAbU"
   },
   "outputs": [],
   "source": [
    "model_xgboost = xgb.XGBClassifier(\n",
    "    # Parameters\n",
    "    learning_rate=0.06,\n",
    "    n_estimators=2200,\n",
    "    max_depth=9, \n",
    "    subsample=0.8,\n",
    "    colsample_bytree=0.7,\n",
    "    gamma=5,\n",
    "    min_child_weight=7,\n",
    "\n",
    "    # Regulariaztion\n",
    "    reg_lambda=6,\n",
    "    objective='binary:logistic',\n",
    "\n",
    "    # Using GPU\n",
    "    n_jobs=-1,\n",
    "    tree_method='gpu_hist',\n",
    "\n",
    "    # Seed\n",
    "    random_state=12345\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "l2EG6eUYK0Wz"
   },
   "outputs": [],
   "source": [
    "# Train the model\n",
    "model_xgboost.fit(\n",
    "    train_data, \n",
    "    train_labels,\n",
    "    eval_set=[(train_data, train_labels), (valid_data, valid_labels)], \n",
    "    verbose=True,\n",
    "    eval_metric=\"error\",\n",
    "    early_stopping_rounds=45\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "H3Ga8ixju4yO"
   },
   "source": [
    "<div align='justify'>I used a learning curve to tune the model.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1574,
     "status": "ok",
     "timestamp": 1582908907577,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "ORfecDt0w8FF",
    "outputId": "626a979f-55b5-41a5-d078-caff48bf9284"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OJ4rAT/dC2gjYvx3euDN8FTTGmBC0Ghj8nMHNwHzcJPEcVV0lIneLyCU+26NA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\nYIwxpgELDMYYYxqwwGCMMaaB/w++GWXeH0Qa6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print the learning curve\n",
    "epochs = len(model_xgboost.evals_result_['validation_0']['error'])\n",
    "plt.plot(np.array(range(0, epochs)), model_xgboost.evals_result_[\"validation_0\"][\"error\"], label=\"train\")\n",
    "plt.plot(np.array(range(0, epochs)), model_xgboost.evals_result_[\"validation_1\"][\"error\"], label=\"valid\")\n",
    "plt.title(\"Learning curve XGBoost\", fontweight='bold', fontsize=12)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2879,
     "status": "ok",
     "timestamp": 1582908913226,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "b3UZDxCVV_9l",
    "outputId": "a9452854-aff4-4146-b72f-89864314985f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.5382744e-07 2.1928279e-06 3.2800995e-07 ... 1.2634838e-06 1.3062867e-06\n",
      " 4.2155825e-06]\n"
     ]
    }
   ],
   "source": [
    "# Predictions on the validation set\n",
    "y_pred = model_xgboost.predict_proba(valid_data)[:, model_xgboost.classes_ == 1][:, 0]\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1186,
     "status": "ok",
     "timestamp": 1582908915185,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "-ROXnZTuY97Z",
    "outputId": "1f658e68-d776-4027-c61a-3c8b0c66b723"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBOOST RESULTS ----\n",
      "Number of true positive : 66494\n",
      "Number of false positive : 91\n",
      "Number of total positive : 67305\n",
      "Number of total negative : 912767\n",
      "For the smallest FPR <= 10^-4 (i.e., 9.969685582410406e-05) TPR = 0.9879503751578634.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the performance metric\n",
    "yvalid_scoreordered = valid_labels[np.argsort(y_pred)]\n",
    "N = np.sum(valid_labels == 0)\n",
    "P = np.sum(valid_labels == 1)\n",
    "FP = 0\n",
    "TP = 0\n",
    "for i in range(len(y_pred) - 1, -1, -1):\n",
    "    if (yvalid_scoreordered[i] == 1):\n",
    "        TP = TP + 1\n",
    "    else:\n",
    "        FP = FP + 1\n",
    "    if (FP / N > 10**-4):\n",
    "        FP = FP - 1\n",
    "        break\n",
    "print(\"XGBOOST RESULTS ----\")\n",
    "print(\"Number of true positive : {}\".format(TP))\n",
    "print(\"Number of false positive : {}\".format(FP))\n",
    "print(\"Number of total positive : {}\".format(P))\n",
    "print(\"Number of total negative : {}\".format(N))\n",
    "print(\"For the smallest FPR <= 10^-4 (i.e., \", FP / N, \") TPR = \", TP / P, \".\", sep = \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "g9EBWeNZZThJ"
   },
   "outputs": [],
   "source": [
    "# To save the model \n",
    "# model_xgboost.save_model(\"/content/drive/My Drive/MDI343 - Data challenge/model/xgboost_9729812049624842.model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bxOsk81NiRM2"
   },
   "source": [
    "### **5. Prediction on the test sample**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bWkwtWyfZW5w"
   },
   "outputs": [],
   "source": [
    "# Model chosen\n",
    "model = model_xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 13596,
     "status": "ok",
     "timestamp": 1582908151236,
     "user": {
      "displayName": "Abou zeid Pierre",
      "photoUrl": "",
      "userId": "02514713738798105230"
     },
     "user_tz": -60
    },
    "id": "nZ5GDfd_PHyl",
    "outputId": "27fa8135-b93a-47b4-8ea8-ccf8849683c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3768311,)\n"
     ]
    }
   ],
   "source": [
    "# Classify the provided test data\n",
    "Xsubmission = Xtest.to_numpy()\n",
    "ytest = model.predict_proba(Xsubmission)[:, model.classes_ == 1][:, 0]\n",
    "print(ytest.shape)\n",
    "np.savetxt('/content/drive/My Drive/Camille/ytest_challenge_camillecochener.csv', ytest, fmt = '%1.15f', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SKKaVwtC7P3F"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [
    "P6SCh0R3uQVd"
   ],
   "machine_shape": "hm",
   "name": "COCHENERCamille_datachallenge.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
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  "varInspector": {
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
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 },
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}