Brocco_Gianfrate_Roveroni_Tesser.ipynb

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    "# Cognitive, Behavioral and Social Data\n",
    "---\n",
    "##### A.A. 2022/2023\n",
    "##### Authors\n",
    "| Student | ID |\n",
    "| --- | --- |\n",
    "| Brocco Mattia | 2044714 |\n",
    "| Gianfrate Flavia | 2038501 |\n",
    "| Roveroni Emma | 2058618 |\n",
    "| Tesser Marco | 2058223 |\n",
    "\n",
    "## *Aim of the project*\n",
    "This study focuses on the replicability of finding relevant predictors for lie detection in various psychometric tests concerning medicine, behavioral science and data science that have been compiled twice, once honestly and once dishonestly.\n",
    "More precisely, the goal is to develop a framework for feature selection that leads to good and similar results for different models used for the discrimination of honesty and dishonesty of test responses. Accuracy, Top-5 stability and Accuracy Standard Deviation are the metrics used to evaluate the results.\n",
    "\n",
    "## *Datasets*\n",
    "The datasets used for this project are 16 and each of them represents the results of a given questionnaire. Each questionnaire is related to a different topic and so has specific questions. These datasets are independent between them and for each one of them, the subjects are asked to answer honestly to the question and then to retake the questionnaire but this time faking good or bad, depending on the objective of the questionnaire. This means that in each dataset we have the same number of honest and dishonest answers. The datasets are built in the same way: each column corresponds to a question and the target column is an honest/dishonest attribute. In particular, each questionnaire is composed of Likert-scaled questions: the answers of the respondents measure their agreement with the questions. In the following table are reported the name of each dataset with the related topics and dimensions. \n",
    "\n",
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    ".tg .tg-0pky{border-color:inherit;text-align:left;vertical-align:top}\n",
    "</style>\n",
    "<table class=\"tg\">\n",
    "<thead>\n",
    "  <tr>\n",
    "    <th class=\"tg-0pky\">Name </th>\n",
    "    <th class=\"tg-0pky\">Topic</th>\n",
    "    <th class=\"tg-0pky\">Faking good/faking bad</th>\n",
    "    <th class=\"tg-0pky\">Number of samples</th>\n",
    "    <th class=\"tg-0pky\">Numbers of features</th>\n",
    "  </tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">DT_df_CC</td>\n",
    "    <td class=\"tg-0pky\">Short Dark Triad 3 for child costudy</td>\n",
    "    <td class=\"tg-0pky\">Faking good </td>\n",
    "    <td class=\"tg-0pky\">482</td>\n",
    "    <td class=\"tg-0pky\">27</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">DT_df_JI</td>\n",
    "    <td class=\"tg-0pky\">Short Dark Triad 3  for a job interview </td>\n",
    "    <td class=\"tg-0pky\">Faking good</td>\n",
    "    <td class=\"tg-0pky\">864</td>\n",
    "    <td class=\"tg-0pky\">27</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">PRMQ_df</td>\n",
    "    <td class=\"tg-0pky\">Identify memory difficulties</td>\n",
    "    <td class=\"tg-0pky\">Faking bad </td>\n",
    "    <td class=\"tg-0pky\">1404</td>\n",
    "    <td class=\"tg-0pky\">16</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">PCL5_df</td>\n",
    "    <td class=\"tg-0pky\">Identify victims of PTSD</td>\n",
    "    <td class=\"tg-0pky\">Faking bad</td>\n",
    "    <td class=\"tg-0pky\">402</td>\n",
    "    <td class=\"tg-0pky\">20</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">NAQ_R_df </td>\n",
    "    <td class=\"tg-0pky\">Identify possible victims of mobbing</td>\n",
    "    <td class=\"tg-0pky\">Faking bad</td>\n",
    "    <td class=\"tg-0pky\">712</td>\n",
    "    <td class=\"tg-0pky\">22</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">PHQ9_GAD7_df</td>\n",
    "    <td class=\"tg-0pky\">Identify possible victims of anxious-depressive syndrom</td>\n",
    "    <td class=\"tg-0pky\">Faking bad</td>\n",
    "    <td class=\"tg-0pky\">1118</td>\n",
    "    <td class=\"tg-0pky\">16</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">PID5_df</td>\n",
    "    <td class=\"tg-0pky\">Identify mental disorders</td>\n",
    "    <td class=\"tg-0pky\">Faking bad</td>\n",
    "    <td class=\"tg-0pky\">824</td>\n",
    "    <td class=\"tg-0pky\">220</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">sPID5_df</td>\n",
    "    <td class=\"tg-0pky\">Identify mental disorders </td>\n",
    "    <td class=\"tg-0pky\">Faking bad</td>\n",
    "    <td class=\"tg-0pky\">1038</td>\n",
    "    <td class=\"tg-0pky\">25</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">PRFQ_df</td>\n",
    "    <td class=\"tg-0pky\">Specific caregivers' ability to mentalize with their children</td>\n",
    "    <td class=\"tg-0pky\">Faking good</td>\n",
    "    <td class=\"tg-0pky\">678</td>\n",
    "    <td class=\"tg-0pky\">18</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">IESR_df</td>\n",
    "    <td class=\"tg-0pky\">Identify possible victims of PTSD</td>\n",
    "    <td class=\"tg-0pky\">Faking bad</td>\n",
    "    <td class=\"tg-0pky\">358</td>\n",
    "    <td class=\"tg-0pky\">22</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">R_NEO_PI_df</td>\n",
    "    <td class=\"tg-0pky\">Personality questionnaire (Big5)</td>\n",
    "    <td class=\"tg-0pky\">Faking good</td>\n",
    "    <td class=\"tg-0pky\">77687</td>\n",
    "    <td class=\"tg-0pky\">30</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">RAW_DDDT_df</td>\n",
    "    <td class=\"tg-0pky\">Identify Dark Triad personality</td>\n",
    "    <td class=\"tg-0pky\">Faking bad</td>\n",
    "    <td class=\"tg-0pky\">986</td>\n",
    "    <td class=\"tg-0pky\">12</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">IADQ_df</td>\n",
    "    <td class=\"tg-0pky\">Identify adjustment disorder (stress response syndrome)</td>\n",
    "    <td class=\"tg-0pky\">Faking bad</td>\n",
    "    <td class=\"tg-0pky\">450</td>\n",
    "    <td class=\"tg-0pky\">9</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">BF_df_CTU</td>\n",
    "    <td class=\"tg-0pky\">Job interview for a salesperson position</td>\n",
    "    <td class=\"tg-0pky\">Faking good</td>\n",
    "    <td class=\"tg-0pky\">442</td>\n",
    "    <td class=\"tg-0pky\">10</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">BF_df_OU</td>\n",
    "    <td class=\"tg-0pky\">Job interview for in humanitarian organization</td>\n",
    "    <td class=\"tg-0pky\">Faking good</td>\n",
    "    <td class=\"tg-0pky\">460</td>\n",
    "    <td class=\"tg-0pky\">10</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td class=\"tg-0pky\">BF_df_V</td>\n",
    "    <td class=\"tg-0pky\">Obtain child costudy </td>\n",
    "    <td class=\"tg-0pky\">Faking good</td>\n",
    "    <td class=\"tg-0pky\">486</td>\n",
    "    <td class=\"tg-0pky\">10</td>\n",
    "  </tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "## *Approaches used*\n",
    "The approaches developed in this project to select the features are the following: \n",
    "  1. **PCA**: select 20% of the total number of features using the principal component analysis.\n",
    "  2. **Permutation importance**: fitted on a random forest, with features selected based on t-test.\n",
    "  3. **Mutual Information**: the features selected by the Joint Mutual Information Maximization (JMIM) algorithm with an importance score of at least 0.8 out of 1 are used.\n",
    "\n",
    "Before applying the methods, the datasets are split into training and test (70%-30%) and for every feature, the mean and the standard deviation are computed in order to scale that feature: $Z=\\frac{X-\\mu}{\\sigma}$. \n",
    "These three methods are independent of each other and each one of them is going to be described in depth later on. \n",
    "\n",
    "## *Models used*\n",
    "Each one of the approaches considered in this project, as mentioned before, selects a number of features from the corresponding original dataset, and then these selected features are used to train different models and to observe their performance. The models trained in this project are: \n",
    "  1. Logistic regression model on all the features (Full LR)\n",
    "  2. Logistic regression model on selected features (LR)\n",
    "  3. Support vector machine (SVM)\n",
    "  4. Random forest (RF)\n",
    "  5. Multi-layer perceptron classifier (MLP) \n",
    "\n",
    "For each of these models is also computed the related accuracy in order to see firstly how good that model is performing with the selected features and secondly to compare the models between them in order to figure out if the selected features give similar performances among all the models. \n",
    "A logistic regression with all the features is trained at the beginning. In this way, it’s possible to have a comparison between the results obtained with the selected features.\n",
    "\n",
    "## *Metrics*\n",
    "\n",
    "* Accuracy: ratio of correct predictions over the number of instances. This has been chosen as all the datasets show a fairly balanced number of examples per class (all are binary classification tasks). The accuracy is computed on the full model (Full LR) as well as all the other four models used for benchmarking and trained only on the subset of features selected by each of the procedures in scope.\n",
    "* Accuracy Standard Deviation: standard deviation of the four models (i.e. LR, SVM, RF, MLP) fitted on the subset of selected features. It is a measure of the consistency of the classification performance across different models, thus the lower the better.\n",
    "* Top-5 stability: a more specific metric for assessing consistency across models (i.e. LR, SVM, RF, MLP). It takes into account the first five most important features used by each of the models, the formula developed is:  \n",
    "<center>\n",
    "    $TOP5 Stability=1-\\big(\\frac{1}{(\\# models-1)*\\min{(5,|\\Omega|)}} \\sum^{\\min{(5,|\\Omega|)}}_{i=1}{|\\beta_{i}|-1}\\big)$\n",
    "</center>\n",
    "where $\\Omega$ is the set of features selected by a procedure, i.e. $\\Omega=\\{\\beta_1,...,\\beta_n\\}$; $\\beta_i$ is a vector with the feature selected with importance $i$ across the models (notice that in our case $\\# models=4$). Finally, $|\\beta_i|$ is the number of unique values in $\\beta_i$.</p>\n",
    "\n",
    "\n",
    "## *Structure of the code*\n",
    "```\n",
    "Brocco_Gianfrate_Roveroni_Tesser.ipynb (main notebook)\n",
    "├── engine.py (pipeline for Mutual Information & JMIM)\n",
    "|    └── Classification\n",
    "|        ├── prepare_data : data import and train-test split.\n",
    "|        ├── variable_selection : Mutual information feature selection procedure\n",
    "|        ├── benchmark_models : model benchmarking.\n",
    "|        └── mutual_info : JMIM feature selection procedure.\n",
    "├── evaluation.py (functions for assessing performances)\n",
    "|    ├── stability_metric : compute top5-stability metric.\n",
    "|    ├── lasso_benchmark : further feature selection to assess the strength of regularization.\n",
    "|    ├── graphical_lasso_benchmark : plot of the previous function.\n",
    "|    ├── just_benchmarking : model benchmarking.\n",
    "|    └── summary_table : comprehensive table of results obtained (always diplayed here)\n",
    "├── pca_engine.py (dedicated engine for PCA)\n",
    "|    ├── prepare_data : data import.\n",
    "|    ├── pca_ds_acc : feature selection procedure.\n",
    "|    └── trts_split : split train-test for model benchmarking.\n",
    "├── summary_graphs.py (functions for graphs)\n",
    "|    ├── accuracy_barplot : plot of mean accuracies of the models used per dataset used with standard errors.\n",
    "|    ├── accuracy_std : plot of standard deviation of accuracies of the models used per dataset.\n",
    "|    └── faking_type_comparison : plot of accuracies (of models used and full model) per dataset, split by faking type.\n",
    "└── support.py (other functions used during development)\n",
    "     ├── likelihood_ratio_test : Wilks test to assess the goodness of fit of two nested models.\n",
    "     ├── D3_pruning : cost-complexity pruning\n",
    "     ├── sorted_importance_index : finds the 5 most important features for each model (always displayed here)\n",
    "     ├── scores_table : prints the number of features selected\n",
    "     ├── build_MLP : build and train a shallow neural network\n",
    "     └── data_to_feather : technical details for file coonversion and sharing\n",
    "```"
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    "import os\n",
    "\n",
    "# COPIA QUI LA DIRECTORY DOVE HAI R\n",
    "os.environ[\"R_HOME\"] = r\"C:\\Program Files\\R\\R-4.0.2\"\n",
    "#os.environ[\"R_LIBS_USER\"] = r\"C:/Program Files/R/R-4.0.2/library/stats/libs/x64\"\n",
    "\n",
    "import sys\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import support\n",
    "import evaluation\n",
    "import pca_engine\n",
    "import summary_graphs\n",
    "from engine import Classification\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "data_dir = \".\\\\data\"\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.options.display.max_columns = 500"
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    "### Principal Components Analysis (PCA)"
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    "The first attempt realizes to accomplish the request of the project involves the use of Principal Component Analysis (PCA).\n",
    "The starting point for this approach is that the number of features that need to be kept in the dataset is 20% of the total number of features. \n",
    "The first step is to compute a PCA that selects the number of components such that the amount of variance explained is 90%. \n",
    "Once the PCA has been calculated, it provides useful information on how to decide which variables will be kept in each of the datasets.  \n",
    "This is done through (i) a vector of the percentages of variance explained by each of the selected components, (ii) a matrix that stores, for each of the obtained components, a value for each of the initial set of variables. This value indicates how influential that feature is for a given component thus, the higher the value the more important the feature is. These values of importance are used later to establish the order of selection of the features.  \n",
    "Subsequently, the percentage of how much each component is \"important\" is computed, where importance, in this case, means how much variance is explaining that component with respect to the total.\n",
    "This information is very useful in deciding how many variables it is right to take from each component. For each component, a number of variables are taken such that it is proportionate to how much variability that component explains compared to our total of about 90%.\n",
    "The selected features, after being divided into train and test sets, are used to fit and train the different models (logistic regression, random forest, SVM, and a neural network). \n",
    "The results accomplished with this approach are shown in the following table and plots.\n",
    "\n",
    "The code reference can be found in the file `pca_engine.py` and the function is `pca_ds_acc`.\n",
    "\n",
    "\n",
    "**Observation**: The same procedure was experimentally run with resized datasets, i.e. the PCA was performed with the datasets that contain only the honest samples. With these different datasets, the results were actually worse and thus we decided to maintain the complete datasets."
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      "BF_df_CTU\n"
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      "5/5 [==============================] - 0s 1ms/step\n",
      "Train size: 309\n",
      "Selected 2 features out of 10\n",
      "[8, 6]\n",
      "                     Accuracy  FI 1  FI 2\n",
      "Full Logit           0.774436   NaN   NaN\n",
      "Logistic Regression  0.736842   6.0   8.0\n",
      "SVC                  0.736842   6.0   8.0\n",
      "Random Forest        0.736842   6.0   8.0\n",
      "Neural Network       0.669173   6.0   8.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 1.0\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "BF_df_OU\n"
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      "5/5 [==============================] - 0s 2ms/step\n",
      "Train size: 322\n",
      "Selected 2 features out of 10\n",
      "[1, 6]\n",
      "                     Accuracy  FI 1  FI 2\n",
      "Full Logit            0.84058   NaN   NaN\n",
      "Logistic Regression   0.76087   6.0   1.0\n",
      "SVC                   0.76087   6.0   1.0\n",
      "Random Forest         0.76087   6.0   1.0\n",
      "Neural Network        0.76087   6.0   1.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 1.0\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "BF_df_V\n"
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      "5/5 [==============================] - 0s 2ms/step\n",
      "Train size: 340\n",
      "Selected 2 features out of 10\n",
      "[1, 7]\n",
      "                     Accuracy  FI 1  FI 2\n",
      "Full Logit           0.767123   NaN   NaN\n",
      "Logistic Regression  0.691781   7.0   1.0\n",
      "SVC                  0.684932   7.0   1.0\n",
      "Random Forest        0.684932   7.0   1.0\n",
      "Neural Network       0.678082   7.0   1.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 1.0\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "DT_df_CC\n"
     ]
    },
    {
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       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 3ms/step\n",
      "Train size: 337\n",
      "Selected 6 features out of 27\n",
      "[1, 4, 10, 19, 21, 23]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.682759   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.675862  10.0   4.0   1.0  19.0  21.0\n",
      "SVC                  0.689655  10.0   4.0   1.0  19.0  21.0\n",
      "Random Forest        0.710345  10.0   4.0   1.0  23.0  21.0\n",
      "Neural Network       0.675862  10.0  21.0   4.0   1.0  19.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.667\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "DT_df_JI\n"
     ]
    },
    {
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      },
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 2ms/step\n",
      "Train size: 604\n",
      "Selected 6 features out of 27\n",
      "[0, 1, 2, 12, 15, 19]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.661538   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.600000   2.0  19.0  15.0  12.0   0.0\n",
      "SVC                  0.596154   2.0  19.0  15.0  12.0   0.0\n",
      "Random Forest        0.573077   2.0  19.0  15.0  12.0   1.0\n",
      "Neural Network       0.626923   2.0  15.0  19.0  12.0   0.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.8\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "IADQ_df\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 2ms/step\n",
      "Train size: 315\n",
      "Selected 2 features out of 9\n",
      "[1, 7]\n",
      "                     Accuracy  FI 1  FI 2\n",
      "Full Logit           0.844444   NaN   NaN\n",
      "Logistic Regression  0.844444   1.0   7.0\n",
      "SVC                  0.844444   1.0   7.0\n",
      "Random Forest        0.837037   1.0   7.0\n",
      "Neural Network       0.725926   1.0   7.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 1.0\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "IESR_df\n"
     ]
    },
    {
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      ]
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 3ms/step\n",
      "Train size: 250\n",
      "Selected 5 features out of 22\n",
      "[4, 8, 13, 15, 18]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.925926   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.925926   8.0  18.0  13.0  15.0   4.0\n",
      "SVC                  0.916667   8.0  18.0  13.0  15.0   4.0\n",
      "Random Forest        0.907407  13.0   8.0  18.0  15.0   4.0\n",
      "Neural Network       0.870370  18.0   8.0  13.0   4.0  15.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.6\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "NAQ_R_df\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step\n",
      "Train size: 498\n",
      "Selected 5 features out of 22\n",
      "[8, 9, 12, 14, 19]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.948598   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.967290   9.0  12.0   8.0  19.0  14.0\n",
      "SVC                  0.967290   9.0  12.0   8.0  19.0  14.0\n",
      "Random Forest        0.953271  19.0   8.0   9.0  12.0  14.0\n",
      "Neural Network       0.967290   9.0  19.0  12.0   8.0  14.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.533\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PCL5_df\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step\n",
      "Train size: 281\n",
      "Selected 4 features out of 20\n",
      "[1, 10, 4, 12]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4\n",
      "Full Logit           0.785124   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.793388   4.0   1.0  10.0  12.0\n",
      "SVC                  0.834711   4.0   1.0  12.0  10.0\n",
      "Random Forest        0.809917   4.0   1.0  12.0  10.0\n",
      "Neural Network       0.809917   4.0   1.0  12.0  10.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.833\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PHQ9_GAD7_df\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 1ms/step\n",
      "Train size: 782\n",
      "Selected 3 features out of 16\n",
      "[1, 13, 7]\n",
      "                     Accuracy  FI 1  FI 2  FI 3\n",
      "Full Logit           0.988095   NaN   NaN   NaN\n",
      "Logistic Regression  0.973214   7.0   1.0  13.0\n",
      "SVC                  0.973214   7.0   1.0  13.0\n",
      "Random Forest        0.958333   7.0   1.0  13.0\n",
      "Neural Network       0.967262   7.0   1.0  13.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 1.0\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PID5_df\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 0s 2ms/step\n",
      "Train size: 576\n",
      "Selected 43 features out of 220\n",
      "[1, 131, 4, 6, 8, 138, 14, 151, 152, 153, 23, 35, 36, 165, 164, 171, 172, 175, 176, 49, 53, 182, 184, 62, 191, 197, 71, 201, 204, 205, 206, 211, 85, 216, 218, 219, 92, 94, 98, 101, 103, 108, 121]\n",
      "                     Accuracy  FI 1   FI 2   FI 3   FI 4   FI 5\n",
      "Full Logit           0.959677   NaN    NaN    NaN    NaN    NaN\n",
      "Logistic Regression  0.939516   1.0   35.0  165.0  171.0  101.0\n",
      "SVC                  0.931452   1.0  165.0   35.0  171.0  101.0\n",
      "Random Forest        0.943548   1.0    4.0   35.0  108.0  121.0\n",
      "Neural Network       0.923387   1.0  191.0    4.0  131.0  138.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.4\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PRFQ_df\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step\n",
      "Train size: 474\n",
      "Selected 4 features out of 18\n",
      "[3, 13, 14, 7]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4\n",
      "Full Logit           0.936275   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.857843   7.0  13.0   3.0  14.0\n",
      "SVC                  0.843137   7.0  13.0   3.0  14.0\n",
      "Random Forest        0.852941   7.0  14.0  13.0   3.0\n",
      "Neural Network       0.833333   7.0  14.0  13.0   3.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.75\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PRMQ_df\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 2ms/step\n",
      "Train size: 982\n",
      "Selected 3 features out of 16\n",
      "[0, 1, 7]\n",
      "                     Accuracy  FI 1  FI 2  FI 3\n",
      "Full Logit           0.907583   NaN   NaN   NaN\n",
      "Logistic Regression  0.876777   1.0   7.0   0.0\n",
      "SVC                  0.872038   1.0   7.0   0.0\n",
      "Random Forest        0.890995   7.0   1.0   0.0\n",
      "Neural Network       0.876777   7.0   1.0   0.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.778\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "RAW_DDDT\n"
     ]
    },
    {
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       "version_minor": 0
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 2ms/step\n",
      "Train size: 690\n",
      "Selected 3 features out of 12\n",
      "[8, 0, 5]\n",
      "                     Accuracy  FI 1  FI 2  FI 3\n",
      "Full Logit           0.746622   NaN   NaN   NaN\n",
      "Logistic Regression  0.662162   0.0   8.0   5.0\n",
      "SVC                  0.658784   0.0   8.0   5.0\n",
      "Random Forest        0.706081   0.0   5.0   8.0\n",
      "Neural Network       0.655405   0.0   8.0   5.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.778\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "R_NEO_PI\n"
     ]
    },
    {
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       "version_minor": 0
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "729/729 [==============================] - 1s 1ms/step\n",
      "Train size: 54380\n",
      "Selected 5 features out of 30\n",
      "[1, 5, 11, 21, 29]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.907195   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.900459  21.0   5.0  29.0   1.0  11.0\n",
      "SVC                  0.898271  21.0   5.0  29.0  11.0   1.0\n",
      "Random Forest        0.901146  21.0   5.0  29.0   1.0  11.0\n",
      "Neural Network       0.898743  21.0   5.0  29.0   1.0  11.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.867\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "sPID-5_df\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 1ms/step\n",
      "Train size: 726\n",
      "Selected 5 features out of 25\n",
      "[4, 7, 8, 20, 23]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.958333   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.935897  23.0  20.0   4.0   8.0   7.0\n",
      "SVC                  0.939103  23.0   4.0  20.0   8.0   7.0\n",
      "Random Forest        0.942308  23.0  20.0   4.0   7.0   8.0\n",
      "Neural Network       0.932692  23.0  20.0   4.0   8.0   7.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.733\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data_collection_pca = {}\n",
    "for dataset in [f for f in os.listdir(data_dir) if \"parquet\" in f]:\n",
    "    print(dataset.split(\".\")[0])\n",
    "    \n",
    "    data_iter = pca_engine.prepare_data(f\"{data_dir}\\\\{dataset}\", \"CONDITION\")\n",
    "    #data_mwu = pca_engine.new_mwu_data(data_iter)\n",
    "    #pca_engine.scree_plot(data_mwu)\n",
    "    \n",
    "    var_sel = pca_engine.pca_ds_acc(data_iter)\n",
    "    X_tr, X_ts, y_tr, y_ts = pca_engine.trts_split(data_iter)\n",
    "    \n",
    "    perfs, stability = Classification().benchmark_models(X_tr, X_ts, y_tr, y_ts, var_sel)\n",
    "    \n",
    "    data_collection_pca[dataset.split(\".\")[0]] = [X_tr, X_ts, y_tr, y_ts, var_sel, perfs, stability]\n",
    "    print(var_sel)\n",
    "    print(perfs)\n",
    "    print()\n",
    "    print(\"==> TOP5 STABILITY SCORE: {}\".format(stability))\n",
    "    print(\"-\" * 100)\n",
    "    print(\"-\" * 100)\n",
    "    print(\"-\" * 100)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8xZ3k46B0LtI",
   "metadata": {
    "id": "8xZ3k46B0LtI"
   },
   "source": [
    "#### Summarize results\n",
    "From this table, it’s possible to see how the number of the selected features changes after only ```20%``` of the total is considered. What’s more, in the table a first comparison between the obtained accuracy is shown. Looking at the accuracies of the models with the selected features, it’s possible to observe that these values are very close between them. This is also supported by the values of the standard deviation which are almost all very low: the accuracies of the different models do not distance a lot from the mean of these values. On the other hand, for almost all the datasets the accuracies of the models with the selected features are lower than the accuracy of the model with all the features. Only for two datasets, NAQ_R and PCL5,  this does not happen: in this case the accuracies of the model with the selected features overcome one of the models with all the features. All these conclusions can be better seen in the following plots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0443129f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T14:42:17.154151Z",
     "start_time": "2023-01-05T14:42:16.664272Z"
    },
    "id": "0443129f",
    "outputId": "6ded0bbc-417c-4db1-fd4b-68899dc36c33"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dataset name</th>\n",
       "      <th>Sample size</th>\n",
       "      <th>Training size</th>\n",
       "      <th>Number of Features</th>\n",
       "      <th>Selected Features</th>\n",
       "      <th>Feat. Top5-Stability</th>\n",
       "      <th>Accuracy - Logit all features</th>\n",
       "      <th>Accuracy - Logit</th>\n",
       "      <th>Accuracy - SVM</th>\n",
       "      <th>Accuracy - RF</th>\n",
       "      <th>Accuracy - MLP</th>\n",
       "      <th>Avg Acc. on selected features</th>\n",
       "      <th>Accuracy Std on selected features</th>\n",
       "      <th>Acc. diff. wrt Full logit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BF_df_CTU</td>\n",
       "      <td>442</td>\n",
       "      <td>309</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.034</td>\n",
       "      <td>-0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BF_df_OU</td>\n",
       "      <td>460</td>\n",
       "      <td>322</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BF_df_V</td>\n",
       "      <td>486</td>\n",
       "      <td>340</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT_df_CC</td>\n",
       "      <td>482</td>\n",
       "      <td>337</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DT_df_JI</td>\n",
       "      <td>864</td>\n",
       "      <td>604</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.573</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IADQ_df</td>\n",
       "      <td>450</td>\n",
       "      <td>315</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.837</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.058</td>\n",
       "      <td>-0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IESR_df</td>\n",
       "      <td>358</td>\n",
       "      <td>250</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.024</td>\n",
       "      <td>-0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NAQ_R_df</td>\n",
       "      <td>712</td>\n",
       "      <td>498</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PCL5_df</td>\n",
       "      <td>402</td>\n",
       "      <td>281</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PHQ9_GAD7_df</td>\n",
       "      <td>1118</td>\n",
       "      <td>782</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PID5_df</td>\n",
       "      <td>824</td>\n",
       "      <td>576</td>\n",
       "      <td>220</td>\n",
       "      <td>43</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PRFQ_df</td>\n",
       "      <td>678</td>\n",
       "      <td>474</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.858</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.011</td>\n",
       "      <td>-0.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PRMQ_df</td>\n",
       "      <td>1404</td>\n",
       "      <td>982</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.008</td>\n",
       "      <td>-0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RAW_DDDT</td>\n",
       "      <td>986</td>\n",
       "      <td>690</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.024</td>\n",
       "      <td>-0.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>R_NEO_PI</td>\n",
       "      <td>77687</td>\n",
       "      <td>54380</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sPID-5_df</td>\n",
       "      <td>1038</td>\n",
       "      <td>726</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Dataset name  Sample size  Training size  Number of Features  \\\n",
       "0      BF_df_CTU          442            309                  10   \n",
       "1       BF_df_OU          460            322                  10   \n",
       "2        BF_df_V          486            340                  10   \n",
       "3       DT_df_CC          482            337                  27   \n",
       "4       DT_df_JI          864            604                  27   \n",
       "5        IADQ_df          450            315                   9   \n",
       "6        IESR_df          358            250                  22   \n",
       "7       NAQ_R_df          712            498                  22   \n",
       "8        PCL5_df          402            281                  20   \n",
       "9   PHQ9_GAD7_df         1118            782                  16   \n",
       "10       PID5_df          824            576                 220   \n",
       "11       PRFQ_df          678            474                  18   \n",
       "12       PRMQ_df         1404            982                  16   \n",
       "13      RAW_DDDT          986            690                  12   \n",
       "14      R_NEO_PI        77687          54380                  30   \n",
       "15     sPID-5_df         1038            726                  25   \n",
       "\n",
       "    Selected Features  Feat. Top5-Stability  Accuracy - Logit all features  \\\n",
       "0                   2                 1.000                          0.774   \n",
       "1                   2                 1.000                          0.841   \n",
       "2                   2                 1.000                          0.767   \n",
       "3                   6                 0.667                          0.683   \n",
       "4                   6                 0.800                          0.662   \n",
       "5                   2                 1.000                          0.844   \n",
       "6                   5                 0.600                          0.926   \n",
       "7                   5                 0.533                          0.949   \n",
       "8                   4                 0.833                          0.785   \n",
       "9                   3                 1.000                          0.988   \n",
       "10                 43                 0.400                          0.960   \n",
       "11                  4                 0.750                          0.936   \n",
       "12                  3                 0.778                          0.908   \n",
       "13                  3                 0.778                          0.747   \n",
       "14                  5                 0.867                          0.907   \n",
       "15                  5                 0.733                          0.958   \n",
       "\n",
       "    Accuracy - Logit  Accuracy - SVM  Accuracy - RF  Accuracy - MLP  \\\n",
       "0              0.737           0.737          0.737           0.669   \n",
       "1              0.761           0.761          0.761           0.761   \n",
       "2              0.692           0.685          0.685           0.678   \n",
       "3              0.676           0.690          0.710           0.676   \n",
       "4              0.600           0.596          0.573           0.627   \n",
       "5              0.844           0.844          0.837           0.726   \n",
       "6              0.926           0.917          0.907           0.870   \n",
       "7              0.967           0.967          0.953           0.967   \n",
       "8              0.793           0.835          0.810           0.810   \n",
       "9              0.973           0.973          0.958           0.967   \n",
       "10             0.940           0.931          0.944           0.923   \n",
       "11             0.858           0.843          0.853           0.833   \n",
       "12             0.877           0.872          0.891           0.877   \n",
       "13             0.662           0.659          0.706           0.655   \n",
       "14             0.900           0.898          0.901           0.899   \n",
       "15             0.936           0.939          0.942           0.933   \n",
       "\n",
       "    Avg Acc. on selected features  Accuracy Std on selected features  \\\n",
       "0                           0.720                              0.034   \n",
       "1                           0.761                              0.000   \n",
       "2                           0.685                              0.006   \n",
       "3                           0.688                              0.016   \n",
       "4                           0.599                              0.022   \n",
       "5                           0.813                              0.058   \n",
       "6                           0.905                              0.024   \n",
       "7                           0.964                              0.007   \n",
       "8                           0.812                              0.017   \n",
       "9                           0.968                              0.007   \n",
       "10                          0.934                              0.009   \n",
       "11                          0.847                              0.011   \n",
       "12                          0.879                              0.008   \n",
       "13                          0.671                              0.024   \n",
       "14                          0.900                              0.001   \n",
       "15                          0.938                              0.004   \n",
       "\n",
       "    Acc. diff. wrt Full logit  \n",
       "0                      -0.055  \n",
       "1                      -0.080  \n",
       "2                      -0.082  \n",
       "3                       0.005  \n",
       "4                      -0.062  \n",
       "5                      -0.031  \n",
       "6                      -0.021  \n",
       "7                       0.015  \n",
       "8                       0.027  \n",
       "9                      -0.020  \n",
       "10                     -0.025  \n",
       "11                     -0.089  \n",
       "12                     -0.028  \n",
       "13                     -0.076  \n",
       "14                     -0.008  \n",
       "15                     -0.021  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_summary = evaluation.summary_table(data_collection_pca, stability = True, neural_net = True)\n",
    "pca_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Bx1ZQx7P0xFk",
   "metadata": {
    "id": "Bx1ZQx7P0xFk"
   },
   "source": [
    "In this plot, the mean of the accuracies of the models with the selected features is shown for each dataset. These values are pretty high for almost all the datasets since they are always above the 0.5 value, this means that with this approach the classification between honest and dishonest is not happening as if it is random. The black line represents the standard deviation, which values can be seen better in the following plot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c69b4d57",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T14:42:18.757591Z",
     "start_time": "2023-01-05T14:42:17.158187Z"
    },
    "id": "c69b4d57",
    "outputId": "68f83c2a-5789-4fd7-b430-49056a55a58a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_graphs.accuracy_barplot(pca_summary, save = True, prefix = \"PCA\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "P6a9qo4H00l8",
   "metadata": {
    "id": "P6a9qo4H00l8"
   },
   "source": [
    "Here the values of the standard deviation are better shown: as mentioned before, these values are mainly not that high, meaning that the accuracies of the models with the selected features tend to be very close to their mean.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "670ea8f5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T14:42:20.268833Z",
     "start_time": "2023-01-05T14:42:18.761471Z"
    },
    "id": "670ea8f5",
    "outputId": "60a7ca15-5bd0-47e9-ceaf-48da035fd3ea"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_graphs.accuracy_std(pca_summary, save = True, prefix = \"PCA\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7mrFsB5o05TI",
   "metadata": {
    "id": "7mrFsB5o05TI"
   },
   "source": [
    "In this plot, there is better visualization of the comparison between the accuracies of the logistic regression model with all the features, the logistic regression model with the selected features, the SVM model with the selected features, the random forest model with the selected features and the neural network with the selected features. In this comparison, the datasets are divided into the ones where the subjects are faking good and the ones where the subjects are faking bad. From here it can be better seen what was said before: for almost all the datasets the values of the accuracies with the selected features decrease with respect to one of the models with all the features. \n",
    "\n",
    "Furthermore, it’s possible to infer that there are slight differences between the performances of faking good and faking bad datasets, so this might point out that this approach considers the intention of lying of the subjects. This is clearer by looking at the difference between the average of the `Acc. diff. wrt Full logit` for the faking good datasets and the one for the faking bad (-0.049 vs -0.019), which is not so high but shows that faking bad performs better.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "554a4dc8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T14:42:23.336867Z",
     "start_time": "2023-01-05T14:42:20.272833Z"
    },
    "id": "554a4dc8",
    "outputId": "f41c6ad2-a08e-4fef-d587-bc61b2ee483a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_graphs.faking_type_comparison(pca_summary, prefix = \"PCA\", save = True)"
   ]
  },
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   "cell_type": "markdown",
   "id": "f2f32564",
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   "source": [
    "### Permutation Importance\n",
    "\n",
    "The following approach aims to be model-agnostic even if the implementation of the permutation importance is model-dependent. This is because the latter is considered when applied to a random forest, a type of ensemble learning (a model made of several simpler models) characterized by bootstrapping and feature randomness so that each weaker model (i.e. decision tree) is trained with a different subset of examples and of features<sup>[1]</sup>.<br>\n",
    "\n",
    "The actual phase of feature selection can be broken down into smaller pieces in which several models are fitted in such a way the generalizing power of the selection of a subset of features is increased and the overall procedure is more automatic. In fact, a further split of the training set (80% of it will be denoted as $X_{train}$ and the remainder $X_{val}$) is performed. A decision tree is fitted to $X_{train}$ and is subjected to minimal cost-complexity pruning<sup>[2]</sup>:  \n",
    "\n",
    "> Given $|T|$ the number of terminal nodes of the decision tree, $R(t)$ the impurity of a node $t$, and $R(T_t)$ the impurity of the branch whose root is $t$, we define  \n",
    "> <center>$\\alpha_{eff}=\\frac{R(t)-R(T_t)}{|T|-1}$</center>\n",
    "> the $\\alpha$ of a node. To prune a tree, then it is sufficient to select $\\alpha_{eff}$, so that all non-terminal nodes will be subsequently pruned if their $\\alpha$ is lower than $\\alpha_{eff}$.\n",
    "\n",
    "$\\alpha_{eff}$ is a hyper-parameter, that is selected via a 3-fold cross-validation procedure on $X_{train}$ on a gradient boosting tree<sup>[3]</sup>. Pruning is a regularization technique that aims at reducing the variance of an estimator, thus preventing overfitting (code reference `support.py`, the function is `D3_pruning`). The resulting pruned boosted tree will provide the maximum tree depth to use for the weaker learners of the random forest. Hence, the framework of a model-independent permutation importance takes place with a random forest with 200 weaker estimators that are not fully grown, and it is repeated for 100 times on unseen data, i.e. $X_{val}$.<br>\n",
    "\n",
    "As the permutation importance is iterated several times, it is possible to obtain a sample of the distribution of the importances of each feature. Given $p$ the number of features and $k$ the number of iterations, and denoting $\\gamma_{j}$ the vector of importances of the $j$-th feature (with $\\gamma\\in\\mathbb{R}^{k}$) we have  \n",
    "\n",
    "<center>\n",
    "    $P\\in\\mathbb{R}^{p\\times k}, P=\\begin{bmatrix}\n",
    "                                        \\gamma_1\\\\\n",
    "                                        \\vdots \\\\\n",
    "                                        \\gamma_p\\\\\n",
    "                                       \\end{bmatrix}$\n",
    "</center>\n",
    "\n",
    "We thus defined that $\\gamma_j$ is the sample of the importances for the $j$-th feature. To select only the features that are significantly important, for each $\\gamma_j$ we provide a statistical test whose hypothesis are  \n",
    "\n",
    "<center>\n",
    "    $\\begin{cases}\n",
    "          H_0: & \\hat{\\mu}_{j}=0 \\\\\n",
    "          H_1: & \\hat{\\mu}_{j}>0\n",
    "    \\end{cases}$\n",
    "</center>\n",
    "\n",
    "where $\\hat{\\mu}_{j}$ is the sample mean of $\\gamma_j$. The null hypothesis $H_0$ is rejected for $\\alpha<0.001$.This way only features whose importance is significantly greater than zero are retained, all the others are discarded. We call this subset of feature $A^*$.  \n",
    "The code reference is `engine.py`, the function is `variable_selection`.<br>\n",
    "\n",
    "In order to further confirm the efficacy of the selection, a likelihood ratio test<sup>[4]</sup> (code reference `support.py`, the function is `likelihood_ratio_test`) is performed. Here, we compare the likelihood of two nested models, i.e. a logistic regression fitted on all the features, and one on $A^*$. The test statistic is  \n",
    "\n",
    "<center>\n",
    "    $\\lambda_{LR} = -2\\big[\\log{L\\big(\\theta_{A^*}\\big)}-\\log{L\\big(\\hat{\\theta}}\\big)\\big] $\n",
    "</center>  \n",
    "\n",
    "where $\\log{L\\big(\\theta_{A^*}\\big)}$ refers to the log-likelihood of the model estimated on a subset of features, while $\\log{L\\big(\\hat{\\theta}}\\big)$ refers to the log-likelihood of a full model (i.e. with all the features available). This corresponds to the null hypothesis $H_0$ being \"the models have similar likelihood\", thus implying that its acceptance results in preferring the simpler (i.e. nested) model. We set the confidence level $\\alpha=0.05$. It never happens that $H_0$ is rejected for all the datasets under the scope of this work.<br>\n",
    "\n",
    "\n",
    "##### SUPPORTING PAPERS\n",
    "[1] Leo Breiman. *Random forests*. Machine learning, 45(1):5-32, 2001.  \n",
    "[2] L. Breiman, J. Friedman, R. Olshen, and C. Stone. *Classification and Regression Trees*. Wadsworth, Belmont, CA, 1984.  \n",
    "[3] J. Friedman, Greedy Function Approximation: *A Gradient Boosting Machine*, The Annals of Statistics, Vol. 29, No. 5, 2001  \n",
    "[4] Koch, Karl-Rudolf (1988). Parameter Estimation and Hypothesis Testing in Linear Models. New York: Springer. p. 306  "
   ]
  },
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   "id": "23cb893e",
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     "start_time": "2023-01-05T14:42:23.342310Z"
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      "10635c6ac44d4dc1b0c13e622b544434",
      "4a8c11164fc8429fb88af54d09c28188",
      "de28605e5bea4fd88915aef1e7ad811b",
      "1afca5cbcef5441388911cdd9925c390",
      "2553c09ff0d04e2daaacfae3d23155bb",
      "c2f774f3c46f4b4aa62754c20f4c7c4b",
      "3aa6d730466343d3ab978cc6e01da0f1",
      "424223dd030a40bab27b0bf8c66f077a",
      "eecdae332cbc42f8b20048e398b22c02",
      "015dccd4a442471c999b20e291de9acd",
      "529d548dda034b8aaa335279ab48ae43",
      "3500b57e1dcc4480856ec130a6a32365",
      "4980d5b9acb0416b8468c1a8ed620747",
      "45137880a3474a7e880ef9588102afea",
      "521ea1f663f04e5d80808ca3fba25ae5",
      "cfac468ad1bd4373b7672d876be322b8",
      "47c5dc7bf6a147779ff3043ae35307ea",
      "adc3f6ac700140e0838a65c29d13de1d",
      "5f2511d3f0d04aeabd8210712bc3515c",
      "af32947186e847b29ff0b1162ca01632",
      "2a25868602de4498a7b8c7cafa825c51",
      "b8a7b4d6c647417583c20ad762958f55",
      "0ab7d8dd61bd4068914659504c2aa2c3",
      "2bbcb2ecc8ab456fa2ff29bd25a6cfe7"
     ]
    },
    "id": "23cb893e",
    "outputId": "fbc19bb3-a015-499d-d0b3-7be3d0ff969f",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BF_df_CTU\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eecdae332cbc42f8b20048e398b22c02",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 1ms/step\n",
      "Train size: 309\n",
      "Selected 1 features out of 10\n",
      "{'Features': [6], 'Wilks test p-value': 1.0, 'High correlation': False}\n",
      "                     Accuracy  FI 1\n",
      "Full Logit           0.774436   NaN\n",
      "Logistic Regression  0.736842   6.0\n",
      "SVC                  0.736842   6.0\n",
      "Random Forest        0.736842   6.0\n",
      "Neural Network       0.736842   6.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 1.0\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "BF_df_OU\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "015dccd4a442471c999b20e291de9acd",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 2ms/step\n",
      "Train size: 322\n",
      "Selected 2 features out of 10\n",
      "{'Features': [6, 7], 'Wilks test p-value': 1.0, 'High correlation': False}\n",
      "                     Accuracy  FI 1  FI 2\n",
      "Full Logit           0.833333   NaN   NaN\n",
      "Logistic Regression  0.789855   6.0   7.0\n",
      "SVC                  0.789855   6.0   7.0\n",
      "Random Forest        0.797101   6.0   7.0\n",
      "Neural Network       0.775362   6.0   7.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 1.0\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "BF_df_V\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "529d548dda034b8aaa335279ab48ae43",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 2ms/step\n",
      "Train size: 340\n",
      "Selected 1 features out of 10\n",
      "{'Features': [7], 'Wilks test p-value': 1.0, 'High correlation': False}\n",
      "                     Accuracy  FI 1\n",
      "Full Logit           0.760274   NaN\n",
      "Logistic Regression  0.684932   7.0\n",
      "SVC                  0.684932   7.0\n",
      "Random Forest        0.684932   7.0\n",
      "Neural Network       0.684932   7.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 1.0\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "DT_df_CC\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3500b57e1dcc4480856ec130a6a32365",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 2ms/step\n",
      "Train size: 337\n",
      "Selected 6 features out of 27\n",
      "{'Features': [4, 8, 9, 10, 12, 13], 'Wilks test p-value': 1.0, 'High correlation': False}\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.682759   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.703448  13.0  10.0   9.0  12.0   8.0\n",
      "SVC                  0.703448  13.0  10.0   9.0   8.0  12.0\n",
      "Random Forest        0.724138  13.0  10.0   9.0   8.0   4.0\n",
      "Neural Network       0.703448  13.0   4.0   9.0   8.0  10.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.667\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "DT_df_JI\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4980d5b9acb0416b8468c1a8ed620747",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 2ms/step\n",
      "Train size: 604\n",
      "Selected 12 features out of 27\n",
      "{'Features': [1, 2, 6, 8, 9, 10, 15, 16, 17, 19, 20, 23], 'Wilks test p-value': 1.0, 'High correlation': False}\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.661538   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.642308   2.0   6.0  15.0  17.0  16.0\n",
      "SVC                  0.638462   2.0   6.0  15.0  17.0  16.0\n",
      "Random Forest        0.603846   6.0   2.0  15.0  19.0  17.0\n",
      "Neural Network       0.626923   6.0   2.0  17.0  16.0  23.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.533\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "IADQ_df\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45137880a3474a7e880ef9588102afea",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 2ms/step\n",
      "Train size: 315\n",
      "Selected 2 features out of 9\n",
      "{'Features': [1, 6], 'Wilks test p-value': 1.0, 'High correlation': False}\n",
      "                     Accuracy  FI 1  FI 2\n",
      "Full Logit           0.851852   NaN   NaN\n",
      "Logistic Regression  0.859259   6.0   1.0\n",
      "SVC                  0.859259   6.0   1.0\n",
      "Random Forest        0.851852   1.0   6.0\n",
      "Neural Network       0.859259   1.0   6.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.667\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "IESR_df\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "521ea1f663f04e5d80808ca3fba25ae5",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step\n",
      "Train size: 250\n",
      "Selected 2 features out of 22\n",
      "{'Features': [8, 18], 'Wilks test p-value': 1.0, 'High correlation': True}\n",
      "                     Accuracy  FI 1  FI 2\n",
      "Full Logit           0.935185   NaN   NaN\n",
      "Logistic Regression  0.907407   8.0  18.0\n",
      "SVC                  0.916667  18.0   8.0\n",
      "Random Forest        0.925926   8.0  18.0\n",
      "Neural Network       0.916667   8.0  18.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.667\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "NAQ_R_df\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cfac468ad1bd4373b7672d876be322b8",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 1ms/step\n",
      "Train size: 498\n",
      "Selected 1 features out of 22\n",
      "{'Features': [9], 'Wilks test p-value': 1.0, 'High correlation': True}\n",
      "                     Accuracy  FI 1\n",
      "Full Logit           0.953271   NaN\n",
      "Logistic Regression  0.929907   9.0\n",
      "SVC                  0.929907   9.0\n",
      "Random Forest        0.929907   9.0\n",
      "Neural Network       0.929907   9.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 1.0\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PCL5_df\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "47c5dc7bf6a147779ff3043ae35307ea",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step\n",
      "Train size: 281\n",
      "Selected 7 features out of 20\n",
      "{'Features': [0, 1, 3, 4, 5, 6, 19], 'Wilks test p-value': 1.0, 'High correlation': False}\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.809917   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.793388   4.0   1.0  19.0   3.0   0.0\n",
      "SVC                  0.834711   4.0   1.0  19.0   3.0   6.0\n",
      "Random Forest        0.760331  19.0   4.0   1.0   3.0   6.0\n",
      "Neural Network       0.834711   4.0   3.0  19.0   6.0   5.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.533\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PHQ9_GAD7_df\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "adc3f6ac700140e0838a65c29d13de1d",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 1ms/step\n",
      "Train size: 782\n",
      "Selected 7 features out of 16\n",
      "{'Features': [0, 1, 2, 4, 5, 6, 7], 'Wilks test p-value': 1.0, 'High correlation': True}\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.991071   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.982143   7.0   1.0   6.0   0.0   5.0\n",
      "SVC                  0.982143   7.0   1.0   6.0   0.0   5.0\n",
      "Random Forest        0.973214   7.0   1.0   6.0   0.0   5.0\n",
      "Neural Network       0.988095   7.0   5.0   1.0   6.0   0.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.733\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PID5_df\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f2511d3f0d04aeabd8210712bc3515c",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 0s 2ms/step\n",
      "Train size: 576\n",
      "Selected 7 features out of 220\n",
      "{'Features': [1, 24, 35, 105, 132, 165, 191], 'Wilks test p-value': 1.0, 'High correlation': False}\n",
      "                     Accuracy   FI 1   FI 2   FI 3   FI 4  FI 5\n",
      "Full Logit           0.959677    NaN    NaN    NaN    NaN   NaN\n",
      "Logistic Regression  0.959677    1.0  191.0  165.0  132.0  35.0\n",
      "SVC                  0.955645    1.0  191.0  165.0  132.0  35.0\n",
      "Random Forest        0.959677    1.0  165.0  132.0  191.0  35.0\n",
      "Neural Network       0.959677  165.0    1.0  191.0  132.0  24.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.533\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PRFQ_df\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af32947186e847b29ff0b1162ca01632",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step\n",
      "Train size: 474\n",
      "Selected 8 features out of 18\n",
      "{'Features': [2, 6, 7, 8, 9, 10, 11, 13], 'Wilks test p-value': 1.0, 'High correlation': False}\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.936275   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.901961  11.0   6.0   7.0   9.0   2.0\n",
      "SVC                  0.906863  11.0   6.0   7.0   2.0  13.0\n",
      "Random Forest        0.911765   6.0   9.0   2.0  13.0   7.0\n",
      "Neural Network       0.906863   6.0  11.0   8.0   9.0   7.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.4\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PRMQ_df\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a25868602de4498a7b8c7cafa825c51",
       "version_major": 2,
       "version_minor": 0
      },
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 1ms/step\n",
      "Train size: 982\n",
      "Selected 3 features out of 16\n",
      "{'Features': [0, 1, 7], 'Wilks test p-value': 1.0, 'High correlation': False}\n",
      "                     Accuracy  FI 1  FI 2  FI 3\n",
      "Full Logit           0.907583   NaN   NaN   NaN\n",
      "Logistic Regression  0.876777   1.0   7.0   0.0\n",
      "SVC                  0.867299   1.0   7.0   0.0\n",
      "Random Forest        0.890995   7.0   1.0   0.0\n",
      "Neural Network       0.876777   1.0   7.0   0.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.778\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "RAW_DDDT\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b8a7b4d6c647417583c20ad762958f55",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 1ms/step\n",
      "Train size: 690\n",
      "Selected 4 features out of 12\n",
      "{'Features': [0, 1, 7, 10], 'Wilks test p-value': 1.0, 'High correlation': False}\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4\n",
      "Full Logit           0.746622   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.729730   1.0  10.0   7.0   0.0\n",
      "SVC                  0.729730   1.0  10.0   7.0   0.0\n",
      "Random Forest        0.733108   1.0  10.0   0.0   7.0\n",
      "Neural Network       0.716216   1.0  10.0   0.0   7.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.833\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "R_NEO_PI\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ab7d8dd61bd4068914659504c2aa2c3",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "729/729 [==============================] - 1s 1ms/step\n",
      "Train size: 54380\n",
      "Selected 9 features out of 30\n",
      "{'Features': [1, 5, 10, 13, 15, 21, 22, 27, 29], 'Wilks test p-value': 1.0, 'High correlation': False}\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.906938   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.904921  13.0   5.0  21.0  29.0  22.0\n",
      "SVC                  0.902604  21.0  13.0   5.0  29.0  22.0\n",
      "Random Forest        0.910413  21.0  29.0  13.0   5.0  10.0\n",
      "Neural Network       0.908911   5.0  13.0  29.0  10.0  21.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.267\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "sPID-5_df\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.2, subsample=1.0 will be ignored. Current value: bagging_fraction=0.2\n",
      "[LightGBM] [Warning] bagging_freq is set=10, subsample_freq=0 will be ignored. Current value: bagging_freq=10\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2bbcb2ecc8ab456fa2ff29bd25a6cfe7",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 2ms/step\n",
      "Train size: 726\n",
      "Selected 13 features out of 25\n",
      "{'Features': [2, 3, 4, 6, 9, 11, 13, 17, 19, 20, 22, 23, 24], 'Wilks test p-value': 1.0, 'High correlation': False}\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.961538   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.961538  23.0  20.0   4.0  24.0  11.0\n",
      "SVC                  0.958333  23.0  20.0   4.0  11.0  24.0\n",
      "Random Forest        0.974359  23.0  20.0   4.0  19.0  24.0\n",
      "Neural Network       0.958333  23.0  24.0  20.0   4.0   2.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.533\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data_collection = {}\n",
    "for dataset in [f for f in os.listdir(data_dir) if \"parquet\" in f]:\n",
    "    print(dataset.split(\".\")[0])\n",
    "    X_tr, X_ts, y_tr, y_ts = Classification().prepare_data(f\"{data_dir}\\\\{dataset}\", \"CONDITION\")\n",
    "    var_sel = Classification().variable_selection(X_tr, X_ts, y_tr, y_ts)\n",
    "    \n",
    "    perfs, stability = Classification().benchmark_models(X_tr, X_ts, y_tr, y_ts, var_sel)\n",
    "    data_collection[dataset.split(\".\")[0]] = [X_tr, X_ts, y_tr, y_ts, var_sel, perfs, stability]\n",
    "    print(var_sel)\n",
    "    print(perfs)\n",
    "    print()\n",
    "    print(\"==> TOP5 STABILITY SCORE: {}\".format(stability))\n",
    "    print(\"-\" * 100)\n",
    "    print(\"-\" * 100)\n",
    "    print(\"-\" * 100)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f100143e",
   "metadata": {
    "id": "f100143e"
   },
   "source": [
    "#### Summarize results\n",
    "From the following plots and the table of summary for all the datasets, we see that on average, `25%` of the features are kept, with an accuracy drop of `-0.02` and a standard deviation of accuracy between benchmarking models of `0.008`. Moreover, when dividing the datasets between *faking good* and *faking bad*, one can notice that the datasets of the former type are characterized by a milder strength of selection, in fact, on average faking good datasets show 30% of the features kept, while for faking bad it is around 20%.\n",
    "<br>\n",
    "\n",
    "There is however no correlation between the percentage of variables selected and the performance of both `Accuracy Std on selected features` and `Acc. diff. wrt Full logit`. This is expected as it is mainly related to the single instance of the problem: that is, given that all the datasets have linearly separable categories, but as each feature corresponds to an item of a questionnaire, there may be items that are completely (or almost completely) uninformative with respect to the target value. Finally, we can see a fair amount of correlation ($r=-0.53$ with p-value $\\approx 0.03$) between `Acc. diff. wrt Full logit` and `Feat. Top5-Stability`, which indicates that the more models seem to agree in the order of the most important features, the more they all experience a bigger drop in accuracy with respect to a full model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9dbf5ae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T14:52:29.574956Z",
     "start_time": "2023-01-05T14:52:28.930375Z"
    },
    "id": "a9dbf5ae",
    "outputId": "d1032dfb-86d6-4f1b-9527-f67620483695"
   },
   "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>Dataset name</th>\n",
       "      <th>Sample size</th>\n",
       "      <th>Training size</th>\n",
       "      <th>Number of Features</th>\n",
       "      <th>Selected Features</th>\n",
       "      <th>Feat. Top5-Stability</th>\n",
       "      <th>Accuracy - Logit all features</th>\n",
       "      <th>Accuracy - Logit</th>\n",
       "      <th>Accuracy - SVM</th>\n",
       "      <th>Accuracy - RF</th>\n",
       "      <th>Accuracy - MLP</th>\n",
       "      <th>Avg Acc. on selected features</th>\n",
       "      <th>Accuracy Std on selected features</th>\n",
       "      <th>Acc. diff. wrt Full logit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BF_df_CTU</td>\n",
       "      <td>442</td>\n",
       "      <td>309</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BF_df_OU</td>\n",
       "      <td>460</td>\n",
       "      <td>322</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.797</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.788</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BF_df_V</td>\n",
       "      <td>486</td>\n",
       "      <td>340</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT_df_CC</td>\n",
       "      <td>482</td>\n",
       "      <td>337</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DT_df_JI</td>\n",
       "      <td>864</td>\n",
       "      <td>604</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IADQ_df</td>\n",
       "      <td>450</td>\n",
       "      <td>315</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.859</td>\n",
       "      <td>0.859</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.859</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IESR_df</td>\n",
       "      <td>358</td>\n",
       "      <td>250</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.008</td>\n",
       "      <td>-0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NAQ_R_df</td>\n",
       "      <td>712</td>\n",
       "      <td>498</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PCL5_df</td>\n",
       "      <td>402</td>\n",
       "      <td>281</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.036</td>\n",
       "      <td>-0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PHQ9_GAD7_df</td>\n",
       "      <td>1118</td>\n",
       "      <td>782</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PID5_df</td>\n",
       "      <td>824</td>\n",
       "      <td>576</td>\n",
       "      <td>220</td>\n",
       "      <td>7</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PRFQ_df</td>\n",
       "      <td>678</td>\n",
       "      <td>474</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PRMQ_df</td>\n",
       "      <td>1404</td>\n",
       "      <td>982</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.010</td>\n",
       "      <td>-0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RAW_DDDT</td>\n",
       "      <td>986</td>\n",
       "      <td>690</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.716</td>\n",
       "      <td>0.727</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>R_NEO_PI</td>\n",
       "      <td>77687</td>\n",
       "      <td>54380</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sPID-5_df</td>\n",
       "      <td>1038</td>\n",
       "      <td>726</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Dataset name  Sample size  Training size  Number of Features  \\\n",
       "0      BF_df_CTU          442            309                  10   \n",
       "1       BF_df_OU          460            322                  10   \n",
       "2        BF_df_V          486            340                  10   \n",
       "3       DT_df_CC          482            337                  27   \n",
       "4       DT_df_JI          864            604                  27   \n",
       "5        IADQ_df          450            315                   9   \n",
       "6        IESR_df          358            250                  22   \n",
       "7       NAQ_R_df          712            498                  22   \n",
       "8        PCL5_df          402            281                  20   \n",
       "9   PHQ9_GAD7_df         1118            782                  16   \n",
       "10       PID5_df          824            576                 220   \n",
       "11       PRFQ_df          678            474                  18   \n",
       "12       PRMQ_df         1404            982                  16   \n",
       "13      RAW_DDDT          986            690                  12   \n",
       "14      R_NEO_PI        77687          54380                  30   \n",
       "15     sPID-5_df         1038            726                  25   \n",
       "\n",
       "    Selected Features  Feat. Top5-Stability  Accuracy - Logit all features  \\\n",
       "0                   1                 1.000                          0.774   \n",
       "1                   2                 1.000                          0.833   \n",
       "2                   1                 1.000                          0.760   \n",
       "3                   6                 0.667                          0.683   \n",
       "4                  12                 0.533                          0.662   \n",
       "5                   2                 0.667                          0.852   \n",
       "6                   2                 0.667                          0.935   \n",
       "7                   1                 1.000                          0.953   \n",
       "8                   7                 0.533                          0.810   \n",
       "9                   7                 0.733                          0.991   \n",
       "10                  7                 0.533                          0.960   \n",
       "11                  8                 0.400                          0.936   \n",
       "12                  3                 0.778                          0.908   \n",
       "13                  4                 0.833                          0.747   \n",
       "14                  9                 0.267                          0.907   \n",
       "15                 13                 0.533                          0.962   \n",
       "\n",
       "    Accuracy - Logit  Accuracy - SVM  Accuracy - RF  Accuracy - MLP  \\\n",
       "0              0.737           0.737          0.737           0.737   \n",
       "1              0.790           0.790          0.797           0.775   \n",
       "2              0.685           0.685          0.685           0.685   \n",
       "3              0.703           0.703          0.724           0.703   \n",
       "4              0.642           0.638          0.604           0.627   \n",
       "5              0.859           0.859          0.852           0.859   \n",
       "6              0.907           0.917          0.926           0.917   \n",
       "7              0.930           0.930          0.930           0.930   \n",
       "8              0.793           0.835          0.760           0.835   \n",
       "9              0.982           0.982          0.973           0.988   \n",
       "10             0.960           0.956          0.960           0.960   \n",
       "11             0.902           0.907          0.912           0.907   \n",
       "12             0.877           0.867          0.891           0.877   \n",
       "13             0.730           0.730          0.733           0.716   \n",
       "14             0.905           0.903          0.910           0.909   \n",
       "15             0.962           0.958          0.974           0.958   \n",
       "\n",
       "    Avg Acc. on selected features  Accuracy Std on selected features  \\\n",
       "0                           0.737                              0.000   \n",
       "1                           0.788                              0.009   \n",
       "2                           0.685                              0.000   \n",
       "3                           0.709                              0.010   \n",
       "4                           0.628                              0.017   \n",
       "5                           0.857                              0.004   \n",
       "6                           0.917                              0.008   \n",
       "7                           0.930                              0.000   \n",
       "8                           0.806                              0.036   \n",
       "9                           0.981                              0.006   \n",
       "10                          0.959                              0.002   \n",
       "11                          0.907                              0.004   \n",
       "12                          0.878                              0.010   \n",
       "13                          0.727                              0.007   \n",
       "14                          0.907                              0.004   \n",
       "15                          0.963                              0.008   \n",
       "\n",
       "    Acc. diff. wrt Full logit  \n",
       "0                      -0.038  \n",
       "1                      -0.045  \n",
       "2                      -0.075  \n",
       "3                       0.026  \n",
       "4                      -0.034  \n",
       "5                       0.006  \n",
       "6                      -0.019  \n",
       "7                      -0.023  \n",
       "8                      -0.004  \n",
       "9                      -0.010  \n",
       "10                     -0.001  \n",
       "11                     -0.029  \n",
       "12                     -0.030  \n",
       "13                     -0.019  \n",
       "14                     -0.000  \n",
       "15                      0.002  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pimp_summary = evaluation.summary_table(data_collection, stability = True, neural_net = True)\n",
    "pimp_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dcdff65",
   "metadata": {},
   "source": [
    "From the plot below it is evident that for most of the datasets at hand, the level of (average) accuracy obtained with different models if fairly high, and it is always higher than 50%, which corresponds to a random classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a87c35a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T14:52:30.890705Z",
     "start_time": "2023-01-05T14:52:29.577796Z"
    },
    "id": "1a87c35a",
    "outputId": "cee0adac-ef19-4a1b-b0be-c938a33cf97d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_graphs.accuracy_barplot(pimp_summary, save = True, prefix = \"PERMIMP\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee302daf",
   "metadata": {},
   "source": [
    "We can see how for most of the datasets the difference in performance lies below the ±2% interval, except for PCL5 (Identify victims of PTSD), which looks almost like an outlier. The fact that for three datasets (i.e. BF_CTU, BF_V and NAQ_R) the standard deviation occurs since the set of selected variables contains only one variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe0e0a56",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T14:52:32.154548Z",
     "start_time": "2023-01-05T14:52:30.895819Z"
    },
    "id": "fe0e0a56",
    "outputId": "5fa584f1-fe98-462b-db4b-0781c968b09a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_graphs.accuracy_std(pimp_summary, save = True, prefix = \"PERMIMP\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26dc8d6f",
   "metadata": {},
   "source": [
    "Interestingly, if *faking good* datasets show more preservation of features, they also show a bigger drop in accuracy with respect to the full model, but a smaller standard deviation between benchmarking models.  \n",
    "By highlighting these differences, and comparing them with the fact that on average *faking bad* datasets show a much higher accuracy, it seems as if the questionnaires associated with these datasets include some items that are more informative than those of *faking good*, thus making lie detection way easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c6118b9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T14:52:33.156294Z",
     "start_time": "2023-01-05T14:52:32.156876Z"
    },
    "id": "5c6118b9",
    "outputId": "2fd1383f-bdff-4e4d-b7c9-d91506799c59"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f7r33Xm699Vbee+892rdvT506dRg4cCCDBw/mvvvuY8iQISxfvpx69eoxYcIEDjvsMFq1akW7du1o27YtBQXV/2jZuHFjBg4cSLt27WjZsmX5ZREA99xzD+eddx7Dhg2jTp06PPDAA+y9997suuuu7L///px88snrtY8jR45k3Lhx5dsvvfRSeWACStcjOPDAAwE48cQT6dChA3vttRedOnWiUaOqv9gOGjSI3r1707x5cyZNmpRp33J19NFH8/bbb3PIIYcApYtT/uUvf6FXr17cfvvttG/fnv32248f/OAHq42rffv2FBQUMGrUqPLFLFu1arXG23Zuv/32jB07losuuojFixezatUqLrnkEvbdd1/+7d/+jcWLF5NS4qc//ekGBRAAYkOneqyvTp06pWnTau9ukJvicoYeu7+XU/k2japOPVmXXerlthiJMxEkSZKkmvf222+z//771/YwNmvLli2jXbt2vPrqq9V+qa9JhYWFNGjQgGXLltG1a1dGjBixxoCIqj9/I2J6SqlT5bIurChJkiRJ2qgmTJhA69atGTJkyEYPIEDpr/kdO3akoKCAvn37GkCoQV7OIEmSJEnaqHr27MnHH3+8yfq79957N1lf2xqDCNpm5HoJC2z6a9kkSZIkaXPm5QySJEmSJCkTgwiSJEmSJCkTgwiSJEmSJCkT10SQJEmSpBp2xTlf1Wh719zdOFO5hx56iFNOOYW3336b1q1b1+gYatPIkSOZNm0at9122wa1M2zYMLp27UrPnj256aabGDRoEPXr16+hUW4bnIkgSZIkSVuJ0aNHc/jhhzNmzJiN2k9xcfFGbX9jGT58OD179gTgpptuYtmyZbU8oi2PMxGkWvbZtTfnVH7XoRdvpJFIkiRpS1ZYWMiLL77IpEmTOPHEE7nqqquA0i/8l112GU8++SQRwcCBAxkyZAhTp07l4osvZunSpeywww4888wz/PWvf13tF//jjz+eSy+9lO7du9OgQQN+9rOf8eSTT/Lf//3fTJw4kUcffZTly5dz6KGH8r//+79EBLNnz+b8889nwYIF5OXl8cADD3DVVVdx6qmnctJJJwHQv39/Tj/9dE488cQN2ucbb7yRO++8E4Bzzz2XSy65BICrr76aUaNGseeee9K0aVMOOuggLr30UgYMGMDxxx/P/PnzmT9/PkceeSRNmzZl0qRJGzSObYlBBKkGTbz8lZzrtGmUW/l0c0HOfcTFr+ZcZ2uR6609N8VtPXMNHAHsUu/unMpvy8dckqRt1bhx4+jVqxf77rsvTZo04dVXX6WgoIARI0YwZ84cZsyYQX5+PosWLeKbb77h9NNP57777qNz5858/fXX1KtXb63tL126lLZt2zJ8+HAADjjgAIYNGwbAWWedxWOPPcYJJ5xA//79GTp0KH369KGoqIiSkhLOPfdc/vCHP3DSSSexePFiJk+ezN135/b3TWXTp0/nrrvu4uWXXyalxMEHH0y3bt0oLi7mr3/9KzNmzGDVqlUUFBRw0EEHrVb3oosu4sYbb2TSpEk0bdp0g8axrTGIIK1Frtey9dh944xDm876XL/YY/f3ciqfa+BIkiQpi9GjR5f/Et+vXz9Gjx5NQUEBEyZM4Pzzzyc/v/TrX5MmTXjjjTdo3rw5nTt3BmCnnXZaZ/t5eXn07du3fHvSpElcd911LFu2jEWLFtGmTRu6d+/OJ598Qp8+fQCoW7cuAN26dePCCy/k888/58EHH6Rv377l41lfL7zwAn369GHHHXcE4JRTTuH555+npKSEk046qTwocsIJJ2xQP1qdQQRJkiRJ2sItXLiQiRMn8uabbxIRFBcXExFcd911pJSIWH22ZXVpAPn5+ZSUlJRvFxUVlT+vW7cueXl55ekXXHAB06ZNY8899+Sqq66iqKiIlNY8C/Sss85i1KhRjBkzpvwShIquuOIKxo8fD8DMmTPXuc9r6mttY9CGc2FFSZIkSdrCjR07lrPPPpt//OMffPTRR8ydO5dWrVrxwgsvcPTRR3P77bezatUqABYtWkTr1q2ZP38+U6dOBWDJkiWsWrWKli1bMnPmTEpKSpg7dy6vvFL95brfBheaNm1KYWEhY8eOBUpnNLRo0YJx48YBsGLFivLFCwcMGMBNN90EQJs2baq0ec011zBz5sxMAQSArl27Mm7cOJYtW8bSpUt56KGHOOKIIzj88MN59NFHKSoqorCwsDwwUVnDhg1ZsmRJpr70L85EkCRJkqQalvWWjDVl9OjRDB06dLW0vn37cu+993Lrrbfy3nvv0b59e+rUqcPAgQMZPHgw9913H0OGDGH58uXUq1ePCRMmcNhhh9GqVSvatWtH27ZtKSiofj2uxo0bM3DgQNq1a0fLli3LL4sAuOeeezjvvPMYNmwYderU4YEHHmDvvfdm1113Zf/99+fkk09er30cOXJkeXAC4KWXXmLAgAF06dIFKF1Y8cADDwTgxBNPpEOHDuy111506tSJRo2qXk86aNAgevfuTfPmzV1YMQdRW1M9OnXqlKZNm1YrfUPui60d12Jxzn3kfp30lJz7cLG17HI95pD7cc/1mEPuxz3XYw4e91z4XpckSevj7bffZv/996/tYWzWli1bRrt27Xj11Ver/VJfkwoLC2nQoAHLli2ja9eujBgxYo0BEVV//kbE9JRSp8plvZxBkiRJkrRRTZgwgdatWzNkyJCNHkCA0lkGHTt2pKCggL59+xpAqEFeziBJkiRJ2qh69uzJxx9/vMn6u/feezdZX9saZyJIkiRJkqRMDCJIkiRJkqRMDCJIkiRJkqRMDCJIkiRJkqRMXFhRkiRJkmrYxMtfqdH2fvi7LpnKPfTQQ5xyyim8/fbbtG7dukbHUJs+++wzfvKTnzB37lxWrlxJy5Ytefzxx2nVqhVPPPEE++23X3nZSy65hN13350uXbpw5JFHcscdd/CTn/wEgBkzZlBQUMD111/PpZdeWlu7s0VzJoIkSZIkbSVGjx7N4YcfzpgxYzZqP8XFxRu1/cqGDRvGUUcdxWuvvcZbb73FtddeC0C/fv1W29eSkhLGjh3L6aefDkC7du247777yvPHjBlDhw4dNunYtzYGESRJkiRpK1BYWMiLL77In//859W+WBcXF3PppZfSrl072rdvz6233grA1KlTOfTQQ+nQoQNdunRhyZIljBw5ksGDB5fXPf7443n22WcBaNCgAcOGDePggw9mypQpDB8+nM6dO9O2bVsGDRpESgmA2bNn07NnTzp06EBBQQEffPABZ511Fg8//HB5u/379+eRRx7JvG///Oc/adGiRfl2+/btATjjjDNW29fnnnuOli1bstdeewHw3e9+l6KiIj777DNSSjzxxBP07t07c7+qyiCCJEmSJG0Fxo0bR69evdh3331p0qQJr776KgAjRoxgzpw5zJgxg9dff53+/fvzzTffcPrpp3PzzTfz2muvMWHCBOrVq7fW9pcuXUrbtm15+eWXOfzwwxk8eDBTp07lzTffZPny5Tz22GNAaYDgwgsv5LXXXmPy5Mk0b96cc889l7vuuguAxYsXM3nyZI499tjM+3bhhRfyk5/8hCOPPJJrrrmG+fPnA6XBhO22247XXnsNKJ1pcMYZZ6xW99RTT+WBBx5g8uTJFBQUsMMOO2TuV1UZRJAkSZKkrcDo0aPp168fUDrNf/To0QBMmDCB888/n/z80iXxmjRpwrvvvkvz5s3p3LkzADvttFN5/prk5eXRt2/f8u1JkyZx8MEH065dOyZOnMisWbNYsmQJn3zyCX369AGgbt261K9fn27dujF79mw+//xzRo8eTd++fdfZX0XHHHMMH374IQMHDuSdd97hwAMPZMGCBcC/ZiOsWrWKhx9+mB/96Eer1T3ttNN44IEHGD16dJUAg3JnEEGSJEmStnALFy5k4sSJnHvuubRs2ZLrr7+e++67j5QSKSUiYrXy1aUB5OfnU1JSUr5dVFRU/rxu3brk5eWVp19wwQWMHTuWN954g4EDB1JUVFR+SUN1zjrrLEaNGsVdd93Fj3/84yr5V1xxBR07dqRjx47V1m/SpAlnnnkm99xzD507d+a5554DSoMI999/PxMmTKB9+/bssssuq9XbbbfdqFOnDk8//TQ9evRY4/iUjXdnkCRtVTr2WPMfL2sy85mqf0RJkrQlGTt2LGeffTb/+7//W57WrVs3XnjhBY4++mhuv/12unfvTn5+PosWLaJ169bMnz+fqVOn0rlzZ5YsWUK9evVo2bIlf/zjHykpKeGTTz7hlVeqv8vEt8GFpk2bUlhYyNixYzn11FPZaaedaNGiBePGjePkk09mxYoVFBcXU79+fQYMGECXLl3YbbfdaNOmTZU2r7nmGq655ppq+5s4cSI/+MEPqF+/PkuWLOGDDz7gu9/9LgDf+9732HnnnRk6dCiXXHJJtfWHDx/O559/Xh4E0foziCBJkiRJNSzrLRlryujRoxk6dOhqaX379uXee+/l1ltv5b333qN9+/bUqVOHgQMHMnjwYO677z6GDBnC8uXLqVevHhMmTOCwww6jVatWtGvXjrZt21JQUFBtf40bN2bgwIG0a9eOli1bll8WAXDPPfdw3nnnMWzYMOrUqcMDDzzA3nvvza677sr+++/PySefnPP+TZ8+ncGDB5fPlDj33HNX6/OMM87g8ssvL7+MorJDDz005z5VvVjbdJONqVOnTmnatGm10jfk/kvVcS0W59xHj93fy6l8m0ZTcu5jl3p351Q+Ln415z62Fuvz62Suxz3XYw65H/dcjzl43HPhe33L50wESVJtePvtt9l///1rexibtWXLltGuXTteffVVGjVqVNvDUQXVnb8RMT2l1KlyWddEkCRJkiRtVBMmTKB169YMGTLEAMIWzssZJEmSJEkbVc+ePfn4449rexiqAZlmIkREr4h4NyJmR8TQavIbRcSjEfFaRMyKiKpLbUqSJEmSpC3aOoMIEZEH/A/QGzgAOCMiDqhU7ELgrZRSB6A78N8RsX0Nj1WSJEmSJNWiLDMRugCzU0ofppS+AcYAJ1Uqk4CGUXqj0QbAImBVjY5UkiRJkiTVqixBhD2AuRW255WlVXQbsD8wH3gDuDilVFK5oYgYFBHTImLaggUL1nPIkiRJkiSpNmRZWLG6+15Vvn/WMcBM4IfA94CnI+L5lNLXq1VKaQQwAqBVq1Zp5MiRqzXSpk0bOnfuzMqVKxk1alSVTjt27EjHjh1ZtmwZ999/f5X8Tp060bZtWxYvXsxDDz1UJf+QQw5hv/3244svvuDw9o9VyX/34yNY8NXeNNrxU9p978nV8vJ3WEXxokNJK3YndphPXpPJVeoXL+xG+qYZUe9j8hq/wuvbL18t//sr21A/NWDhdp/xSf5HVervlUpoENsxu/gbZhWvqJJ/dJ0dqRfb8U7xCt4t/gaA7VcdVp5/ZoOXqBPFTF3Rkre+qRzngXMavgjA5MmTee+91W9JV6dOHfr37w/A3//+d+bMmbNafv369TnttNOA0pVV582bt1r+TjvtxCmnnALAE088waeffrpa/s4778wJJ5wAwKOPPsrChQtXy99tt93o1asXAA8++CBff73aqUOLFi3o2bMnAPfffz/Lli1bLb9Vq1Z069YNgFGjRrFy5crV8vfdd1/gEAAOb1/1VnmfLDiAOf/sTN52Kzmk7b3l6fk7lE6oKVlyACWFB8B2y8nfdXyV+iVft6dk6b6sYDnvbv96lfw9VrVk55JdWRaFzK4za7W82d8s4aD8urTYrg5flKzixVXLq9Q/OL8eu22Xz6clq/jbksOq5B9T7w12y/+aD1c24/mifavkH//FFzRt2pR3332XKVOq3l6wT58+NGrUiDfffJPqbr162mmnUb9+fWbOnMnMmTOr5Pfv3586deowdepUZs2aVSV/wIABQO2cex332ZmZ7x9f9vwxGtRb/dxbXLgbb3x4DAAH7fcQ+TstWi0/FTWn+MvS1zx/18dgu6LV8kuW7wl8B4A360ylJFaPnzYpbkaL4r0BeH37l4HSY/6t7+VtT9u8HViZEo+vLKSy/fK2p3XeDiwr2Z4Hlnaukt9phzm02X4+i0vqMm7pQf/KKPt8rfi599hjVT/3unbtyt57782nn37KE088USW/R48e7LnnnsydO5dnnnmmSn6vXr3Ybbfd+PDDD3nuueeq5B9//PGb/Nw7vP2/8l94/RwAvt9iMrs1eX+1usUl+Ux5c9N97v1zxmur5TfdLo/D8usD8MzKpRRWir3vfegPNvhz79t7Ylf+/xY27f+528q5V1Ftfu5tDv/neu557m2L515BQQEAK1euZPHixRTfUbWPDdHk5xdQp04dVqxYwZIlS6rkN2rUiDp16nDffffRr18/Jk+ezD777FOe37hxY/Lz81m+fDlLly6tUv873/kOeXl5LFu2rMprD9CkSRO22247li5dyvLlVf9ebdq0KQCFhYUUFa3+91JEsPPOOwOwZMkSVqxY/bvOdtttR5MmTQD4+uuv+eabb1bLz8vL4+GHH+YXv/gFu+22G0VFRZxzzjmcf/75ANxwww3cc889NGvWjOLiYo488kiGDRtWXr9OnTrld4T48ssvKS4uXq397bffnp122gmARYsWUVKy+v/JO+ywAw0bNgRg4cKFpLT61+G6devSoEEDAL744osqr029evXYcccdKSkpYdGiRVXy69evT/369SkuLubLL7+skr/jjjtSr149Vq1axVdffVUlv0GDBtStW7f83KusYcOG7LDDDmvM/3bf1/S5V1mWmQjzgD0rbLegdMZBRT8GHkylZgNzgNYZ2pYkSZIk1ZD777+fgw8+uNpAR02q/EV8Uzj99NN54YUXGD9+PH/4wx/45JNPyvN++tOfMnPmTJ5//vnVAgiqeVE5ilKlQEQ+8B7QA/gEmAqcmVKaVaHM/wM+SyldFRG7Aq8CHVJKVcMwZTp16pSqi75uKh17rH2/KzuuRdWIzbr02P29dReqoE2jqtHqddmlXtVf1dcmLn415z62Frkec8j9uOd6zCH3457rMQePey58r2/5tpb3+q5DL865D0lS7Xn77bfZf//9y7c/u/bmGm0/y/8LhYWF7LfffkyaNIkTTzyRd955Byj9wn/ZZZfx5JNPEhEMHDiQIUOGMHXqVC6++GKWLl3KDjvswDPPPMNf//pXpk2bxm233QaUzq659NJL6d69Ow0aNOBnP/sZTz75JP/93//NxIkTefTRR1m+fDmHHnoo//u//0tEMHv2bM4//3wWLFhAXl4eDzzwAFdddRWnnnoqJ51Uurxe//79Of300znxxBMz7f/IkSNXG9cPfvADbrnlFrp06cJVV11FgwYNuPTSS9fnpRVVz1+AiJieUupUuew6ZyKklFYBg4EngbeB+1NKsyLi/Ig4v6zY1cChEfEG8Axw2doCCJIkSZKkmjVu3Dh69erFvvvuS5MmTXj11dIfFUaMGMGcOXOYMWMGr7/+Ov379+ebb77h9NNP5+abb+a1115jwoQJ1KtXb63tL126lLZt2/Lyyy9z+OGHM3jwYKZOncqbb77J8uXLyy8h6t+/PxdeeCGvvfYakydPpnnz5px77rncddddACxevJjJkydz7LHHrtd+fvzxxxQVFdG+/b+uYfzDH/5QfknKk08+uZba2lBZ1kQgpfQ48HiltNsrPJ8PHF2zQ5MkSZIkZTV69GguueQSAPr168fo0aMpKChgwoQJnH/++eTnl379a9KkCW+88QbNmzenc+fSNZe+vS5+bfLy8ujbt2/59qRJk7juuutYtmwZixYtok2bNnTv3p1PPvmEPn36AKXrBQB069aNCy+8kM8//5wHH3yQvn37lo8nq/vuu49Jkybx7rvv8qc//am8bSi9nMGZCJtGbkdNkiRtEunmgpzreBlLdjOfqW7daEnaci1cuJCJEyfy5ptvEhEUFxcTEVx33XWklIhY/XOvujSA/Pz81RYWrLhIYt26dcnLyytPv+CCC5g2bRp77rknV111FUVFRVUWHazorLPOYtSoUYwZM4Y777yzSv4VV1zB+PGli5hXt7Do6aefzm233caUKVM47rjj6N27N7vtttvaXxjVuCwLK0qSJEmSNmNjx47l7LPP5h//+AcfffQRc+fOpVWrVrzwwgscffTR3H777axaVXrnsUWLFtG6dWvmz5/P1KlTgdK7JqxatYqWLVsyc+ZMSkpKmDt3Lq+88kq1/X0bXGjatCmFhYWMHTsWKJ3R0KJFC8aNGwfAihUryu/2MGDAAG666Sag9G4ZlV1zzTVrvDNJRYcccghnnXUWN99cs+tOKBtnIkiSJElSDdvUC+SOHj2aoUOHrpbWt29f7r33Xm699Vbee+892rdvT506dRg4cCCDBw/mvvvuY8iQISxfvpx69eoxYcIEDjvsMFq1akW7du1o27Zt+a0rK2vcuDEDBw6kXbt2tGzZsvyyCIB77rmH8847j2HDhlGnTh0eeOAB9t57b3bddVf2339/Tj755A3e38suu4yCggL+8z//c4PbUm4MIkiSJEnSFu7ZZ5+tknbRRReVP7/xxhu58cYbV8vv3LkzL730UpV6o0aNqraPwsLC1bZ/85vf8Jvf/KZKuX322YeJEydWSV+2bBnvv/8+Z5xxRrXtr82AAQMYMGBA+fbuu+/Op59+CsBVV12Vc3tafwYRJEmStMVZn9u5uhaGVHsmTJjAv//7v/Ozn/2MRo0a1fZwtAEMIkiSJEmSNqqePXvy8ccf1/YwVANcWFGSJEmSJGViEEGSJEmSJGViEEGSJEmSJGXimgiSJGmbc8U5X+Vcp8fu7+VUvk2jKTn3sUu9u3OuExe/mnMdSZLWl0EESZIkSaph6eaCGm0va8DwoYce4pRTTuHtt9+mdevWNTqG2jRy5Eh+/OMfM2HCBHr06AH8a18feOABTj31VLp3784NN9xAp06dyus9++yznHTSSey9994UFRXRr18/rrzyytraja2ClzNIkiRJ0lZi9OjRHH744YwZM2aj9lNcXLxR269Ou3btGD16dPn2mDFj6NChwzrrHXHEEcyYMYNp06bxl7/8henTp2/MYW71DCJIkiRJ0gb47NqbKV78NSv/+Vn5ozYUFhby4osv8uc//3m1IEJxcTGXXnop7dq1o3379tx6660ATJ06lUMPPZQOHTrQpUsXlixZwsiRIxk8eHB53eOPP55nn30WgAYNGjBs2DAOPvhgpkyZwvDhw+ncuTNt27Zl0KBBpJQAmD17Nj179qRDhw4UFBTwwQcfcNZZZ/Hwww+Xt9u/f38eeeSRnPbviCOO4JVXXmHlypUUFhYye/ZsOnbsmLn+jjvuyEEHHcQHH3yQU79anUEESZIkSdoKjBs3jl69erHvvvvSpEkTXn219BKIESNGMGfOHGbMmMHrr79O//79+eabbzj99NO5+eabee2115gwYQL16tVba/tLly6lbdu2vPzyyxx++OEMHjyYqVOn8uabb7J8+XIee+wxoDRAcOGFF/Laa68xefJkmjdvzrnnnstdd90FwOLFi5k8eTLHHntsTvsXEfTs2ZMnn3yShx9+mBNPPDGn+gsXLuSll16iTZs2OdXT6gwiSJIkSdJWYPTo0fTr1w+Afv36lU/9nzBhAueffz75+aVL4jVp0oR3332X5s2b07lzZwB22mmn8vw1ycvLo2/fvuXbkyZN4uCDD6Zdu3ZMnDiRWbNmsWTJEj755BP69OkDQN26dalfvz7dunVj9uzZfP7554wePZq+ffuus7/q9OvXjzFjxjBmzBjOOOOMTHWef/55DjzwQI4++miGDh1qEGEDubCiJEmSJG3hFi5cyMSJE3nzzTeJCIqLi4kIrrvuOlJKRMRq5atLA8jPz6ekpKR8u6ioqPx53bp1ycvLK0+/4IILmDZtGnvuuSdXXXUVRUVF5Zc0VOess85i1KhRjBkzhjvvvLNK/hVXXMH48eMBmDlzZrVtdOnShTfffJN69eqx7777rvkFqeCII44onyWhDedMBEmSJEnawo0dO5azzz6bf/zjH3z00UfMnTuXVq1a8cILL3D00Udz++23s2rVKgAWLVpE69atmT9/PlOnTgVgyZIlrFq1ipYtWzJz5kxKSkqYO3cur7zySrX9fRtcaNq0KYWFhYwdOxYondHQokULxo0bB8CKFStYtmwZAAMGDOCmm24CqHY2wDXXXMPMmTPXGED41u9+9zt++9vf5vT6qOY4E0GSJEmSatiq0/62xrw6zXet8f5Gjx7N0KFDV0vr27cv9957L7feeivvvfce7du3p06dOgwcOJDBgwdz3333MWTIEJYvX069evWYMGEChx12GK1ataJdu3a0bduWgoLqb1XZuHFjBg4cSLt27WjZsmX5ZREA99xzD+eddx7Dhg2jTp06PPDAA+y9997suuuu7L///px88skbtK+9e/deY95xxx1HnTp1ADjkkEO48MILN6gvVWUQQZIkSdJW6Ypzvsq5To/d38u5TptGuZVPn72Vcx+x6wFrzf/2DgoVXXTRReXPb7zxRm688cbV8jt37sxLL71Upd6oUaOq7aOwsHC17d/85jf85je/qVJun332YeLEiVXSly1bxvvvv595LYOKBgwYwIABA6qkjxw5svx5da8BQPfu3XPuT2vm5QySJEmSpI1qwoQJtG7dmiFDhtCoUY5RF21WnIkgSZIkSdqoevbsyccff1zbw1ANcCaCJEmSJNWAtd2ZQNpc5XreOhNBkiRJ24Rcr49fv2vjp+RcZ5d6d+dUPi5+Nec+thYde+T2Zee4FhtpINXIX1LIoqWFNNmxQbW3TpQ2RyklFi5cSN26dTPXMYggSZIkSRtop9ff5Svgi4YN1lk2L5bm3sEiAxPaOOrWrUuLFtkjbgYRJEmSJGkD5a1cyXemv5mpbK6zT2DbnoGizYtrIkiSJEmSpEwMIkiSJEmSpEy8nEGSJEmStFnKdTFNgJnPuH7ExuRMBEmSJEmSlIlBBEmSJEmSlIlBBEmSJEmSlIlBBEmSJEmSlIkLK0qSJEmSthpXnPNVznWuubtxjY9ja2UQQZIkSZK0TZt4+Ss5lf/h77pspJFs/rycQZIkSZIkZWIQQZIkSZIkZeLlDJIkSZIkbWTp5oKcysfFr26kkWwYZyJIkiRJkqRMDCJIkiRJkqRMMgURIqJXRLwbEbMjYugaynSPiJkRMSsi/l6zw5QkSZIkSbVtnWsiREQe8D/AUcA8YGpEPJJSeqtCmcbAH4FeKaWPI2KXjTReSZIkSZJUS7IsrNgFmJ1S+hAgIsYAJwFvVShzJvBgSuljgJTS5zU9UEmSJEmSNgefXXtzznV2qbcRBlILslzOsAcwt8L2vLK0ivYFvhMRz0bE9Ig4u7qGImJQREyLiGkLFixYvxFLkiRJkqRakSWIENWkpUrb+cBBwHHAMcCvImLfKpVSGpFS6pRS6tSsWbOcBytJkiRJkmpPlssZ5gF7VthuAcyvpswXKaWlwNKIeA7oALxXI6OUJEmSJEm1LstMhKnAPhHRKiK2B/oBj1Qq8zBwRETkR0R94GDg7ZodqiRJkiRJqk3rnImQUloVEYOBJ4E84M6U0qyIOL8s//aU0tsR8QTwOlAC3JFSenNjDlySJEmSJG1aWS5nIKX0OPB4pbTbK21fD1xfc0OTJEmSJEmbkyyXM0iSJEmSJBlEkCRJkiRJ2RhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmWQKIkREr4h4NyJmR8TQtZTrHBHFEXFqzQ1RkiRJkiRtDtYZRIiIPOB/gN7AAcAZEXHAGsr9HniypgcpSZIkSZJqX5aZCF2A2SmlD1NK3wBjgJOqKTcE+CvweQ2OT5IkSZIkbSayBBH2AOZW2J5XllYuIvYA+gC3r62hiBgUEdMiYtqCBQtyHaskSZIkSapFWYIIUU1aqrR9E3BZSql4bQ2llEaklDqllDo1a9Ys4xAlSZIkSdLmID9DmXnAnhW2WwDzK5XpBIyJCICmwLERsSqlNK4mBilJkiRJkmpfliDCVGCfiGgFfAL0A86sWCCl1Orb5xExEnjMAIIkSZIkSVuXdQYRUkqrImIwpXddyAPuTCnNiojzy/LXug6CJEmSJEnaOmSZiUBK6XHg8Upp1QYPUkoDNnxYkiRJkiRpc5NlYUVJkiRJkiSDCJIkSZIkKRuDCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKRODCJIkSZIkKZNMQYSI6BUR70bE7IgYWk1+/4h4vewxOSI61PxQJUmSJElSbVpnECEi8oD/AXoDBwBnRMQBlYrNAbqllNoDVwMjanqgkiRJkiSpdmWZidAFmJ1S+jCl9A0wBjipYoGU0uSU0pdlmy8BLWp2mJIkSZIkqbZlCSLsAcytsD2vLG1NfgL8rbqMiBgUEdMiYtqCBQuyj1KSJEmSJNW6LEGEqCYtVVsw4khKgwiXVZefUhqRUuqUUurUrFmz7KOUJEmSJEm1Lj9DmXnAnhW2WwDzKxeKiPbAHUDvlNLCmhmeJEmSJEnaXGSZiTAV2CciWkXE9kA/4JGKBSLiu8CDwFkppfdqfpiSJEmSJKm2rXMmQkppVUQMBp4E8oA7U0qzIuL8svzbgWHAzsAfIwJgVUqp08YbtiRJkiRJ2tSyXM5ASulx4PFKabdXeH4ucG7NDk2SJEmSJG1OslzOIEmSJEmSZBBBkiRJkiRlYxBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlYhBBkiRJkiRlkimIEBG9IuLdiJgdEUOryY+IuKUs//WIKKj5oUqSJEmSpNq0ziBCROQB/wP0Bg4AzoiIAyoV6w3sU/YYBPy/Gh6nJEmSJEmqZVlmInQBZqeUPkwpfQOMAU6qVOYk4P9SqZeAxhHRvIbHKkmSJEmSalGklNZeIOJUoFdK6dyy7bOAg1NKgyuUeQy4NqX0Qtn2M8BlKaVpldoaROlMBYD9gHdrake2ME2BL2p7ENrkPO7bHo/5tsnjvu3xmG+bPO7bHo/5tmlbPu57pZSaVU7Mz1AxqkmrHHnIUoaU0ghgRIY+t2oRMS2l1Km2x6FNy+O+7fGYb5s87tsej/m2yeO+7fGYb5s87lVluZxhHrBnhe0WwPz1KCNJkiRJkrZgWYIIU4F9IqJVRGwP9AMeqVTmEeDssrs0/ABYnFL6Zw2PVZIkSZIk1aJ1Xs6QUloVEYOBJ4E84M6U0qyIOL8s/3bgceBYYDawDPjxxhvyVmGbv6RjG+Vx3/Z4zLdNHvdtj8d82+Rx3/Z4zLdNHvdK1rmwoiRJkiRJEmS7nEGSJEmSJMkggiRJkiRJysYggiRJkiRJysQgQg4i4tmIeDciXouIFyNivwrpncqefxQRb5Q93oqI30TEDmtob0BELIiImWWPc3MYy8iIOLXs+RERMausjXo1sa+CiCgue03fjIgHIqJ+NemPRkTjsvSWEbG8wvGcWXZHEyLi5Ih4PSLeKat3ag7jaBkRb1bYHl3W1k9reJe3SWs6nmso2zIiUkQMqZB2W0QMKHs+MiLmVDj+kyuUq3gOvBERJ69jXBXbejUiDqmQnvn8UfUqHPfXyl7fQ8vS1/g+ztDmRxHRtOz5RRHxdkSMWkv5NZ4TFf9fqTCuN6ttSGu1mRzr3hExrazcOxFxQ0RcUaHf4grPL6rufR4RhRvyOqhUbZ4PZX3Mi4jtKqXPjIguNbF/KlXhOM8qO9Y/i4jtIuKYCse3MEr/rp8ZEf+Xsd2Kf/P/qOxYT1pL+S4R8VxZP+9ExB3xr78nq3wu1MzeCzaPcyAiukfEY2XPB0TEbTW3h7UspeQj4wN4FuhU9nwQ8Eg16R8BTcueNwDuBe5eQ3sDgNvWcywjgVPLnt8O/Li2X5+t7QEUVng+CvhZNel3A1eUPW8JvFlNOx0ovXNJq7LtVsAHwEEZx1HeLrAb8I/afm22pseajudajsVnZcdz+7K024ABZc/L35cZzoHZQPu19FXxPX408Pra+vCxQcf9GODvFY5xlfdxxjYrfv6/8+3xXkPZtZ4TFf9f2dBxbeuPzeBYty37zG9dtp0PXLCmMZZtV3mfVy7jY4s9H6YA3SpstwY+qO3XZWt7VDrOuwATgF9XKrPa52zGdsvrAE8AR66l7K7AP4BDyrYDOLUsfZ2fCz62inOgO/BY2fMBrOf3vs3x4UyENYiIHSNifFnk6s2IOL1SkeeA76+tjZRSIXA+cHJENNnA8USU/uL5VkSMp/TNQJTOXjgNGLa2X0G0wZ6n+uM9BdhjHXUvBX6bUpoDUPbvb4Gfr6lCRBxUdu5NAS6skPUUsEtZxPSIXHZAmWQ5nguAZ4Bzcmi3unPgd8AvMtZf5+eNNshOwJe5VoqInSPiqYiYERH/S+kfiETE7cDewCOx5hlDG3pOaP3UxrH+JXBNSukdKL11dkrpj+s3fNWw2jgfRgP9Kmz3K0vTRpJS+pzSH/8GR0TkUjci6kXEmCidNXYfUK8sfRhwOHB7RFy/huoXUvpD4pSycaSU0tiU0mf4ubBJ1eI5sNUyiLBmvYD5KaUOKaW2lEaaKjoBeGNdjaSUvgbmAPusoUjfspNybETsuZam+gD7Ae2AgcChZe3fATwC/CKl1H9d41HuIiIf6E2l4x0ReUAPSl//b32vwhSp/ylLawNMr9TsNOCAtXR7F3BRSumQSuknUvqLRceU0vM57orWYg3Hc02uBX5eVqey6yucA98G9tZ0DrTJOLxMnzfKSb2yY/QOcAdwdYW86t7H1bkSeCGldCCl5813AVJK5wPzKf114g9rqLuh54Syq+1j3Zaqx1q1p7bPh/sp/XEpv2z7dGDMBuyPMkgpfUjp955dcqz6H8CylFJ74BrgoLL2hlP6md0/pbSm4O/a3vt+LmxitXQObLXy111km/UGcENE/J7SaSjPlwWuRkXEckqnrg1ZS/2K1hTxehQYnVJaERHnUzqV+odrKNu1rGwxMD8iJmbsW+uvXkTMLHv+PPDnSuktKf0P4OkKdT5IKXWs1E4AqZq0akVEI6BxSunvZUn3UBrE0MaxtuNZrZTSnIh4BTizmuxfpJTGVkpb0zlQOa2y6yPivyid/fCTdY1LOVn+7Xs1Steb+L+IaFuWV937uDpdgVMAUkrjIyKXXzTXdU5Ud26s63xR9Wr7WK8Pj//GU6vnQ0rp04iYBfSIiM+AlSkl1zvZNHL6BbpMV+AWgJTS6xHxes0OSZuY50ANcSbCGqSU3qM00vQG8LuyKStQGm3qmFI6OaU0d13tRERDSr+cvBcR13wb4S7rY2FKaUVZ0T+V9bfWYa3Hrmj9LS871h1TSkNSSt9UTAf2ArZn9csNqjML6FQprYDS6GV1sny5VM3J9Xh+67fAZWT7HF3TOfDWOur9ouz8O8o/MjeesqmmTYFm61N9Pbtd1zmxEPhOhbwmwBfr2ZfK1OKxXtf/75WtdvzLLon0+NewWjof4F+XNHgpwyYSEXsDxcDn61F9Y7z31+dzQRugls6BrZZBhDWIiN0pnbryF+AGSv+4y7WNBsAfgXEppS9TSld8+6W0LL95heInAm+vpbnngH4RkVdW78hcx6OalVJaDFwEXBoRddZS9Abg8ohoCaWrMwOXANVeP5VS+gpYHBGHlyV5mcomkMPx/Lb8O5R+4Ts+Q/PVnQP/Cfz3+o5XNSciWgN5lH5xy8VzlL0/I6I3q3/pX5d1nRPPAv9W4drNc4A1rgCubGrpWF8P/GdE7FtWf7uI+Nk66jwLnB7/ujvAADz+Na6WzgeAvwLH4qUMm0RENKN0EfLbUkq5fhmseKzbAu1zqHsbcE5EHFxhLP8WEbuxfp8LWk+1eA5stbycYc3aUTqVuARYSen1MFlvvTKp7A+/7YCHWP16u4ouiogTgVXAIkr/SFiThyi91OEN4D3g72spq00kpTQjIl6j9NeEatcoSCnNjIjLgEej9HafLSm9XvLdtTT9Y+DOiFgGPFnDw9YaVDqe92Socg0wo1Lat5cgfKtLpXOgDqWfKb9MKc2siXFrvVS8XCmAc1JKxTmut/RrYHREvErpZ/LHWStmOCdGULpq+2sRkSiduXR5LoNTudo+1q9HxCVl9etT+ovW+HXUeSwiDgKmR0Qxpau4n5/LgLVGtXo+QOmPBRHxErDrt4urqsZ9e5zrUPp39j3AjevRzv8D7iqbwj4TeCVrxZTSZxHRj9LLo3cBSij9Qvpg2WUtl5DD54JyVuvnAKXftVess9QWKHIPxkjaEBFxLXAwcEyFSyQkSZIkbSUi4mJgj5TSL2t7LDXNIIIkSZIkSTUkIv5M6V04Tksp/aO2x1PTvJxhMxMR7ag6jXpFSung6spry1Z2C6nDKiXfnFK6qzbGo1Kb8n3oObD5i4gfAxdXSn4xpbTORTg3pK42PY+1KvJ82HZExDHA7yslz0kp9dmYdbX52EjnwFb7/c2ZCJIkSZIkKRPvziBJkiRJkjIxiCBJkiRJkjIxiCBJ0hYsIoojYmZEzIqI1yLiZxGx1v/fI6JlRJy5EcZySdntyiRJ0lbKIIIkSVu25SmljimlNsBRwLHAleuo0xKo8SACcAlgEEGSpK2YQQRJkrYSKaXPgUHA4CjVMiKej4hXyx6HlhW9FjiibAbDT9dULiKaR8RzZeXejIgjytKPjogpZWUfiIgGEXERsDswKSImVR5bRHwUEb8uq/NGRLQuS+8SEZMjYkbZv/uVpQ+IiHER8WhEzImIwWWzLGZExEsR0aSs3Pci4omImF62D6039ussSdK2zLszSJK0BYuIwpRSg0ppXwKtgSVASUqpKCL2AUanlDpFRHfg0pTS8WXl66+h3M+BuimlayIij9JZBjsADwK9U0pLI+IyYIeU0vCI+AjolFL6oppxfgT8d0rp1oi4AChIKZ0bETsBy1JKqyKiJ/AfKaW+ETEA+C/gQKAuMBu4LKV0e0T8AfhHSummiHgGOD+l9H5EHAz8LqX0wxp7gSVJ0mrya3sAkiSpxkXZv3WA2yKiI1AM7LuG8msqNxW4MyLqAONSSjMjohtwAPBiRABsD0zJOK4Hy/6dDpxS9rwRcHdZ8CKVjeVbk1JKS4AlEbEYeLQs/Q2gfUQ0AA4FHigbC5QGOSRJ0kZiEEGSpK1IROxNaSDgc0rXRvgM6EDpJYxFa6j20+rKpZSei4iuwHHAPRFxPfAl8HRK6Yz1GN6Ksn+L+dffIFdTGizoExEtgWerKQ9QUmG7pKz+dsBXKaWO6zEWSZK0HlwTQZKkrURENANuB25LpdcrNgL+mVIqAc4C8sqKLgEaVqhabbmI2Av4PKX0J+DPQAHwEnBYRHy/rEz9iNh3De1m0Qj4pOz5gFwqppS+BuZExI/KxhIR0SHH/iVJUg4MIkiStGWr9+0tHoEJwFPAr8vy/gicExEvUXqJwtKy9NeBVWW3hPzpWsp1B2ZGxAygL3BzSmkBpV/2R0fE65QGFb5dzHAE8LfqFlZci+uA30XEi/wryJGL/sBPIuI1YBZw0nq0IUmSMnJhRUmSJEmSlIkzESRJkiRJUiYGESRJkiRJUiYGESRJkiRJUiYGESRJkiRJUiYGESRJ2kJFxEcRsTwiCis8di/LaxURJRHxx2rqpW9v0Vi2fWlE/DMi2kRE94iYVyHv2Ygoiog9K6T1jIiPKrXZLyJejoilEfF52fMLIiLWMPZv2y2MiMUR8VxEtKum3ICy8Z5WKb172f59u9/zIuL+iOicw0soSZJyZBBBkqQt2wkppQYVHvPL0s8GvgT6RcQOa6ocEf8FXAJ0SynNWkOxpcCv1tLGz4GbgeuB3YBdgfOBw4Dt1zL2wSmlBsDOwLPAPdWUOQdYVPZvZfPL6jcEfgC8AzwfET3W0qckSdoABhEkSdo6nQ38F7ASOKG6AhHxG+BcoGtK6b21tHULcEbF2QsV2mgEDAcuSCmNTSktSaVmpJT6p5RWrGugKaVVwBjggEpt7wV0AwYBx0TErmuon1JK81JKw4A7gN+vq09JkrR+DCJIkrSViYgjgBaUfjG/n9KAQmXXAqdTGkD4cB1NfgL8CbiqmrxDgB2AhzdgvNsD/YGXKmWdDUxLKf0VeLuszLo8CBRExI7rOx5JkrRmBhEkSdqyjYuIr8oe48rSzgH+llL6ErgX6B0Ru1SqdzTwRErp44z9/A44ISLaVEpvCnxRNpsAgIiYXDae5RHRdS1t3hIRXwGFwGDg15Xyzy4bP2X/VndJQ2XzgQAaZygrSZJyZBBBkqQt28kppcZlj5Mjoh7wI2AUQEppCvAxcGalev2AUyOi8hf3aqWUFgC3UXrpQkULgaYRkV+h7KEppcZleWv7W+OisnJ1geOBsRHRHiAiDgNaUTqbAkqDCO0iouM6hroHkICv1rlTkiQpZwYRJEnauvQBdgL+GBGfRsSnlH6xrnxJw3tAT+CCiBiase3rgSOBgyqkTQFWACet74BTSiUppeeB2ZTOkIDSWQcBzCzbh5fL0qu7NKOiPsCrKaWl6zseSZK0ZvnrLiJJkrYg5wB3AldUSNsDmBoR7VJKb3ybmFKaFRE9gYkRUZRSumltDaeUvoqI/wZ+CSypkPZrSoMWATwBLAPaA5nXJYiIQyhdWHFWRNQFTqN0QcXxFYr1BYZFxC8r1Q1gd0oXiTwXODFrv5IkKTcGESRJ2kpExB5AD+DAlNKnFbI+jYgnKA0wXFqxTkrptYg4Bng6IooovU3i2twMXFypjesi4hNKgwv/R+ktIT8ELgMmr6Wt2yLipm/HCPxXSulvEdEPWA78X0ppZYX9+zOl6yb0onQdhd0jopDSGQuLy/rqnlKqvECjJEmqIZFSqu0xSJIkSZKkLYBrIkiSJEmSpEwMIkiSJEmSpEwMIkiSJEmSpEwMIkiSJEmSpExq7e4MTZs2TS1btqyt7iVJkiRJ0hpMnz79i5RSs8rptRZEaNmyJdOmTaut7iVJkiRJ0hpExD+qS/dyBkmSJEmSlIlBBEmSJEmSlIlBBEmSJEmSlIlBBEmSJEmSlMk6gwgRcWdEfB4Rb64hPyLiloiYHRGvR0RBzQ9TkiRJkiTVtiwzEUYCvdaS3xvYp+wxCPh/Gz4sSZIkSZK0uVlnECGl9BywaC1FTgL+L5V6CWgcEc1raoCSJEmSJGnzUBNrIuwBzK2wPa8srYqIGBQR0yJi2oIFC2qga0mSJEmStKnURBAhqklL1RVMKY1IKXVKKXVq1qxZDXQtSZIkSZI2lZoIIswD9qyw3QKYXwPtSpIkSZKkzUh+DbTxCDA4IsYABwOLU0r/rIF2N6qOPaqdLLFGM5+pbsJF7Us353YzjLj41Y00EkmSJEnS1m6dQYSIGA10B5pGxDzgSqAOQErpduBx4FhgNrAM+PHGGqwkSZIkSao96wwipJTOWEd+Ai6ssRFtpq4456uc6/TY/b2cyv/wd11y7kOSJEmSpE2lJtZEkCRJkiRJ24CaWBNBNeSza2/Ouc4u9TbCQCRJkiRJqoYzESRJkiRJUiYGESRJkiRJUiZezqBtRq639QQ4rsXinMpfc3fjnPvI9TKWXerdnXMf3tpTkiRJUk0wiCDVoImXv5JznTaNNsJAJEmSJGkjMIggaauW6wyUmc/ERhqJJEmStOVzTQRJkiRJkpSJQQRJkiRJkpSJQQRJkiRJkpSJayJIkrYq63MnFtfCkCRJysYggiRVcMU5X+Vcp8fu7+VUvk2jKTn3keutPb2tpyRJkjYGgwiSJG0Cn117c07ldx168UYaiSRJ0vpzTQRJkiRJkpSJMxEkScrRxMtfyblOm0a5lU83F+Tch5exSJKkjc2ZCJIkSZIkKRNnIkiSpC1ernfl8I4ckiStH4MIkqRtXq535eix+8YZhyRJ0ubOyxkkSZIkSVImBhEkSZIkSVImXs4gSZK0EXx27c0519l16MUbYSSSJNWcWgsiLFy4kJEjR66W1qZNGzp37szKlSsZNWpUlTodO3akY8eOLFu2jPvvv79KfqdOnWjbti2LFy/moYceqpJ/yCGHsN9++/HFF19wePvHquS/+/ERLPhqbxrt+Cntvvfkann5O6yieNGhpBW7EzvMJ6/J5Cr1ixd2I33TjKj3MXmNX+H17Zevlv/9lW2onxqwcLvP+CT/oyr190olNIjtmF38DbOKV1TJP7rOjtSL7XineAXvFn8DwParDivPP7PBS9SJYqauaMlb3+xRpf45DV8EYPLkybz33nur5dWpU4f+/fsD8Pe//505c+asll+/fn1OO+00ACZMmMC8efNWy99pp5045ZRTAHjiiSf49NNPV8vfeeedOeGEEwB49NFHWbhw4Wr5u+22G7169QLgwQcf5Ouvv14tv0WLFvTs2ROA+++/n2XLlq2W36pVK7p16wbAqFGjWLly5Wr5++67L3AIAIe3v5vKPllwAHP+2Zm87VZySNt7y9Pzd1gFQMmSAygpPAC2W07+ruOr1C/5uj0lS/dlBct5d/vXq+TvsaolO5fsyrIoZHadWavlzf5mCQfl16XFdnX4omQVL65aXqX+wfn12G27fD4tWcXflhxWJf+Yem+wW/7XfLiyGc8X7Vsl//gvvqBp06a8++67TJkypUp+nz59aNSoEW+++SbTpk2rkn/aaadRv359Zs6cycyZM6vk9+/fnzp16jB16lRmzZpVJX/AgAFA7Zx7HffZmZnvH1/2/DEa1Fv93FtcuBtvfHgMAAft9xD5Oy1aLT8VNaf4y9LXPH/Xx2C7otXyS5bvCXwHgDfrTKUkSlbLb1LcjBbFewPw+vYvA6XH/Fvfy9uetnk7sDIlHl9ZSGX75W1P67wdWFayPQ8s7Vwlv9MOc2iz/XwWl9Rl3NKD/pVR9vla8XPvsceqfu517dqVvffem08//ZQnnniiSn6PHj3Yc889mTt3Ls8880yV/F69erHbbrvx4Ycf8txzz1XJP/744zf5uXd4+3/lv/D6OQB8v8Vkdmvy/mp1i0vymfJm6bm3XeOX2a7e3NUbL6nLqs9Kz52877xI1P1nedbr2y9n+1SX1is7APBB/lss3W7JatXrlezIPqvaAvB+/purHXeAptvlcVh+fQCeWbmUwrT6ubM3+9Oj3tsA3L+0M8tLtl8tv1WdBXStW/p+GlX4A1alvPLjDqWfe4ceeihAlf9vYdP+n7spzr2Kxx1g5vvHUbi8Kbs1eZfvt3ipSv3FizfN597MVUX8o2T1/5PyCY7bvgEA01cVMa9C/vYjR24V/+duS+deZbXxuVdRbf6f67nnuQeee1vjuVeZlzNIkiRJkqRMIqXcbolUUzp16pSqi4BtKrneCuq4Fotz7qPH7u+tu1AFbRpVjRiuyy71qv6qvjZx8as597G1yPWYQ+7HPddjDrkf91yPOXjcc+F7fcvne33btDne4tHLGSRJW7KImJ5S6lQ53TURJEnSNifX23oCXHN34xofR2Xp5oKc62zLwSNJ0qbn5QySJEmSJCkTZyJIkiRlMPHyV3Iq36bRRhqIJEm1yJkIkiRJkiQpE2ciSJIkaZuQ61oY67OI6g9/1yXnOrmuheE6GJJqkzMRJEmSJElSJs5EkCRJ0hZn/W7nuhEGUsn63Npzl3obYSCStJE4E0GSJEmSJGViEEGSJEmSJGViEEGSJEmSJGXimgiSJEmSVGbi5a/kXCfXu3LkekcO8K4c2nw4E0GSJEmSJGViEEGSJEmSJGViEEGSJEmSJGViEEGSJEmSJGWSKYgQEb0i4t2ImB0RQ6vJbxQRj0bEaxExKyJ+XPNDlSRJkiRJtWmdd2eIiDzgf4CjgHnA1Ih4JKX0VoViFwJvpZROiIhmwLsRMSql9M1GGbUkSZKkbU7HHimn8jOfiY00EmnblWUmQhdgdkrpw7KgwBjgpEplEtAwIgJoACwCVtXoSCVJkiRJUq3KEkTYA5hbYXteWVpFtwH7A/OBN4CLU0ollRuKiEERMS0ipi1YsGA9hyxJkiRJkmpDliBCdXOAKs8jOgaYCewOdARui4idqlRKaURKqVNKqVOzZs1yHKokSZIkSapN61wTgdKZB3tW2G5B6YyDin4MXJtSSsDsiJgDtAZeqZFRSpIkSVKOrjjnq5zr9Ni95schbU2yzESYCuwTEa0iYnugH/BIpTIfAz0AImJXYD/gw5ocqCRJkiRJql3rnImQUloVEYOBJ4E84M6U0qyIOL8s/3bgamBkRLxB6eUPl6WUvtiI45YkSZIkSZtYlssZSCk9DjxeKe32Cs/nA0fX7NAkSZIkSdLmJMvlDJIkSZIkSQYRJEmSJElSNgYRJEmSJElSJpnWRJAkSZIkVe+za2/Oqfwu9TbSQKRNwJkIkiRJkiQpE4MIkiRJkiQpE4MIkiRJkiQpE9dEkCRJkiRtljr2SDnXmflMbISR6FvORJAkSZIkSZkYRJAkSZIkSZl4OYMkSZIkSTnI9baeALsOvXgjjGTTM4ggSZIkSdpqXHHOVznX6bH7ezmVb9Mo5y62Gl7OIEmSJEmSMjGIIEmSJEmSMvFyBkmSJEmSNrJ0c0FO5ePiVzfSSDaMMxEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImBhEkSZIkSVImmYIIEdErIt6NiNkRMXQNZbpHxMyImBURf6/ZYUqSJEmSpNqWv64CEZEH/A9wFDAPmBoRj6SU3qpQpjHwR6BXSunjiNhlI41XkiRJkiTVkiwzEboAs1NKH6aUvgHGACdVKnMm8GBK6WOAlNLnNTtMSZIkSZJU27IEEfYA5lbYnleWVtG+wHci4tmImB4RZ1fXUEQMiohpETFtwYIF6zdiSZIkSZJUK7IEEaKatFRpOx84CDgOOAb4VUTsW6VSSiNSSp1SSp2aNWuW82AlSZIkSVLtWeeaCJTOPNizwnYLYH41Zb5IKS0FlkbEc0AH4L0aGaUkSZIkSap1WWYiTAX2iYhWEbE90A94pFKZh4EjIiI/IuoDBwNv1+xQJUmSJElSbVrnTISU0qqIGAw8CeQBd6aUZkXE+WX5t6eU3o6IJ4DXgRLgjpTSmxtz4JIkSZIkadPKcjkDKaXHgccrpd1eaft64PqaG5okSZIkSdqcZLmcQZIkSZIkySCCJEmSJEnKxiCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKxCCCJEmSJEnKJFMQISJ6RcS7ETE7IoaupVzniCiOiFNrboiSJEmSJGlzsM4gQkTkAf8D9AYOAM6IiAPWUO73wJM1PUhJkiRJklT7ssxE6ALMTil9mFL6BhgDnFRNuSHAX4HPa3B8kiRJkiRpM5EliLAHMLfC9ryytHIRsQfQB7h9bQ1FxKCImBYR0xYsWJDrWCVJkiRJUi3KEkSIatJSpe2bgMtSSsVrayilNCKl1Cml1KlZs2YZhyhJkiRJkjYH+RnKzAP2rLDdAphfqUwnYExEADQFjo2IVSmlcTUxSEmSJEmSVPuyBBGmAvtERCvgE6AfcGbFAimlVt8+j4iRwGMGECRJkiRJ2rqsM4iQUloVEYMpvetCHnBnSmlWRJxflr/WdRAkSZIkSdLWIctMBFJKjwOPV0qrNniQUhqw4cOSJEmSJEmbmywLK0qSJEmSJBlEkCRJkiRJ2RhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmRhEkCRJkiRJmWS6xeOmsnLlSubNm0dRUdFG7+vGX+VWvl5eSc591M3bPqfyC7b7Qc59LIp2uVV4++2c+9ha5HrMIdtxTwkWf1GHqU81WY9RSZIkSdKWY7MKIsybN4+GDRvSsmVLImKj9lWyXcqpfOPti3Puo2GdFTmVr5dXmHMf+dstzKl87Lp/zn1sLXI95pDtuKeUWLrzImARTF2PgUmSJEnSFmKzupyhqKiInXfeeaMHEKSaFBHsWK8JjZqurO2hSJIkSdJGtVkFEQADCNoiRQSeupIkSZK2dptdEEGSJEmSJG2eNusgQsceqUYfWU14+iHatt6ODz98ZyPu3aY3csxDDB48eIPbGTZsGBMmTADgpptuYtmyZTnVHzBgAGPHjgWge/fuTJs2rUqZ559/njZt2tCxY0eWL1+e8xh/+9vf5lxHkiRJkrR2m3UQobb8bfwYCg46nL+NH7NR+ykuzn2xxs3B8OHD6dmzJ7B+QYQsRo0axaWXXsrMmTOpV69ezvXXJ4iwatWqnOtIkiRJ0rbEIEIly5YWMuPVFxn+mzt44vH7ytOLi4u5+re/pEevjvTsfSB33n0bADNfm8pJpx7BUccWcNzJh1BYuIT7x97NFVdeVF73tAGn8vyU5wDYfb9dueaGq/nhCd15ZfrL/P6m39H9uK78oEdn/uMXl5JS6YyJ2XPm0Ou0H3FQzx/S5eij+OCjjxgwZDCPPPFEebtnX3gBjzw5cYP3+cYbb6Rt27a0bduWm266qTz96quvpnXr1hx11FGcccYZ3HDDDcC/ZhLccsstzJ8/nyOPPJIjjzyySrvDhw+nc+fOtG3blkGDBpXv27rccccd3H///QwfPpz+/fsDcP3119O5c2fat2/PlVdeWV725JNP5qCDDqJNmzaMGDECgKFDh7J8+XI6duxI//79+eijj2jbtm15nbv+fAP/c+tVpfty1pHcdON/MuDfuvOX/7uZWW9OZ8C/dee0Uzox6Ce9+OzzfwLw55G3cuTR7enZ+0AuuKh/9hdXkiRJkrYim9UtHjcHzzwzjsOOOIaWrfZlp0ZNeGvWqxzQpoBRo//Ex3M/4snHppGfn8+XXy3im2++4YKL+vPHW0bRsUNnliz5mrp11/6r+dJlS9l/vwO44tJfAbDfPq257JLLAfiPSwYw/umnOf7oozln8AX8YvAQTu59LEVFRZSUlPDvZ57JzSNGcGKvXiz++mtemjaV/7v1yrV1t07Tp0/nrrvu4uWXXyalxMEHH0y3bt0oLi7mr3/9KzNmzGDVqlUUFBRw0EEHrVb3oosu4sYbb2TSpEk0bdq0StuDBw9m2LBhAJx11lk89thjnHDCCesc07nnnssLL7zA8ccfz6mnnspTTz3F+++/zyuvvEJKiRNPPJHnnnuOrl27cuedd9KkSROWL19O586d6du3L9deey233XYbM2fOBOCjjz5aa39LlnzFyL88y8qVKxlwVndu/eM4mjRpxt8ev4/rbvgV/33dHfzx9uuZ/Pf32WGHHVj89VeZXltJkiRJ2toYRKjk8fFjOOvsiwHofezpPD5+NAe0KeD5Fydy1pmDyM8vfcm+07gJb7/zBrvsshsdO3QGoGHDndbZfl5eHicde3L59vNTnuPm/3cTy5cv46vFizhg3/3oduihzP/0U07ufSwAdevWBaDrIYdy0X9ezudfLGDc44/T59jjysezvl544QX69OnDjjvuCMApp5zC888/T0lJCSeddFL5pQRZvvxXNmnSJK677jqWLVvGokWLaNOmzXq189RTT/HUU09x4IEHAlBYWMj7779P165dueWWW3jooYcAmDt3Lu+//z4777xzTu336n06AB/NeZfZ77/JwH8/GoCS4mKa77obAK1bt2PIT8/mmKNPpNdRJ+W8D5IkSZK0NTCIUMFXXy7klZcmMvv9NwmC4pJiIoKf/+I6UkpVbj+ZUiKoel+/vPx8UklJ+XbRiqLy53V3qEteXl5pelERP7/ipzw7/nla7N6C6/9wJUUritY67b9/31MZ/eCD3P/wOEbc+Icq+Vf87mYen/B3AGY88+A693lNfWW99GBNioqKuOCCC5g2bRp77rknV111FUVFReuuuIaxXH755Zx33nmrpT/77LNMmDCBKVOmUL9+fbp3715tH/n5+ZRUOB7ffLN6mXr1dizv5/vfb8Oo+yaX5zXevnTdiv/78yO89MrzPD3hUW6+9bdMfPK1DQ7gSJIkSdKWxjURKnjqybGceNJZPD3xI56aOIdnnv2YFnu04tXpL9DtiJ7cc++I8sX3vvxqEd//Xms++/yfzHxtKgCFhUtYtWoVe+6xF7Pefo2SkhLmzZ/HqzOnV9vft8GFnb+zM4VLC3lw/GMA7NSwIXs0b87Df/sbACtWrChfvPDs0/tx659Kr/1vs1/rKm1ec/nFzHjmwUwBBICuXbsybtw4li1bxtKlS3nooYc44ogjOPzww3n00UcpKiqisLCQ8ePHV1u/YcOGLFmypOq+lX2Zb9q0KYWFheV3Y1gfxxxzDHfeeSeFhYUAfPLJJ3z++ecsXryY73znO9SvX5933nmHl156qbxOnTp1WLlyJQC77rorn3/+OV99uZBvvlnB35+tfl9atdqPRV8uYOaMKQCsXLmSd9+bRUlJCfP/OZfDDunOFUOv5euvv2LpssL13h9JkiRJ2lJt1j+lznym6q/8G9Pj48dw7qDLVkvrefQpjH/sXq4bfhMfznmfo44tID8/nzP7/YQfn30hf7xlFL/69SUUFS2nbt16jLnnSTp3Oow9W7TkkKO6sP++B9ChbYdq+2vcqDHnnDGAQ446mO/u+V06dehYnnfXLbdx4WW/4Nc3XEed/DqMHvEn9t5rL3Zt1ozW++zLib16rdc+jhw5knHjxpVvv/TSSwwYMIAuXboApesRfHvZwIknnkiHDh3Ya6+96NSpE40aNarS3qBBg+jduzfNmzdn0qRJ/9q3xo0ZOHAg7dq1o2XLlnTu3Hm9xgtw9NFH8/bbb3PIIYcA0KBBA/7yl7/Qq1cvbr/9dtq3b89+++3HD37wg9XG1b59ewoKChg1ahTDhg3jjNN/wB4tWtGqVdXgC0Cd7bfnDzc/wO+uuZglSxZTXLyKQf8+hL1b7ctFPz2Hr5csLn2N/v1iGu3UeL33R5IkSZK2VLGh09bXV6dOndK0adNWS3v77bfZf//9N0n/s97Nbb+/ndaei4Z1VuRUvl7eun/dXrZsGQU9juTlJ5+m0U47kb/dwpz6iF0PyFy2sLCQBg0asGzZMrp27cqIESMoKCjIqb/NSa7HHHI77h/Pe5fljy/NuY82jabkVH6Xenfn3Edc/GrOdbYWHXvkdtyPa7E45z567P5eTuVzPeaQ+3H3mOcm1+Oe6zEH3+sb27b6Xodt97hvLe918DM+F5vjex38jN+YNsV7HTbPz/jaPuYRMT2l1KlyupczbEGeee452nU9ggv+/Sc02mndizhuqEGDBtGxY0cKCgro27fvFh1AkCRJkiRtuM36cgatrkfXrnwwrfr1FTaGe++9d5P1JUmSJEna/DkTQZIkSZIkZWIQQZIkSZIkZWIQQZIkSZIkZWIQQZIkSZIkZbJZL6x4xTlf1Wh719zdOFO5CU8/xCVD+vLI42+x996ta3QMtWnkmIeY/v4fue222zaonWHDhtG1a1d69uzJTTfdxKBBg6hfv34NjVKSJEmStLlyJkI1/jZ+DAUHHc7fxo/ZqP0UFxdv1PY3luHDh9OzZ08AbrrpJpYtW1bLI5IkSZIkbQoGESpZtrSQGa++yPDf3METj99Xnl5cXMzVv/0lPXp1pGfvA7nz7tJf82e+NpWTTj2Co44t4LiTD6GwcAn3j72bK668qLzuaQNO5fkpzwGw+367cs0NV/PDE7rzyvSX+f1Nv6P7cV35QY/O/McvLiWlBMDsOXPoddqPOKjnD+ly9FF88NFHDBgymEeeeKK83bMvvIBHnpy4wft844030rZtW9q2bctNN91Unn711VfTunVrjjrqKM444wxuuOEGAAYMGMDYsWO55ZZbmD9/PkceeSRHHnnkBo9DkiRJkrR526wvZ6gNzzwzjsOOOIaWrfZlp0ZNeGvWqxzQpoBRo//Ex3M/4snHppGfn8+XXy3im2++4YKL+vPHW0bRsUNnliz5mrp16621/aXLlrL/fgdwxaW/AmC/fVpz2SWXA/Aflwxg/NNPc/zRR3PO4Av4xeAhnNz7WIqKiigpKeHfzzyTm0eM4MRevVj89de8NG0q/3frlRu0v9OnT+euu+7i5ZdfJqXEwQcfTLdu3SguLuavf/0rM2bMYNWqVRQUFHDQQQetVveiiy7ixhtvZNKkSTRt2nSDxiFJkiRJ2vw5E6GSx8ePofex/QDofezpPD5+NADPvziRs84cRH5+adzlO42b8MGH77LLLrvRsUNnABo23Kk8f03y8vI46diTy7efn/IcPzyhO4f07MKzL77AW+++y5LCQuZ/+ikn9z4WgLp161K/fn26HnIoH3w0h8+/WMB94x6iz7HHrbO/dXnhhRfo06cPO+64Iw0aNOCUU07h+eef54UXXuCkk06iXr16NGzYkBNOOGGD+pEkSZIkbfmciVDBV18u5JWXJjL7/TcJguKSYiKCn//iOlJKRMRq5VNKBFGlnbz8fFJJSfl20Yqi8ud1d6hLXl5eaXpRET+/4qc8O/55Wuzeguv/cCVFK4rKL2moTv++pzL6wQe5/+FxjLjxD1Xyr/jdzTw+4e8AzHjmwXXu85r6WtsYJEmSJEnbJmciVPDUk2M58aSzeHriRzw1cQ7PPPsxLfZoxavTX6DbET25594RrFq1CoAvv1rE97/Xms8+/yczX5sKQGHhElatWsWee+zFrLdfo6SkhHnz5/HqzOnV9vdtcGHn7+xM4dJCHhz/GAA7NWzIHs2b8/Df/gbAihUryhcvPPv0ftz6pxEAtNmv6p0jrrn8YmY882CmAAJA165dGTduHMuWLWPp0qU89NBDHHHEERx++OE8+uijFBUVUVhYyPjx46ut37BhQ5YsWZKpL0mSJEnSlm2znomQ9ZaMNeXx8WM4d9Blq6X1PPoUxj92L9cNv4kP57zPUccWkJ+fz5n9fsKPz76QP94yil/9+hKKipZTt249xtzzJJ07HcaeLVpyyFFd2H/fA+jQtkO1/TVu1JhzzhjAIUcdzHf3/C6dOnQsz7vrltu48LJf8OsbrqNOfh1Gj/gTe++1F7s2a0brffblxF691msfR44cybhx48q3X3rpJQYMGECXLl0AOPfccznwwAMBOPHEE+nQoQN77bUXnTp1olGjRlXaGzRoEL1796Z58+ZMmjRpvcYkSZIkSdoybNZBhE1t5D1VvwT/29mld1nIzy/myv+6gcrLGHbs0JlHH3yxSr3bbrqHhnVWVEmf/+5nq23/6pdX8qtflrZaL6+wPH2fvffmqQf+WqX+smXLmD3nQ04/uc8696eyAf368OOLr6iS/rOf/Yyf/exnVdIvvfRSrrrqKpYtW0bXrl35+c9/DpQGIr41ZMgQhgwZkvNYJEmSJElbHi9n2II889xztOt6BBf8+09otNNOG72/QYMG0bFjRwoKCujbty8FBQUbvU9JkiRJ0ubLmQhbkB5du/LBtOrXV9gY7r333k3WlyRJkiRp8+dMBEmSJEmSlEmmIEJE9IqIdyNidkQMrSa/f0S8XvaYHBHVryQoSZIkSZK2WOsMIkREHvA/QG/gAOCMiDigUrE5QLeUUnvgamBETQ9UkiRJkiTVriwzEboAs1NKH6aUvgHGACdVLJBSmpxS+rJs8yWgRc0OU5IkSZIk1bYsCyvuAcytsD0POHgt5X8C/K26jIgYBAwC+O53v7vOjide/kqG4WX3w991yVRuwtMPccmQvjzy+FvsvXfrGh1DbfpswRec+5PjmTt3LitXrqRly5Y8/vjjtGrViieeeIL99tuvvOwll1zC7rvvTpcuXTjyyCO54447+MlPfgLAjBkzKCgo4Prrr+fSSy+trd2RJEmSJG1iWWYiRDVpqdqCEUdSGkS4rLr8lNKIlFKnlFKnZs2aZR/lJva38WMoOOhw/jZ+zEbtp7i4eKO2X9mw627jqKOO4rXXXuOtt97i2muvBaBfv36MGfOvfS0pKWHs2LGcfvrpALRr14777ruvPH/MmDF06OCyF5IkSZK0rckSRJgH7FlhuwUwv3KhiGgP3AGclFJaWDPD2/SWLS1kxqsvMvw3d/DE4//64lxcXMzVv/0lPXp1pGfvA7nz7tsAmPnaVE469QiOOraA404+hMLCJdw/9m6uuPKi8rqnDTiV56c8B8Du++3KNTdczQ9P6M4r01/m9zf9ju7HdeUHPTrzH7+4lJRK4zOz58yh12k/4qCeP6TL0UfxwUcfMWDIYB554onyds++8AIeeXJi5n379LMFtGjxrytN2rdvD8AZZ5yxWhDhueeeo2XLluy1115A6ayRoqIiPvvsM1JKPPHEE/Tu3Ttzv5IkSZKkrUOWIMJUYJ+IaBUR2wP9gEcqFoiI7wIPAmellN6r+WFuOs88M47DjjiGlq32ZadGTXhr1qsAjBr9Jz6e+xFPPjaNCX+bQZ+TzuSbb77hgov68+tf3cjTj7/KmHuepG7demttf+mypey/3wFMfPRZDulyKAPPOY9nxz/HS89MZXlREeOffhqAcwZfwPk//jHTJ0zkuUcepfkuu/DvZ57J3feVftlf/PXXvDRtKsf26Jp53y748Rn85Cc/4cgjj+Saa65h/vzSWFD79u3ZbrvteO2114DSmQZnnHHGanVPPfVUHnjgASZPnkxBQQE77LBD5n4lSZIkSVuHdQYRUkqrgMHAk8DbwP0ppVkRcX5EnF9WbBiwM/DHiJgZEdM22og3ssfHj6H3sf0A6H3s6Tw+fjQAz784kbPOHER+fukyEt9p3IQPPnyXXXbZjY4dOgPQsOFO5flrkpeXx0nHnly+/fyU5/jhCd05pGcXnn3xBd56912WFBYy/9NPObn3sQDUrVuX+vXr0/WQQ/ngozl8/sUC7hv3EH2OPW6d/VV0zJGH8+GHHzJw4EDeeecdDjzwQBYsWAD8azbCqlWrePjhh/nRj360Wt3TTjuNBx54gNGjR1cJMEiSJEmStg2ZvoGmlB4HHq+UdnuF5+cC59bs0Da9r75cyCsvTWT2+28SBMUlxUQEP//FdaSUiFh9eYiUElHNkhF5+fmkkpLy7aIVReXP6+5Ql7y8vNL0oiJ+fsVPeXb887TYvQXX/+FKilYUlV/SUJ3+fU9l9IMPcv/D4xhx4x+q5F/xu5t5fMLfAZjxzINV8ps0acKZZ57JmWeeyfHHH89zzz1H3759OeOMMzj66KPp1q0b7du3Z5dddlmt3m677UadOnV4+umnufnmm5k8efIaxyhJkiRJ2jpluZxhm/HUk2M58aSzeHriRzw1cQ7PPPsxLfZoxavTX6DbET25594RrFq1CoAvv1rE97/Xms8+/yczX5sKQGHhElatWsWee+zFrLdfo6SkhHnz5/HqzOnV9vdtcGHn7+xM4dJCHhz/GAA7NWzIHs2b8/DfSm9ysWLFCpYtWwbA2af349Y/jQCgzX5V7xxxzeUXM+OZB6sNIEx84aXydpYsWcIHH3xQfpeM733ve+y8884MHTp0jTMNhg8fzu9///vyIIgkSZIkaduSfS58Lch6S8aa8vj4MZw7aPUbS/Q8+hTGP3Yv1w2/iQ/nvM9RxxaQn5/Pmf1+wo/PvpA/3jKKX/36EoqKllO3bj3G3PMknTsdxp4tWnLIUV3Yf98D6NC2+jsZNG7UmHPOGMAhRx3Md/f8Lp06dCzPu+uW27jwsl/w6xuuo05+HUaP+BN777UXuzZrRut99uXEXr1y3r/pr73FkF91Ij8/n5KSEs4991w6d+5cnn/GGWdw+eWX06dPn2rrH3rooTn3KUmSJEnaemzWQYRNbeQ9k6qk/dvZpXdZyM8v5sr/uoErK+V37NCZRx98sUq92266h4Z1VlRJn//uZ6tt/+qXV/KrX5a2Wi+vsDx9n7335qkH/lql/rJly5g950NOP7n6L/pr84sL/51fDr9hjfk//elP+elPf7paWvfu3enevXuVsldddVXO/UuSJEmStmxezrAFeea552jX9Qgu+Pef0GinnWp7OJIkSZKkbYwzEbYgPbp25YNp1a+vIEmSJEnSxuZMBEmSJEmSlIlBBEmSJEmSlIlBBEmSJEmSlIlBBEmSJEmSlMlmvbDiZ9feXKPt7Tr04kzlJjz9EJcM6csjj7/F3nu3rtEx1KaRYx7il7/pxh577EFRURHnnXde+S0dr7rqKv70pz/RrFkzAHr16sW1115bm8OVJEmSJG1mnIlQjb+NH0PBQYfzt/FjNmo/xcXFG7X96px++unMnDmTF198kWuuuYa5c+eW5/30pz9l5syZzJw50wCCJEmSJKkKgwiVLFtayIxXX2T4b+7gicfvK08vLi7m6t/+kh69OtKz94HcefdtAMx8bSonnXoERx1bwHEnH0Jh4RLuH3s3V1x5UXnd0wacyvNTngNg9/125ZobruaHJ3Tnlekv8/ubfkf347rygx6d+Y9fXEpKCYDZc+bQ67QfcVDPH9Ll6KP44KOPGDBkMI888UR5u2dfeAGPPDlxvfZz55135vvf/z7//Oc/16u+JEmSJGnbYxChkmeeGcdhRxxDy1b7slOjJrw161UARo3+Ex/P/YgnH5vGhL/NoM9JZ/LNN99wwUX9+fWvbuTpx19lzD1PUrduvbW2v3TZUvbf7wAmPvr/27v7+CqKe4/jn59JNFgBHyoqoiAqIChERGzUUK6A8nAFUZSAVUEttWpVriK2alF7lVvF4gNtvXivD0VLqC0IAoo8SIMUqjwqalEQ6rU+o6IxBIX87h87iYeTc05OYnhIzvf9euWV7Ozs7pwzu7Ozk5nZheR3PZUfX/ITFs4qZun8l9lSVsasuXMBuOTqK7li+HCWz1tA8YxnOKxZMy4dOpTHp0S9IzZ/8QVLl71M3x7davU533nnHcrKyujYsWNl2Pjx48nLyyMvL485c+bUar8iIiIiIiLScKkRIc7sWUX06VsIQJ++g5k9azIAixYv4KKhI8jOjqaROGD/A1n/9lqaNTuUvE4nA9C4cZPK9clkZWUxoO85lcuLlhRzxtndye/ZlYWLX+T1tWv5sqSE9z74gHP69AUgNzeXfffdl275p7J+4wY++uRjpjw9jYF9+1V7vHhTpkyhQ4cOtG7dmmuvvZbc3NzKdbHDGc4666wa7VdEREREREQavj16YsVd7fPPNvHS0gWse2sNhrG9fDtmxvWj7sbdMbMd4rs7hlXZT1Z2Nl5eXrlctrWs8u/cfXLJysqKwsvKuP7mkSyctYgWzVtwz/gxlG0tqxzSkMiF5w1i8tSp/Gn600z8zfgq628eez+z5/0VgJXzp1ZZP3jwYCZMmMCSJUvo168fffr04dBDD63mmxERERERERFRT4QdPD/nz/QfcBFzF2zk+QUbmL/wHVocfhQrlr/IDwt6MumPE9m2bRsAn33+Kccc3Y4PP3qfVatfBqCk5Eu2bdvGEYe35LU3VlNeXs67773LilXLEx6vonHhoAMOouSrEqbOmglAk8aNOfyww5j+7LMAbN26ldLSUgAuHlzIgw9PBKBD26pvjrjz59eycv7UhA0IsfLz87nooou4//66fQOGiIiIiIiINFx7dE+EdF/JWFdmzyri8hGjdwjreea5zJr5R+6+4z7e3vAWvfp2Jjs7m6GFlzH84qv43QNPcuvt11FWtoXc3EYUTZrDyV1O44gWrcjv1ZXj2rSn0/GdEh5v/6b7c8mQYeT3OoUjjziSLp3yKtc9+sAErho9itvH3U1Odg6TJz5M65YtOeTgg2l3bBv69+79nT/v6NGj6dy5M7/4xS++875ERERERESk4dujGxF2tccmvVAl7EcXR29ZyM7ezphbxjEmbn1ep5N5ZuriKttNuG8SjXO2Vgl/b+2HOyzfeuMYbr0x2mujrJLK8GNbt+b5p/5SZfvS0lLWbXibwecMrPbzxBtWOJDhh7SvXG7evDkffPABALfddluN9yciIiIiIiKZRcMZ6pH5xcWc0K2AKy+9jKZNmuzu5IiIiIiIiEiGUU+EeqRHt26sX5Z4fgURERERERGRnU09EUREREREREQkLWpEEBEREREREZG0qBFBRERERERERNKiRgQRERERERERScsePbGi39+5Tvdn165IK968udO47mfnMWP267Ru3a5O07A7PVY0jUuv68C8efPo0aMHANOmTePcc8/lqaeeYtCgQXTv3p1x48bRpUuXyu0WLlzIgAEDaN26NWVlZRQWFjJmTPzLLkVERERERKShU0+EBJ6dVUTnk07n2VlFO/U427dv36n7T+SEE05g8uTJlctFRUV06tSp2u0KCgpYuXIly5Yt44knnmD5cr0lQkREREREJNOoESFO6VclrFyxmDv+8394bvaUyvDt27fzq7tupEfvPHr2OZFHHp8AwKrVLzNgUAG9+nam3zn5lJR8yZ/+/Dg3j7mmctsLhg1i0ZJiAJq3PYQ7x/2KM87uzkvL/86v7xtL937d+EGPk/npqBtwdwDWbdhA7wvO56SeZ9D1zF6s37iRYT+7mhnPPVe534uvupIZcxbU6PMVFBTw0ksv8c0331BSUsK6devIy8tLe/vvfe97nHTSSaxfv75GxxUREREREZH6T40IcebPf5rTCs6i1VFtaNL0QF5/LRoC8eTkh3nn/zYyZ+Yy5j27koEDhvL1119z5TUXcvutv2Hu7BUUTZpDbm6jlPv/qvQrjmvbngXPLCS/66n8+JKfsHBWMUvnv8yWsjJmzZ0LwCVXX8kVw4ezfN4Cimc8w2HNmnHp0KE8PiXqHbH5iy9Yuuxl+vboVqPPZ2b07NmTOXPmMH36dPr371+j7Tdt2sTSpUvp0KFDjbYTERERERGR+k+NCHFmzyqiT99CAPr0HczsWVHX/0WLF3DR0BFkZ0fTSByw/4Gsf3stzZodSl6nkwFo3LhJ5fpksrKyGND3nMrlRUuKOePs7uT37MrCxS/y+tq1fFlSwnsffMA5ffoCkJuby7777ku3/FNZv3EDH33yMVOensbAvv2qPV4ihYWFFBUVUVRUxJAhQ9LaZtGiRZx44omceeaZ3HTTTWpEEBERERERyUB79MSKu9rnn23ipaULWPfWGgxje/l2zIzrR92Nu2NmO8R3dwyrsp+s7Gy8vLxyuWxrWeXfufvkkpWVFYWXlXH9zSNZOGsRLZq34J7xYyjbWlY5pCGRC88bxOSpU/nT9KeZ+JvxVdbfPPZ+Zs/7KwAr509NuI+uXbuyZs0aGjVqRJs2bVJ8I98qKChg5syZacUVERERERGRhkk9EWI8P+fP9B9wEXMXbOT5BRuYv/AdWhx+FCuWv8gPC3oy6Y8T2bZtGwCfff4pxxzdjg8/ep9Vq18GoKTkS7Zt28YRh7fktTdWU15ezrvvvcuKVYknIaxoXDjogIMo+aqEqbOih/QmjRtz+GGHMf3ZZwHYunUrpaWlAFw8uJAHH54IQIe2Vd8ccefPr2Xl/KlJGxAqjB07lrvuuqumX5GIiIiIiIhksD26J0K6r2SsK7NnFXH5iNE7hPU881xmzfwjd99xH29veItefTuTnZ3N0MLLGH7xVfzugSe59fbrKCvbQm5uI4omzeHkLqdxRItW5PfqynFt2tPp+MRvP9i/6f5cMmQY+b1O4cgjjqRLp7zKdY8+MIGrRo/i9nF3k5Odw+SJD9O6ZUsOOfhg2h3bhv69e3+nz9qnT5+k6/r160dOTg4A+fn5XHXVVd/pWCIiIiIiItIw7NGNCLvaY5NeqBL2o4ujtyxkZ29nzC3jGBO3Pq/TyTwzdXGV7SbcN4nGOVurhL+39sMdlm+9cQy33hjttVFWSWX4sa1b8/xTf6myfWlpKes2vM3gcwZW+3niDSscyPBD2lcJf+yxxyr/XrhwYcJtu3fvXuPjiYiIiIiISMOi4Qz1yPziYk7oVsCVl15G0yZNdndyREREREREJMOoJ0I90qNbN9YvSzy/goiIiIiIiMjOtsf1REj1ZgKRPZW7o1NXREREREQauj2qESE3N5dNmzapIUHqFXfnqy2fsvmTnN2dFBERERERkZ1qjxrO0KJFC959910+/vjjnX6sDz+sPk6sL7LKa3yM3KxvahQ/Z6+qEzFWJ8u+qtkGn1qNj9FQ1DTPIb18d4fNn+Tw8vMHcvoBn9f8ICIiIiIiIvXEHtWIkJOTw1FHHbVLjjXk6pr1dujXYnONj9Gj+Zs1it+h6dIaH6NZo8drFH9XvzZzT1LTPIda5PsBNT6EiIiIiIhIvZHWcAYz621ma81snZndlGC9mdkDYf0rZta57pMqIiIiIiIiIrtTtY0IZpYF/BboA7QHhphZ+7hofYBjw88I4Pd1nE4RERERERER2c3S6YnQFVjn7m+7+9dAETAgLs4A4A8eWQrsb2aH1XFaRURERERERGQ3surehGBmg4De7n55WL4IOMXdr46JMxP4L3d/MSzPB0a7+7K4fY0g6qkA0BZYW1cfpJ75PvDJ7k6E7HLK98yjPM9MyvfMozzPTMr3zKM8z0yZnO8t3f3g+MB0JlZMNJ1/fMtDOnFw94nAxDSO2aCZ2TJ377K70yG7lvI98yjPM5PyPfMozzOT8j3zKM8zk/K9qnSGM7wLHBGz3AJ4rxZxRERERERERKQeS6cR4WXgWDM7ysz2BgqBGXFxZgAXh7c0/ADY7O7v13FaRURERERERGQ3qnY4g7tvM7OrgTlAFvCIu79mZleE9Q8Bs4G+wDqgFBi+85LcIGT8kI4MpXzPPMrzzKR8zzzK88ykfM88yvPMpHyPU+3EiiIiIiIiIiIikN5wBhERERERERERNSKIiIiIiIiISHrUiCAiIiIiIiIiaWnwjQhmtt3MVpnZGjN7ysz2DeElcfGGmdmEmOURZvaP8LPMzLrHrDvDzFaEfT5uZiknqDSz3mb2UtjXKjObYmZHxqzPNrNPzGxs3HYLzWytmb0Stp1gZvuHdW3Dvip+vjCz69L8TlqZ2ZqY5cnhGCPT2T7TVHcOhe9zi5mtNLM3Ql5fErN9dzPbHJNXv6zBsW8zsxvC3+3C9ivN7Oi6/pyZyszczO6NWb7BzG6Li7PazCbHhe1tZveZ2XozW2dmM2Ov6yTHij2Xnqm4ntNM52NmNij8XWBmr4V9NUp3H5Jagms6toy9OKy71MxeDWXmGjMbEMIfM7MNIe5qM+tRg+N2N7OZ4e99zGxe2M/gnfE5M12KMj3h9RnOBzezX8Xs4/tm9k269YY00qRzYBexqvW/kWZWZmZNY8Iq7tsrQz2s2Mz+PW475Xc9keKaP9TMisJ9/HUzm21mbSyunhyzn9vM7F8x94W+NUjDQjPrEv4+36L64gt19ykzS3X1qfh6m5lNM7NzYpbXmtktMct/MbNzkxwrZXkQd168ZWZTzax9zPqEz3NmdlDMufRB3Lm1d918UztPg29EALa4e567Hw98DVxR3QbhxPgJcLq7twNGAE+Y2eFmthfwOFAY9vlP4JIU+zoeeBC4xN3buXse8CTQKibamcBa4AIzs7hdXOjuHYGOwFZgOoC7rw2fKw84ieitGNOq+2wJ0ncocKq7d3T38TXdPkOkcw6td/cT3f04otegjjSz2LeULKrIL3e/o5bpOAeYHo6zvpb7kKq2Auea2fcTrTSz44jKym5m9r2YVXcBjYE27n4M8Bdgeigjkok9lz4Frqplmi8ExoV9banlPiS19THXbJ67/8HMWgA3E90bOgI/AF6J2WZUKJOvAx6q5XFPBHLCMad8h/RLcsnK9FTX59tA7EPk+cBrFQup6g21SJ/OgV1rCNHrzAfGhS8K99u2wDXABAuNg8rveqfKNR/q29OAhe5+tLu3B34BHFLNvsbH3Bdm1zI9lwFXuvu/1XJ7SVFeJ6m3/Q04Naw/CCgB8mP2lx/iJJO0PAgqzotjgSnAAjM7OGZ9lec5d98U8yz3EDueW1/X8PvY5TKhESHWIuCYNOKNJqoMfgLg7iuAR4lO0IOAre7+Zog7Fzivmn3d5e5vVAS4+wx3L46JMwS4H3iHqFJaRTiZbgSONLNOcat7EFV4/5ksEWZ2UmiVW8KOFaPngWah1asgxeeQSLXnkLu/DfwHUSFTY2Z2c2ixnAe0DWF9iR5MLlfLdZ3bRvTqnmQ9cYYCk4iulf4A4b8Yw4GR7r4dwN0fJbop9UzzuEuApBVOi0wI/x2ZBTQL4ZcDFwC/NLMn0zyW1I1mwJdE+Yy7l7j7hgTxUuYtVPZQ+4eZvQicG8KaAU8AeaFMVo+jnS9ZmR6fh1uANyr+kwgMBv4Usz5VvSEhnQO7X/h+9wNuIaqLJeTuq4A7gKtDkPK7/qq45v8N+Ca8qh6I8tndF9XFQcysUejl8IqZTQEahfBfAqcDD5nZPXVxLKlSXleptwGLCY0I4fdM4OBQ1zqKqFHig3QOlqA8iF8/JRx7aIJ1qZ7n6pWMaUSwaMhBH+DVENQopsvIKqKToUIHYHncLpYB7YFPgJyYisQg4IgUh+4ArEiRrkZEjQAzgcmkvoltB1YD7eJWFYZtU3kUuMbd8+PC+/Ptf9zqpOBsqBKcQ6msYMd8yg+NOM+aWYcUxziJKD9PJKpknAwQWrsrWinVcl33fgtcaDHdWWMMJmpVjr0+jwHecfcv4uJWlBMpmVkW0XU/I0W0gUSNSCcAPybc/Nz9f8J2o9z9wuqOJbV2tO04nKGAqPz9ENhgZo+a2dlJtu0NPJ1sx2aWCzwMnA0UAIcCuPtHwOV823NJPY52omRleorrswgoDD1StgPvxaxLVW9IdGydA3uGIURl+yKgbXioTyb2vq78rofirvnjqZqH6bg6NAw8YmYHpIj3U6A0/Pf5TqJew4TeqMuI/jM9qhbHlxhJyutE9bblwPFhmMCpRA0Pa4HjwvLiGh46vp6f9voUz3P1SiY0IjQKjQTLiP7T/78hvKIbTEU3kurGqRuAuzvRQ954M3uJ6L9S29JJSMzYlzctjHMn6h75gruXEnWHHhguiJTpiNnn3kQNAU+lOG5TYH93/2sImpROeqVSsnMoldh8WgG0dPdORENbnk6xXQEwzd1LwwNqqodMqSPhu/4Dcb1HzOxk4OPQy2c+0DlUGgzwBLuKH44Ur+Jc2gQcSNSTKZluwGR33+7u7wEL0vksUmfihzMsCjf+3kSNx28S3Qdui9nmHjN7m+g/i3el2Hc7YIO7vxXuKU/spM8giSUr06u7Pp8DehFVStPpdp6qPNA5sGcoBIrcvRyYSjRMJZnqynfl956rNvW4RH4PHA3kAe8D96aI242Qz+7+CjsOfZPvLmF5naze5u5biYagdSbq9f13ooaEU8NPqqEMiXyX8iCd9Xu8TGhEiG0s+FmaY0xeJ7QYxuhMVPjg7kvcvcDduwLFwFsp9lVxwlIx9oWo6/R+Yf0QoKeZbSRqJTuIqItVFaFx4QTgjZjgPsAKd/8wRRqSPfBIempzDp1IyCd3/8LdK7o/zybqyZJw/H2gvNo97iMapxg778EQoF24PtcDTYiGL60DWppZ47h9VJYTSWwJZUBLYG+qnxNB58IexiMvuftYogeQ2OFso4h6qdxCNHdOyl3tpCRK9ZKV6SmvzxBvOXA9UaN/rJT1hiR0DuxGZtYROBaYG8r4QlL0BiXmvo7yu75JdM2/RtU8TMndPwwN++VEPUu6VrdJLdMr1UtWXiert0HUUNANaOzunwFL+bYRoaY9EWLLgxqtT/I8V+9kQiNCbdwN/DpMvIGZ5RF1Lf7vsFwxNnkfonFxqSbQuhu4OUzyUaFiVtgmROOijnT3Vu7eiugiqHITM7McYCzwf6FFs0JFV7yk3P1zYLOZnR6C1AV6JzKzVsA4ol4HFbP/Wvi7K9F1tynJ5sVEvVEahQfUZN2lpY65+6dEY5wvA7BogsTzgY4x1+cAYIi7f0X0kPibip5DFs3eX0YaNyJ330zU6+GGcG0nUkzUdTrLzA4jSeOi7Dpm1tzMOscE5RFNrlspVC7vB/Yys7OS7OofwFEx459TPbjILlbN9XkvMNrd48vwlPWGBHQO7H5DgNsqynd3bw4cbmYt4yOGBodbiYa+gfK7IVgA7GNmP64IMLOTzeyHyTYI9+IKA4Eqb3CIUUyob1s0yXrH75ZcSSSuvN6HJPW2EH0x0YSoq8PyK0S9Eo4kZqLc6iQoD+LXn0c0aX6V57MUz3P1TspXE2Yqd59hZs2BxWH81KFAJ3f/OEQZZdHMvHsBv3f3pN2M3f1VM7sW+EN4KNxE1JVqDNGY9wWhi02F6cDd4UIAeNLMtgL7APOILgagcnK3XkQXRHWGA4+YWSkwJ434UjNHm9lKIJdoiMuDHk20B1HX55+a2TaiybkKQ3fGKtx9hUUT8KwiejjRPBW71r18O1FON+Bf7v6vmPXFQPtQkfg5cA+wNsxt8jGQnyxv47n7SjNbTfTfr0RDjKYBZxCN3XwT+GuCOLLzHB26SlZ4hKh8HhfuD2VEeV7lbS3u7mb2n0STJ1Upb929zMxGALPM7BPgRaLxubKHiLs+F8WEv0aCymYa9Yb4+DoHdr9Cot6csaaF8L8DBeG+vi/wEdG8UvNB+d0QhHJ6IHCfmd1EVKZvJJrEGqI5Mt6N2WQk0D80GHmIm6r+/XvgUTN7hahO91IdJl9ixJTXF5C63vY3oDXRQzzuvs3MPiJ6oC+v5jBJy4NgpJn9iKg36xrgjLjyIOnzXH1ladZ3M1a4OTxK1GDwo3QfEEQkc1j0qtTngN+5+8TdnR4R2X1Ub8gsym8RyURqRBARERERERGRtGg4Qx0xs+HAtXHBi929uonT6jodvwVOiwu+P6ZrvewBwjjK+QlW9Ugw1lbqkZrmrZmdQNXhDFvd/ZSdkT7ZtcxsGnBUXPBod9ewsgyhcyCzKL8zS5j75tdxwRvcfeDuSI/UnvKyZtQTQURERERERETSorcziIiIiIiIiEha1IggIiIiIiIiImlRI4KIiEg9ZmbbzWyVmb1mZqvN7D/MLOX93cxamdnQnZCW68Lrh0VERKSBUiOCiIhI/bbF3fPcvQPQC+gLjKlmm1ZAnTciEL1jXY0IIiIiDZgaEURERBoId/8IGAFcbZFWZrbIzFaEn1ND1P8CCkIPhpHJ4pnZYWZWHOKtMbOCEH6mmS0JcZ8ys/3M7BqgOfCCmb0QnzYz22hmt4dtXjWzdiG8q5n9zcxWht9tQ/gwM3vazJ4xsw1mdnXoZbHSzJaa2YEh3tFm9pyZLQ+fod3O/p5FREQymd7OICIiUo+ZWYm77xcX9hnQDvgSKHf3MjM7Fpjs7l3MrDtwg7v/e4i/b5J41wO57n6nmWUR9TLYB5gK9HH3r8xsNLCPu99hZhuBLu7+SYJ0bgTudfcHzexKoLO7X25mTYBSd99mZj2Bn7r7eWY2DLgFOBHIBdYRvSrvITMbD/zT3e8zs/nAFe7+lpmdAox19zPq7AsWERGRHWTv7gSIiIhInbPwOweYYGZ5wHagTZL4yeK9DDxiZjnA0+6+ysx+CLQHFpsZwN7AkjTTNTX8Xg6cG/5uCjweGi88pKXCC+7+JfClmW0GngnhrwIdzWw/4FTgqZAWiBo5REREZCdRI4KIiEgDYmatiRoCPiKaG+FDoBPREMayJJuNTBTP3YvNrBvQD5hkZvcAnwFz3X1ILZK3Nfzezrd1kF8RNRYMNLNWwMIE8QHKY5bLw/Z7AZ+7e14t0iIiIiK1oDkRREREGggzOxh4CJjg0XjFpsD77l4OXARkhahfAo1jNk0Yz8xaAh+5+8PA/wKdgaXAaWZ2TIizr5m1SbLfdDQF/hX+HlaTDd39C2CDmZ0f0mJm1qmGxxcREZEaUCOCiIhI/dao4hWPwDzgeeD2sO53wCVmtpRoiMJXIfwVYFt4JeTIFPG6A6vMbCVwHnC/u39M9LA/2cxeIWpUqJjMcCLwbKKJFVO4GxhrZov5tpGjJi4ELjOz1cBrwIBa7ENERETSpIkVRURERERERCQt6okgIiIiIiIiImlRI4KIiIiIiIiIpEWNCCIiIiIiIiKSFjUiiIiIiIiIiEha1IggIiIiIiIiImlRI4KIiIiIiIiIpEWNCCIiIiIiIiKSlv8H1cWsg1sWTZYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_graphs.faking_type_comparison(pimp_summary, prefix = \"PERMIMP\", save = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4fb8dc",
   "metadata": {
    "id": "fb4fb8dc"
   },
   "source": [
    "### Mutual Information (JMIM)\n",
    "\n",
    "Joint Mutual Information Maximization (JMIM)<sup>[1]</sup> is a greedy algorithm used to select the best subset of features to predict a response variable. The way the algorithm works is based on information theory and especially mutual information. Mutual information can be expressed as the amount of information provided by variable $X$, which reduces the uncertainty of the variable, i.e. mutual information (MI) is zero if the random variables are statistically independent<sup>[2]</sup>.\n",
    "MI does not make an assumption of linearity between the variables and can deal both with categorical and numerical data with two or more class values<sup>[3]</sup>. With this algorithm, we especially deal with joint mutual information which is simply the mutual information between a set of variables and another single variable. In this case, the set of variables will represent the already chosen features and the other variable will be the response variable. The way the algorithm proceeds so is the following:\n",
    "\n",
    "1. The first feature is simply chosen by taking the feature which maximizes the MI with the response variable. <br>\n",
    "2. From now on every other feature will be chosen based on the JMIM criteria, so we will choose the feature that maximizes the MI between the set of already chosen features (plus itself) and the response variable. <br>\n",
    "3. We repeat this procedure until the desired number of features is reached. <br>\n",
    "\n",
    "The advantage of this algorithm over all the other MI-based competitors is that for the MI maximization, it takes into account every single possible new feature with the already chosen features, in this way we will avoid choosing redundant features unlike in other methods such as Conditional Mutual Information Maximization (CMIM) where we simply maximize the mutual information between the remaining features and the response variable.\n",
    "\n",
    "In this specific case, the algorithm is run in order to return a subset of features that consists of all of the features of the dataset. This is done in order to have a \"score\" of how much information is added to the model when choosing a specific variable. If we look at the trend across datasets we can see that as we add features to the \"predictors\" set we have a declining level of added information (e.g the features don't add much value and we risk overfitting). \n",
    "So what is practically done is that we run the algorithm until the features selected add a value of at least 0.8 (scores go from 0 to 1) and after that we stop and use the set, up to that point, of selected features as the predictor variables.\n",
    "\n",
    "The code reference is `engine.py`, the function is `mutual_info`. \n",
    "\n",
    "##### SUPPORTING PAPERS\n",
    "[1] M. Bennasar, Y. Hicks and R. Setchi, *Feature selection using Joint Mutual Information Maximisation*, 2015.  \n",
    "[2] A. Kraskov, H. Stogbauer and P. Grassberger, *Estimating mutual information*. Phys. Rev. E 69, 2004.  \n",
    "[3] B. C. Ross *Mutual Information between Discrete and Continuous Data Sets*. PLoS ONE 9(2), 2014.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c63d1bd7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T14:57:26.307669Z",
     "start_time": "2023-01-05T14:52:33.162347Z"
    },
    "colab": {
     "referenced_widgets": [
      "58a5e16c9baf4bfa99efffcce4bd3475",
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      "3bb74c8988594d82bcea3568da039ee1",
      "16e8970bd39840b4abf0be0a7945fff9",
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      "8939a4173d814afeaf7ecb5251c77d64",
      "3d20988943a84588bb7b32b289a39df3",
      "aa4349a4714c456bae632c6157978fcb",
      "99af526df0aa41fdb1d2deeb95c3032a",
      "cba1ae5d7c844a1ab291013709c97b5f",
      "f07d764187e94cd98d2b7a7e2483465b",
      "83cba8b511de4f488120f9a6e9f4e343",
      "99c5453cb6144eff894137edee3a18f3",
      "8f8c9b1f475f4eaf99e5092eda7ab115",
      "f561700a223d484f8321fee54ae235df",
      "9c7bc2e5b15946c4a90a2761d00d319a"
     ]
    },
    "id": "c63d1bd7",
    "outputId": "c51e8d19-cfeb-4acb-bbb4-5cf5c3ca59e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BF_df_CTU\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "58a5e16c9baf4bfa99efffcce4bd3475",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 2ms/step\n",
      "Train size: 309\n",
      "Selected 3 features out of 10\n",
      "[4 7 2]\n",
      "                     Accuracy  FI 1  FI 2  FI 3\n",
      "Full Logit           0.774436   NaN   NaN   NaN\n",
      "Logistic Regression  0.819549   7.0   2.0   4.0\n",
      "SVC                  0.819549   7.0   2.0   4.0\n",
      "Random Forest        0.804511   7.0   4.0   2.0\n",
      "Neural Network       0.842105   7.0   4.0   2.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.778\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "BF_df_OU\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f7335f6d49e4e369746aad16661eb35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 2ms/step\n",
      "Train size: 322\n",
      "Selected 2 features out of 10\n",
      "[0 7]\n",
      "                     Accuracy  FI 1  FI 2\n",
      "Full Logit           0.833333   NaN   NaN\n",
      "Logistic Regression  0.746377   7.0   0.0\n",
      "SVC                  0.753623   7.0   0.0\n",
      "Random Forest        0.768116   7.0   0.0\n",
      "Neural Network       0.760870   7.0   0.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 1.0\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "BF_df_V\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3bb74c8988594d82bcea3568da039ee1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 2ms/step\n",
      "Train size: 340\n",
      "Selected 3 features out of 10\n",
      "[4 0 6]\n",
      "                     Accuracy  FI 1  FI 2  FI 3\n",
      "Full Logit           0.760274   NaN   NaN   NaN\n",
      "Logistic Regression  0.760274   6.0   0.0   4.0\n",
      "SVC                  0.760274   6.0   0.0   4.0\n",
      "Random Forest        0.760274   6.0   4.0   0.0\n",
      "Neural Network       0.760274   4.0   0.0   6.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.556\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "DT_df_CC\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "16e8970bd39840b4abf0be0a7945fff9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 2ms/step\n",
      "Train size: 337\n",
      "Selected 2 features out of 27\n",
      "[10  6]\n",
      "                     Accuracy  FI 1  FI 2\n",
      "Full Logit           0.682759   NaN   NaN\n",
      "Logistic Regression  0.689655  10.0   6.0\n",
      "SVC                  0.689655  10.0   6.0\n",
      "Random Forest        0.689655  10.0   6.0\n",
      "Neural Network       0.689655   6.0  10.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.667\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "DT_df_JI\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e88b0a63276458bafa44cc68531a876",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 2ms/step\n",
      "Train size: 604\n",
      "Selected 2 features out of 27\n",
      "[2 6]\n",
      "                     Accuracy  FI 1  FI 2\n",
      "Full Logit           0.661538   NaN   NaN\n",
      "Logistic Regression  0.642308   2.0   6.0\n",
      "SVC                  0.642308   2.0   6.0\n",
      "Random Forest        0.623077   2.0   6.0\n",
      "Neural Network       0.642308   2.0   6.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 1.0\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "IADQ_df\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8939a4173d814afeaf7ecb5251c77d64",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 2ms/step\n",
      "Train size: 315\n",
      "Selected 6 features out of 9\n",
      "[1 4 8 2 6 3]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.851852   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.844444   3.0   1.0   4.0   8.0   6.0\n",
      "SVC                  0.844444   3.0   1.0   8.0   4.0   2.0\n",
      "Random Forest        0.829630   8.0   4.0   3.0   1.0   6.0\n",
      "Neural Network       0.829630   8.0   2.0   6.0   1.0   4.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.333\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "IESR_df\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d20988943a84588bb7b32b289a39df3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step\n",
      "Train size: 250\n",
      "Selected 11 features out of 22\n",
      "[17  8 18 15 16 19 11 14 13  7  5]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.935185   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.907407   8.0  18.0  19.0  13.0  14.0\n",
      "SVC                  0.888889   8.0  18.0  19.0  13.0  14.0\n",
      "Random Forest        0.898148   8.0  13.0   7.0  19.0  17.0\n",
      "Neural Network       0.925926  14.0  13.0  18.0  19.0   7.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.533\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "NAQ_R_df\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa4349a4714c456bae632c6157978fcb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step\n",
      "Train size: 498\n",
      "Selected 21 features out of 22\n",
      "[ 1 15 12  5 16 10 17 19  8  6 13 21  9  7 18 11  3 14  4  0 20]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.953271   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.971963   9.0  21.0  12.0   8.0   5.0\n",
      "SVC                  0.962617   9.0  21.0   5.0  12.0   3.0\n",
      "Random Forest        0.962617  21.0  15.0  11.0   6.0   3.0\n",
      "Neural Network       0.962617  19.0  12.0  10.0   8.0  13.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.267\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PCL5_df\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99af526df0aa41fdb1d2deeb95c3032a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step\n",
      "Train size: 281\n",
      "Selected 5 features out of 20\n",
      "[18  1 13  2 10]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.809917   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.768595   1.0  13.0   2.0  10.0  18.0\n",
      "SVC                  0.776860   1.0  13.0  10.0   2.0  18.0\n",
      "Random Forest        0.760331  13.0   1.0   2.0  10.0  18.0\n",
      "Neural Network       0.834711   2.0  13.0   1.0  10.0  18.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.6\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PHQ9_GAD7_df\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cba1ae5d7c844a1ab291013709c97b5f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 3ms/step\n",
      "Train size: 782\n",
      "Selected 7 features out of 16\n",
      "[ 1  6  9  0  5  7 12]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.991071   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.982143   7.0   6.0   1.0   9.0   5.0\n",
      "SVC                  0.982143   7.0   1.0   6.0   0.0   9.0\n",
      "Random Forest        0.973214   7.0   1.0  12.0   6.0   0.0\n",
      "Neural Network       0.982143   7.0   6.0   5.0   1.0   9.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.4\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PID5_df\n"
     ]
    },
    {
     "data": {
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       "model_id": "f07d764187e94cd98d2b7a7e2483465b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 0s 2ms/step\n",
      "Train size: 576\n",
      "Selected 13 features out of 220\n",
      "[165 132 191 171 151  57 138   1 189 177 216 135  35]\n",
      "                     Accuracy   FI 1   FI 2   FI 3   FI 4   FI 5\n",
      "Full Logit           0.959677    NaN    NaN    NaN    NaN    NaN\n",
      "Logistic Regression  0.955645    1.0  165.0  191.0  177.0   57.0\n",
      "SVC                  0.955645    1.0  165.0  191.0  177.0  171.0\n",
      "Random Forest        0.947581    1.0  165.0  132.0   57.0  171.0\n",
      "Neural Network       0.947581  165.0  135.0  177.0    1.0   57.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.533\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PRFQ_df\n"
     ]
    },
    {
     "data": {
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       "model_id": "83cba8b511de4f488120f9a6e9f4e343",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 1ms/step\n",
      "Train size: 474\n",
      "Selected 4 features out of 18\n",
      "[6 2 8 7]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4\n",
      "Full Logit           0.936275   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.877451   6.0   7.0   8.0   2.0\n",
      "SVC                  0.877451   6.0   7.0   8.0   2.0\n",
      "Random Forest        0.872549   6.0   2.0   8.0   7.0\n",
      "Neural Network       0.911765   7.0   8.0   6.0   2.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.583\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "PRMQ_df\n"
     ]
    },
    {
     "data": {
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       "model_id": "99c5453cb6144eff894137edee3a18f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 1ms/step\n",
      "Train size: 982\n",
      "Selected 5 features out of 16\n",
      "[ 7 12  4 14 15]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.907583   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.864929  12.0   7.0   4.0  14.0  15.0\n",
      "SVC                  0.862559  12.0   7.0   4.0  14.0  15.0\n",
      "Random Forest        0.864929  12.0   7.0   4.0  14.0  15.0\n",
      "Neural Network       0.879147   7.0  12.0  14.0   4.0  15.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.733\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "RAW_DDDT\n"
     ]
    },
    {
     "data": {
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       "model_id": "8f8c9b1f475f4eaf99e5092eda7ab115",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 2ms/step\n",
      "Train size: 690\n",
      "Selected 3 features out of 12\n",
      "[10  0  7]\n",
      "                     Accuracy  FI 1  FI 2  FI 3\n",
      "Full Logit           0.746622   NaN   NaN   NaN\n",
      "Logistic Regression  0.736486   7.0  10.0   0.0\n",
      "SVC                  0.746622   7.0  10.0   0.0\n",
      "Random Forest        0.756757  10.0   7.0   0.0\n",
      "Neural Network       0.739865  10.0   0.0   7.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.556\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "R_NEO_PI\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f561700a223d484f8321fee54ae235df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "729/729 [==============================] - 1s 2ms/step\n",
      "Train size: 54380\n",
      "Selected 6 features out of 30\n",
      "[13 29 22  5 21 14]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.906938   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.902433  13.0  29.0  21.0   5.0  22.0\n",
      "SVC                  0.900888  13.0  21.0  29.0   5.0  22.0\n",
      "Random Forest        0.906294  29.0  13.0   5.0  21.0  22.0\n",
      "Neural Network       0.904964  29.0  13.0  22.0   5.0  21.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.467\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "sPID-5_df\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c7bc2e5b15946c4a90a2761d00d319a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 0s 1ms/step\n",
      "Train size: 726\n",
      "Selected 10 features out of 25\n",
      "[14 20 11  1 22  4 23 16  6  2]\n",
      "                     Accuracy  FI 1  FI 2  FI 3  FI 4  FI 5\n",
      "Full Logit           0.961538   NaN   NaN   NaN   NaN   NaN\n",
      "Logistic Regression  0.967949  23.0  14.0   4.0  11.0  16.0\n",
      "SVC                  0.971154  23.0  14.0  16.0   4.0  11.0\n",
      "Random Forest        0.964744  23.0  20.0   4.0  16.0  11.0\n",
      "Neural Network       0.971154  23.0  20.0  16.0  14.0  11.0\n",
      "\n",
      "==> TOP5 STABILITY SCORE: 0.6\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mis_datasets = {}\n",
    "\n",
    "for dataset in [f for f in os.listdir(\".\\\\data\") if \"parquet\" in f]:\n",
    "    print(dataset.split(\".\")[0])\n",
    "    X_tr, X_ts, y_tr, y_ts = Classification().prepare_data(f\".\\\\data\\\\{dataset}\", \"CONDITION\")\n",
    "    \n",
    "    var_sel = Classification().mutual_info(X_tr, y_tr)\n",
    "    perfs, stability = Classification().benchmark_models(X_tr, X_ts, y_tr, y_ts, var_sel)\n",
    "    \n",
    "    mis_datasets[dataset.split(\".\")[0]] = [X_tr, X_ts, y_tr, y_ts, var_sel, perfs, stability]\n",
    "    print(var_sel)\n",
    "    print(perfs)\n",
    "    print()\n",
    "    print(\"==> TOP5 STABILITY SCORE: {}\".format(stability))\n",
    "    print(\"-\" * 100)\n",
    "    print(\"-\" * 100)\n",
    "    print(\"-\" * 100)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b6baff",
   "metadata": {
    "id": "36b6baff"
   },
   "source": [
    "#### Summarize results\n",
    "\n",
    "Whit this approach we see that `32%` of features were kept on average so a slightly less conservative selection method, the accuracy dip was instead around `-0.013` and the std on accuracies on benchmark models was about `0.009`.\n",
    "Instead in this case despite the fact that the faking good datasets were the ones where features were cut the most (`0.22%` features kept vs `0.43%` of faking bad) they were still the ones that had the lowest dip in performance (`-0.011` vs `-0.014` of faking bad) and they were even the most stable in terms of std of accuracy on benchmarking models (`0.007` vs `0.011` faking bad). Examining instead the single accuracies on the 4 models that use only the selected features the analysis reports that the best-performing model is the multi-layer perceptron with an average accuracy of `0.849`, followed by the logistic regression and SVM (tied with `0.840`) and last the Random Forest with an average accuracy of `0.837`. This may suggest that with fewer features connectionist models might be the best way to model the data.\n",
    "Another surprising trend that we can observe looking at these 4 models is that in this case (if we look at the model by themselves without comparing them to the full logistic regression) the faking bad datasets have the best accuracy on average in all 4 cases (`0.878875`, `0.877625`, `0.87425`, `0.887875` vs `0.800625`, `0.801875`, `0.79875`, `0.810375` of faking good)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "502b6593",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T14:57:26.880730Z",
     "start_time": "2023-01-05T14:57:26.311380Z"
    },
    "id": "502b6593",
    "outputId": "08d4b722-96a1-49c0-b197-62beb4aa71e3"
   },
   "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>Dataset name</th>\n",
       "      <th>Sample size</th>\n",
       "      <th>Training size</th>\n",
       "      <th>Number of Features</th>\n",
       "      <th>Selected Features</th>\n",
       "      <th>Feat. Top5-Stability</th>\n",
       "      <th>Accuracy - Logit all features</th>\n",
       "      <th>Accuracy - Logit</th>\n",
       "      <th>Accuracy - SVM</th>\n",
       "      <th>Accuracy - RF</th>\n",
       "      <th>Accuracy - MLP</th>\n",
       "      <th>Avg Acc. on selected features</th>\n",
       "      <th>Accuracy Std on selected features</th>\n",
       "      <th>Acc. diff. wrt Full logit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BF_df_CTU</td>\n",
       "      <td>442</td>\n",
       "      <td>309</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.842</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BF_df_OU</td>\n",
       "      <td>460</td>\n",
       "      <td>322</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-0.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BF_df_V</td>\n",
       "      <td>486</td>\n",
       "      <td>340</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT_df_CC</td>\n",
       "      <td>482</td>\n",
       "      <td>337</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DT_df_JI</td>\n",
       "      <td>864</td>\n",
       "      <td>604</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.623</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.010</td>\n",
       "      <td>-0.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IADQ_df</td>\n",
       "      <td>450</td>\n",
       "      <td>315</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.837</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IESR_df</td>\n",
       "      <td>358</td>\n",
       "      <td>250</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.016</td>\n",
       "      <td>-0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NAQ_R_df</td>\n",
       "      <td>712</td>\n",
       "      <td>498</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PCL5_df</td>\n",
       "      <td>402</td>\n",
       "      <td>281</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.034</td>\n",
       "      <td>-0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PHQ9_GAD7_df</td>\n",
       "      <td>1118</td>\n",
       "      <td>782</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PID5_df</td>\n",
       "      <td>824</td>\n",
       "      <td>576</td>\n",
       "      <td>220</td>\n",
       "      <td>13</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PRFQ_df</td>\n",
       "      <td>678</td>\n",
       "      <td>474</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.018</td>\n",
       "      <td>-0.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PRMQ_df</td>\n",
       "      <td>1404</td>\n",
       "      <td>982</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.008</td>\n",
       "      <td>-0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RAW_DDDT</td>\n",
       "      <td>986</td>\n",
       "      <td>690</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.736</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>R_NEO_PI</td>\n",
       "      <td>77687</td>\n",
       "      <td>54380</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>0.467</td>\n",
       "      <td>0.907</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sPID-5_df</td>\n",
       "      <td>1038</td>\n",
       "      <td>726</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.971</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.971</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Dataset name  Sample size  Training size  Number of Features  \\\n",
       "0      BF_df_CTU          442            309                  10   \n",
       "1       BF_df_OU          460            322                  10   \n",
       "2        BF_df_V          486            340                  10   \n",
       "3       DT_df_CC          482            337                  27   \n",
       "4       DT_df_JI          864            604                  27   \n",
       "5        IADQ_df          450            315                   9   \n",
       "6        IESR_df          358            250                  22   \n",
       "7       NAQ_R_df          712            498                  22   \n",
       "8        PCL5_df          402            281                  20   \n",
       "9   PHQ9_GAD7_df         1118            782                  16   \n",
       "10       PID5_df          824            576                 220   \n",
       "11       PRFQ_df          678            474                  18   \n",
       "12       PRMQ_df         1404            982                  16   \n",
       "13      RAW_DDDT          986            690                  12   \n",
       "14      R_NEO_PI        77687          54380                  30   \n",
       "15     sPID-5_df         1038            726                  25   \n",
       "\n",
       "    Selected Features  Feat. Top5-Stability  Accuracy - Logit all features  \\\n",
       "0                   3                 0.778                          0.774   \n",
       "1                   2                 1.000                          0.833   \n",
       "2                   3                 0.556                          0.760   \n",
       "3                   2                 0.667                          0.683   \n",
       "4                   2                 1.000                          0.662   \n",
       "5                   6                 0.333                          0.852   \n",
       "6                  11                 0.533                          0.935   \n",
       "7                  21                 0.267                          0.953   \n",
       "8                   5                 0.600                          0.810   \n",
       "9                   7                 0.400                          0.991   \n",
       "10                 13                 0.533                          0.960   \n",
       "11                  4                 0.583                          0.936   \n",
       "12                  5                 0.733                          0.908   \n",
       "13                  3                 0.556                          0.747   \n",
       "14                  6                 0.467                          0.907   \n",
       "15                 10                 0.600                          0.962   \n",
       "\n",
       "    Accuracy - Logit  Accuracy - SVM  Accuracy - RF  Accuracy - MLP  \\\n",
       "0              0.820           0.820          0.805           0.842   \n",
       "1              0.746           0.754          0.768           0.761   \n",
       "2              0.760           0.760          0.760           0.760   \n",
       "3              0.690           0.690          0.690           0.690   \n",
       "4              0.642           0.642          0.623           0.642   \n",
       "5              0.844           0.844          0.830           0.830   \n",
       "6              0.907           0.889          0.898           0.926   \n",
       "7              0.972           0.963          0.963           0.963   \n",
       "8              0.769           0.777          0.760           0.835   \n",
       "9              0.982           0.982          0.973           0.982   \n",
       "10             0.956           0.956          0.948           0.948   \n",
       "11             0.877           0.877          0.873           0.912   \n",
       "12             0.865           0.863          0.865           0.879   \n",
       "13             0.736           0.747          0.757           0.740   \n",
       "14             0.902           0.901          0.906           0.905   \n",
       "15             0.968           0.971          0.965           0.971   \n",
       "\n",
       "    Avg Acc. on selected features  Accuracy Std on selected features  \\\n",
       "0                           0.821                              0.016   \n",
       "1                           0.757                              0.009   \n",
       "2                           0.760                              0.000   \n",
       "3                           0.690                              0.000   \n",
       "4                           0.638                              0.010   \n",
       "5                           0.837                              0.009   \n",
       "6                           0.905                              0.016   \n",
       "7                           0.965                              0.005   \n",
       "8                           0.785                              0.034   \n",
       "9                           0.980                              0.004   \n",
       "10                          0.952                              0.005   \n",
       "11                          0.885                              0.018   \n",
       "12                          0.868                              0.008   \n",
       "13                          0.745                              0.009   \n",
       "14                          0.904                              0.002   \n",
       "15                          0.969                              0.003   \n",
       "\n",
       "    Acc. diff. wrt Full logit  \n",
       "0                       0.047  \n",
       "1                      -0.076  \n",
       "2                       0.000  \n",
       "3                       0.007  \n",
       "4                      -0.024  \n",
       "5                      -0.015  \n",
       "6                      -0.030  \n",
       "7                       0.012  \n",
       "8                      -0.025  \n",
       "9                      -0.011  \n",
       "10                     -0.008  \n",
       "11                     -0.051  \n",
       "12                     -0.040  \n",
       "13                     -0.002  \n",
       "14                     -0.003  \n",
       "15                      0.007  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mis_summary = evaluation.summary_table(mis_datasets, stability = True, neural_net = True)\n",
    "mis_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "315f1b15",
   "metadata": {},
   "source": [
    "The fact that this method keeps the accuracy on all datasets pretty high can be seen better in this plot of the average accuracy of the 4 models on selected features. We go from a minimum of around `0.63` in the worst-case scenario to `0.98` in the best. With an average accuracy of `0.84` and a standard deviation that never exceeds `0.034`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f6fccdc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T14:57:28.182182Z",
     "start_time": "2023-01-05T14:57:26.882606Z"
    },
    "id": "3f6fccdc",
    "outputId": "4c477556-506e-430a-c3ea-df911097ef4f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_graphs.accuracy_barplot(mis_summary, save = True, prefix = \"MIS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e117c5f",
   "metadata": {},
   "source": [
    "Standard deviations by themselves are highlighted again by this plot. With the maximum of 0.034 being the only value surpassing the 0.02 mark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3dc6519",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T14:57:29.886468Z",
     "start_time": "2023-01-05T14:57:28.187616Z"
    },
    "id": "f3dc6519",
    "outputId": "2dd0c020-8014-4597-f266-e2905f25c6ec"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_graphs.accuracy_std(mis_summary, save = True, prefix = \"MIS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f24cd77",
   "metadata": {},
   "source": [
    "As previously said, in this plot we can appreciate the difference between faking good and bad datasets. With the latter outperforming on average the former in accuracy terms. Also to be noted, the difference between a full and reduced model in many cases is minimal if not zero.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41ef01d8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-05T14:57:32.549980Z",
     "start_time": "2023-01-05T14:57:29.888468Z"
    },
    "id": "41ef01d8",
    "outputId": "108a4040-b4af-4553-c67a-d5c71c795aa4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_graphs.faking_type_comparison(mis_summary, prefix = \"MIS\", save = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c23eb6df",
   "metadata": {
    "id": "c23eb6df"
   },
   "source": [
    "## *Comparison between approaches*\n",
    "From the plot on the first panel, that of the average accuracy, it is clear how the variable selection performed by the PCA-based approach is almost always equal or lower compared with the other two approaches proposed.  \n",
    "This behavior is obviously reflected also in the second panel of the plot, where the aggregate (i.e. average of the models used as a benchmark) is shown. Also, from this latter graph, we can see how in some cases, reducing the dimensionality of the problem actually brings an increase in the accuracy - this is likely due to collinearity between different items in the questionnaire. Moreover, save for some extreme cases in which the aggregate accuracy with the subset of features decreases by almost 8 percentage points, the drop in the accuracy with respect to a model fitted on all the variables is fairly low (-2% on average across all approaches at trial), which may suggest that in terms of lie detection most of the items of the questionnaires at hand are of little or no use, given that, on overall average, the number of selected features is roughly 26%.  \n",
    "In the third panel, we see that in general models tend to be fairly stable in terms of accuracy and that the PCA-based approach is the one that provides a subset of variables that is less consistent in terms of resulting accuracy across different models, while on the other hand, Permutation Importance and Mutual Information show similar behavior.\n",
    "Finally, from the last panel, the results on the tailor-made metric of stability to address the problem of how the importance of each of the selected features changes based on the model used for benchmarking, are shown. It is evident that the PCA-based approach is outperforming both the other methods, which may suggest that the inherent nature of the principal components yields a latent \"structure\" (set of variable) that preserve the importance of the data itself, even if those variables are not \"informative\" per se, thus causing high variability across benchmarking models' accuracies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21de7e0b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-03T13:57:22.289149Z",
     "start_time": "2023-01-03T13:57:18.397403Z"
    },
    "id": "21de7e0b",
    "outputId": "ff271402-d059-4e0c-be62-0615496291b7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1296 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_graphs.approaches_comparison(pca_summary, pimp_summary, mis_summary, save = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57ba7c05",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-06T00:36:17.378459Z",
     "start_time": "2023-01-06T00:36:16.967064Z"
    },
    "id": "57ba7c05",
    "outputId": "56ad2ddd-6886-4a99-e0c9-940c0d3c5de6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc. diff. wrt Full logit\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "      <th>JMIM</th>\n",
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       "      <td>-0.0183</td>\n",
       "      <td>-0.0132</td>\n",
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       "         PCA  PERM. IMP.    JMIM\n",
       "mean -0.0344     -0.0183 -0.0132\n",
       "min  -0.0890     -0.0750 -0.0760\n",
       "max   0.0270      0.0260  0.0470"
      ]
     },
     "execution_count": 56,
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     "output_type": "execute_result"
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    "summary_graphs.final_table(\"Acc. diff. wrt Full logit\", pca_summary, pimp_summary, mis_summary)"
   ]
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     "text": [
      "Accuracy Std on selected features\n"
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       "         PCA  PERM. IMP.    JMIM\n",
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     "text": [
      "Feat. Top5-Stability\n"
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       "         PCA  PERM. IMP.    JMIM\n",
       "mean  0.7962      0.6965  0.6004\n",
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    "## *Conclusions*\n",
    "\n",
    "### Perfomance assessment\n",
    "During the study, we have taken into consideration three model-agnostic methods and evaluated them via four benchmarking models, based on three main metrics. This is because each of the metrics is supposed to assess a different aspect of the solution to the problem at hand: in fact, a variable selection procedure can be evaluated in terms of how much it reduces the accuracy of prediction (*Acc. diff. wrt Full logit*), but we also take into account the model-agnostic part, i.e. how stable is a selection procedure when its output (a subset of features) is used to train models that are completely different from each other (*Accuracy Std on selected features*) and how the importance of the feature in this subset change importance accordingly (*Top-5 stability*).<br>\n",
    "\n",
    "What is interesting is that, if we consider the average performance over the sixteen datasets within the scope of the project, the Mutual information-based approach slightly outperforms the other methods, which thus results in the most suitable approach if the ultimate goal is to select features in order to save computational time without losing accuracy of prediction, i.e. *Acc. diff. wrt Full logit* - this is true also because the strength of selection is slightly lower than Permutation Importance.  \n",
    "Instead, when the goal of the feature selection is to have a common baseline subset of features to preserve accuracy across different classification algorithms, Permutation Importance-based approach is the one that seems more suitable - i.e. it minimizes the *Accuracy Std on selected features*.  \n",
    "Finally, the PCA-based approach outperforms all the other approaches found when it comes to the importance ranking of the selected features across all the models (*Top-5 stability*) - this is inherently coming from the definition of principal components, and this study provides additional proof.\n",
    "\n",
    "### Interpretation of results\n",
    "The datasets used in this project are psychological questionnaires submitted to different individuals, which compiled them twice, one time lying and the other answering truthfully. Each dataset entails a specific questionnaire used in a specific context, but, in order to address whether some similarities in how the methods presented selected the features, we divided some of them into groups based on the context for which they were applied:\n",
    "* Group 1 (job interviews): BF_CTU, BF_OU, DT_JI\n",
    "* Group 2 (identification of diseases/syndroms): PCL5, IESR, IADQ, PHQ9_GAD7\n",
    "* Group 3 (identification of different mental disorders): PID, sPID\n",
    "* Group 4 (child custody): DT_CC, BF_V\n",
    "* Group 5 (personality traits): R_NEO_PI, RAW_DDDT\n",
    "* Treated singularly: NAQR, PRMQ, PRFQ\n",
    "\n",
    "First, we qualitatively assessed if there are and which are the variables that have been chosen by all the three procedures used (PCA, PIMP, JMIM). Then we focus on the variables selected by the PCA, as it is the best-performing model in terms of the stability in the importance of the features within the models used for benchmarking.\n",
    "\n",
    "#### Varibles commonly selected by the different approaches (see Appendix for reference)\n",
    "\n",
    "**Group 1**: they are all datasets of faking good, thus (apart from reversed items) we expect to have DISHONESTS responding with higher values. What is interesting is how crucial for lie detection the items related to stress management (both for BF_CTU and BF_OU - items 6 & 7) are, regardless of the context for which the text is taken. The trait addressed by DT_JI are not within the scope of the BF datasets, but we see how also “repressing” opportunism (like item 1 and item 2) and risk-aversion (item 15) are as well good predictors for liars.  \n",
    "\n",
    "**Group 2**: The datasets in this group are faking bad and their goal is to identify possible victims of mental disorders derived from some experience. In choosing the variables for these tests, we can see some similarities in the questions that have been selected. In particular, \"IADQ\" and \"IESR\" questions often refer to the memory of the traumatic event and if the mind often thinks about it. Interestingly, some questions of \"IESR\" (item 18), \"PCL5\" (item 5), and \"PHQ9_GAD7\" (item 8) are very similar and related to possible physical reactions and movement behaviors; another pattern is the selction of questions that explicitly mention negative feellings (PCL5 item 11 and PHQ9_GAD7 item 2).  \n",
    "\n",
    "**Group 3**: The datasets \"PID5\" and \"sPID5\" are faking bad datasets, where faking is associated with pretending to have a mental disorder. The aim of these questionnaires is to identify more than one mental disorder, thus each question is associated with a particular disorder. Keep in mind that sPID5 is a short version of the PID5 questionnaire, thus questions are very similar. In choosing the variables for these tests, we can see similarities in the questions that have been selected. In fact, there are four main characteristics that are present in the selected features, both for sPID5 and PID5: concern, emotionality, feeling abnormal, and difficulty in distinguishing between reality and dream. Another interesting thing regards the association between selected features and the corresponding mental disorder: for both datasets, it can be seen that for almost all the mental disorders at least one feature has been selected.  \n",
    "\n",
    "**Group 4**: these two datasets are associated with faking good in the context of obtaining child custody. The variables selected from the two datasets do not have a lot in common. However, we can infer some observations. Regarding the DT_CC datasets, it seems like features that represent manipulation (items 1 and 4) and features that represent the feeling of being special (item 21) are favored. Instead, for the BF_V dataset, features quite different are chosen, the only one that is common between two approaches is regarding nervousness (item 7).  \n",
    "\n",
    "**Group 5**: When assessing the personality trait, the variables selected that seem common in lie detection are related to exaggerating kindness (item 5), self-discipline (item 13), emotional stability (item 21), and imagination (item 29). From the DDDT test instead, the lie detection pivots upon \"hiding\" insensitivity (item 7) and narcissism (item 10). Finally, the most important features are the ones regarding the inner self and solitude in the R_NEO_PI dataset.  \n",
    "\n",
    "**Treated singularly**\n",
    "- NAQ_R: In the workplace, for all of these items, obviously the pattern is the same, that is faking to be subjected to a given type of behavior from the coworkers. In particular, \"pretending\" to be exposed to intimidating behaviors (item 9) seems to be the most explanatory sign of lying.\n",
    "- PRFQ: The features that seem to influence the most the mentalization with children are the ones related to thinking about the child’s behavior (items 2, 8) and the ones regarding knowing the child very well (items 7, 13).\n",
    "- PRMQ: When evaluating memory traits in a faking bad scenario the things that are the most evidently lied about are the most obvious ones that clearly indicate bad memory symptoms.\n",
    "\n",
    "#### Variables selected by PCA\n",
    "The five most important features (according to Top5-staility) are listed below.\n",
    "<ul>\n",
    "    <li>BF_CTU\n",
    "        <ul>\n",
    "            <li>[ES1G] Is relaxed, handles stress well (emotional stability).\n",
    "                [O1G] Has few artistic interests (openness, reversed).</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>BF_OU\n",
    "        <ul>\n",
    "            <li>[EX2G] Is outgoing, sociable (extroversion).\n",
    "                [ES1G] Is relaxed, handles stress well (emotional stability).</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>BF_V\n",
    "        <ul>\n",
    "            <li>[EX2G] Is outgoing, sociable (extroversion).\n",
    "                [ES2G] Gets nervous easily (emotional stability, reversed).</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>DT_CC\n",
    "        <ul>\n",
    "            <li>[Mach2] I like to use clever manipulation to get my way. \n",
    "                [Mach5] It's wise to keep track of information that you can use against people later. \n",
    "                [Psycho2] I avoid dangerous situations. \n",
    "                [Narc2] I hate being the center of attention. \n",
    "                [Narc4] I know that I am special because everyone keeps telling me so. \n",
    "                [Narc6] I feel embarrassed if someone compliments me. </li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>DT_JI\n",
    "        <ul>\n",
    "            <li>[Mach1] It’s not wise to tell your secrets. \n",
    "                [Mach2] I like to use clever manipulation to get my way. \n",
    "                [Mach3] Whatever it takes, you must get the important people on your side. \n",
    "                [Psycho4] People often say I’m out of control. \n",
    "                [Psycho7] I have never gotten into trouble with the law. \n",
    "                [Narc2] I hate being the center of attention.</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>IADQ\n",
    "        <ul>\n",
    "            <li>[1] I cannot stop thinking about the stressful event(s) (preoccupation).\n",
    "                [7] Affected your relationships or social life? (evidence of functional impairment).</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>IESR\n",
    "        <ul>\n",
    "            <li>\n",
    "                [IESR5] I avoided letting myself get upset when I thought about it or was reminded of it. \n",
    "                [IESR9] Pictures about it popped into my mind. \n",
    "                [IESR14] I found myself acting or feeling like I was back at that time. \n",
    "                [IESR16] I had waves of strong feelings about it. \n",
    "                [IESR19] Reminders of it caused me to have physical reactions, such as sweating, trouble breathing, nausea, or a pounding heart.</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>NAQ_R\n",
    "        <ul>\n",
    "            <li>[PI_9] Being shouted at or being target of spontaneous anger. \n",
    "                [WR_10] Intimidating behaviors such as finger-pointing, invasion of personal space, shoving, blocking your way. \n",
    "                [WR_13] Being ignored or facing a hostile reactionwhen you approach. \n",
    "                [PI_15] Having your opinion ingored. \n",
    "                [PR_20] Pressure not to claim something to which by right you are entitled (e.g. sick leave, holiday).</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>PCL5\n",
    "        <ul>\n",
    "            <li>[item 2] Repeated, disturbing dreams of the stressful experience? \n",
    "                [item 5] Having strong physical reactions when something reminded you of the stressful experience (for example, heart pounding, trouble breathing, sweating)? \n",
    "                [item 11] Having strong negative feelings such as fear, horror, anger, guilt, or shame? \n",
    "                [item 13] Feeling distant or cut off from other people?\n",
    "            </li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>PHQ9_GAD7\n",
    "        <ul>\n",
    "            <li>[PHQ2] Feeling down, depressed, or hopeless. \n",
    "                [PHQ8] Moving or speaking so slowly that other people could have noticed? Or the opposite - being so fidgety or restless that you have been moving around a lot more than usual. \n",
    "                [GAD5] Being so restless that it is hard to sit still.</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>PID5\n",
    "        <ul>\n",
    "            <li>[item 2] A lot of people are mad at me. \n",
    "                [item 5] I often have ideas that are too unusual to explain to anyone. \n",
    "                [item 36] I may have difficulty in pointing out the difference between dreams and real life. \n",
    "                [item 166] If I hurt other people's feelings, it is nothing exceptional. \n",
    "                [item 172] I have been told more than once that I have several strange quirks or habits.</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>PRFQ\n",
    "        <ul>\n",
    "            <li>[PreM4] My child cries around strangers to embarrass me. \n",
    "                [C2] Try to understand reasons why my child misbehaves. \n",
    "                [IC2] I can always predict what my child will do. \n",
    "                [IC3] I always know why i do what i do to my child.</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>PRMQ\n",
    "        <ul>\n",
    "            <li>[P1] Do you decide to do something in a few minutes time and then forget to do it. \n",
    "                [N2] do you fail to recognize a place you have visited before. \n",
    "                [N8] do you fail to recall things that have happened to you in the last few days?</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>RAW_DDDT\n",
    "        <ul>\n",
    "            <li>[P1] I tend to manipulate others to get my way.\n",
    "                [M6] I tend to be unconcerned with the morality of my actions.\n",
    "                [M9] I tend to want others to admire me.</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>R_NEO_PI\n",
    "        <ul>\n",
    "            <li>[PI_9] Being shouted at or being target of spontaneous anger.\n",
    "                [WR_10] Intimidating behaviors such as finger-pointing, invasion of personal space, shoving, blocking your way.\n",
    "                [WR_13] Being ignored or facing a hostile reactionwhen you approach.\n",
    "                [PI_15] Having your opinion ingored.\n",
    "                [PR_20] Pressure not to claim something to which by right you are entitled (e.g. sick leave, holiday)</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li>sPID-5\n",
    "        <ul>\n",
    "            <li>[item 5]: Others see me as irresponsible.\n",
    "                [item 8]: I worry about almost everything. \n",
    "                [item 9]: I get emotional easily, often for little reason. \n",
    "                [item 21]: I often have thoughts that make sense to me, but that other people say are strange. \n",
    "                [item 24]: Often things around me seem unreal, or more real than usual.</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "</ul>"
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    "### Appendix\n",
    "##### Items of reference\n",
    "BF_OU[1]: Is outgoing, sociable (extroversion).  \n",
    "BF_CTU[6]: Is relaxed, handles stress well (emotional stability).  \n",
    "BF_OU[7]: Gets nervous easily (emotional stability, reversed).  \n",
    "BF_CTU[8]: Has few artistic interests (openness, reversed).  \n",
    "\n",
    "IADQ[1]: I cannot stop thinking about the stressful event(s) (preoccupation).  \n",
    "IADQ[7]: Affected your relationships or social life? (evidence of functional impairment).  \n",
    "\n",
    "IESR[4]: I avoided letting myself get upset when I thought about it or was reminded of it.  \n",
    "IESR[8]: Pictures about it popped into my mind.  \n",
    "IESR[13]: I found myself acting or feeling like I was back at that time.  \n",
    "IESR[15]: I had waves of strong feelings about it.  \n",
    "IESR[18]: Reminders of it caused me to have physical reactions, such as sweating, trouble breathing, nausea, or a pounding heart.  \n",
    "\n",
    "PCL5[1]: Repeated, disturbing dreams of the stressful experience?  \n",
    "PCL5[4]: Having strong physical reactions when something reminded you of the stressful experience (for example, heart pounding, trouble breathing, sweating)?  \n",
    "PCL5[10]: Having strong negative feelings such as fear, horror, anger, guilt, or shame?  \n",
    "PCL5[12]: Feeling distant or cut off from other people?  \n",
    "\n",
    "PHQ9_GAD7[1]: Feeling down, depressed or hopeless.  \n",
    "PHQ9_GAD7[7]: Moving or speaking so slowly that order people could have noticed? Or the opposite - being so fidgety or restless that you have been moving around a lot more than usual.  \n",
    "PHQ9_GAD7[13]: Being so restless that it is hard to sit still.  \n",
    "\n",
    "PID5[1]: A lot of people are mad at me.  \n",
    "PID5[4]: I often have ideas that are too unusual to explain to anyone.  \n",
    "PID5[35]: I may have difficulty in pointing out the difference between dreams and real life.  \n",
    "PID5[165]: If I hurt other people's feelings, it is nothing exceptional.  \n",
    "PID5[171]: I have been told more than once that I have several strange quirks or habits.  \n",
    "\n",
    "sPID5[4]: Others see me as irresponsible.  \n",
    "sPID5[7]: I worry about almost everything.  \n",
    "sPID5[8]: I get emotional easily, often for little reason.  \n",
    "sPID5[20]: I often have thoughts that make sense to me, but that other people say are strange.  \n",
    "sPID5[23]: Often things around me seem unreal, or more real than usual.  \n",
    "\n",
    "DT_JI[0] (mach1): It’s not wise to tell your secrets.  \n",
    "DT_JI[1] (mach2): I like to use clever manipulation to get my way.  \n",
    "DT_JI[2] (mach3): Whatever it takes, you must get the important people on your side.  \n",
    "DT_CC[4] (mach5): It's wise to keep track of information that you can use against people later.  \n",
    "DT_CC[10] (psycho2): I avoid dangerous situations. (R)  \n",
    "DT_JI[12] (psycho4): People often say I’m out of control.  \n",
    "DT_JI[15] (psycho7): I have never gotten into trouble with the law.  \n",
    "DT_JI[19] (narc2): I hate being the center of attention.  \n",
    "DT_JI[21] (narc4): I know that I am special because everyone keeps telling me so.  \n",
    "DT_JI[23] (narc6): I feel embarrassed if someone compliments me.  \n",
    "\n",
    "R_NEO_PI[1]: non mi piace la musica alta (ESTROVERSIONE Ricerca di stimoli).  \n",
    "R_NEO_PI[5]: mantengo le distanze con gli altri (ESTROVERSIONE Cordialità).  \n",
    "R_NEO_PI[13]: Spreco il mio tempo (COSCIENZIOSITA' Autodisciplina).  \n",
    "R_NEO_PI[21]: Sto bene con me stesso (EQUILIBRIO EMOTIVO Stabilità).  \n",
    "R_NEO_PI[22]: Raramente mi lamento (EQUILIBRIO EMOTIVO Freddezza).  \n",
    "R_NEO_PI[29]: ho difficoltà a immaginarmi le cose (APERTURA MENTALE Immaginazione).  \n",
    "\n",
    "RAW_DDDT[0]: I tend manipulate other people to obtain what I want.  \n",
    "RAW_DDDT[5]: I tend not to care about the morality of my actions.  \n",
    "RAW_DDDT[7]: I tend to be clouds or insensitive.  \n",
    "RAW_DDDT[8]: I tend to want others to admire me.  \n",
    "RAW_DDDT[10]: i tend to want others to pay attention to me.  \n",
    "\n",
    "PRMQ[0]: do you decide to do something in a few minutes time and then forget to do it.  \n",
    "PRMQ[1]: do you fail to recognize a place you have visited before.  \n",
    "PRMQ[7]: do you fail to recall things that have happened to you in the last few days?.  \n",
    "\n",
    "PRFQ[3]: My child cries around strangers to embarrass me.  \n",
    "PRFQ[7]: Try to understand reasons why my child misbehaves.  \n",
    "PRFQ[13]: I can always predict what my child will do.  \n",
    "PRFQ[14]: I always know why i do what i do to my child.  \n",
    "\n",
    "\n",
    "NAQ_R[8]: Being shouted at or being target of spontaneous anger.  \n",
    "NAQ_R[9]: Intimidating behaviors such as finger-pointing, invasion of personal space, shoving, blocking your way.  \n",
    "NAQ_R[12]: Being ignored or facing a hostile reactionwhen you approach.  \n",
    "NAQ_R[14]: Having your opinion ingored.  \n",
    "NAQ_R[19]: Pressure not to claim something to which by right you are entitled (e.g. sick leave, holiday, etc).  "
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