notebooks/plot_morf_clf_comparisons.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "658a52fd",
   "metadata": {},
   "source": [
    "# Plotting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d9b3f53b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext lab_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "155adae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "import json\n",
    "import os\n",
    "from itertools import product\n",
    "from pathlib import Path\n",
    "import sys\n",
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "# warnings.simplefilter(\"ignore\")\n",
    "import numpy as np\n",
    "from mne_bids.path import get_entities_from_fname\n",
    "from natsort import natsorted\n",
    "from rerf.rerfClassifier import rerfClassifier\n",
    "\n",
    "# comparative classifiers\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "\n",
    "from sklearn.calibration import calibration_curve\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import brier_score_loss\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.utils import resample\n",
    "from sklearn.metrics import (\n",
    "    average_precision_score,\n",
    "    roc_auc_score,\n",
    "    f1_score,\n",
    "    roc_curve,\n",
    "    balanced_accuracy_score,\n",
    "    accuracy_score,\n",
    "    auc,\n",
    "    brier_score_loss,\n",
    "    plot_precision_recall_curve,\n",
    "    average_precision_score,\n",
    "    precision_recall_curve,\n",
    ")\n",
    "from sklearn.model_selection import GroupKFold, cross_validate\n",
    "from sklearn.utils import resample\n",
    "from sklearn.calibration import calibration_curve\n",
    "\n",
    "import dabest\n",
    "import scipy.stats\n",
    "from scipy.stats import ranksums, wilcoxon\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sys.path.append(\"../\")\n",
    "# functions related to the feature comparison experiment\n",
    "from sample_code.io import read_participants_tsv\n",
    "from sample_code.utils import _load_turbo, _plot_roc_curve\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d05f904",
   "metadata": {},
   "outputs": [],
   "source": [
    "def average_roc(fpr, tpr):\n",
    "    \"\"\"Compute average ROC statistics.\"\"\"\n",
    "    tprs = []\n",
    "    aucs = []\n",
    "    mean_fpr = np.linspace(0, 1, 200)\n",
    "\n",
    "    n_splits = len(fpr)\n",
    "    print(f\"Computing average ROC over {n_splits} CV splits\")\n",
    "    for i in range(n_splits):\n",
    "        interp_tpr = np.interp(mean_fpr, fpr[i], tpr[i])\n",
    "        interp_tpr[0] = 0.0\n",
    "        tprs.append(interp_tpr)\n",
    "        aucs.append(auc(mean_fpr, interp_tpr))\n",
    "\n",
    "    mean_tpr = np.mean(tprs, axis=0)\n",
    "    mean_tpr[-1] = 1.0\n",
    "    mean_auc = auc(mean_fpr, mean_tpr)\n",
    "    std_auc = np.std(aucs)\n",
    "\n",
    "    std_tpr = np.std(tprs, axis=0)\n",
    "    return mean_fpr, tprs, aucs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3c492eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def combine_patient_predictions(\n",
    "    ytrues, ypred_probs, subject_groups, pat_predictions=None, pat_true=None\n",
    "):\n",
    "    if pat_predictions is None or pat_true is None:\n",
    "        pat_predictions = collections.defaultdict(list)\n",
    "        pat_true = dict()\n",
    "\n",
    "    # loop through things\n",
    "    for ytrue, ypred_proba, subject in zip(ytrues, ypred_probs, subject_groups):\n",
    "        pat_predictions[subject].append(float(ypred_proba))\n",
    "\n",
    "        if subject not in pat_true:\n",
    "            pat_true[subject] = ytrue[0]\n",
    "        else:\n",
    "            if pat_true[subject] != ytrue[0]:\n",
    "                raise RuntimeError(\"wtf subject should all match...\")\n",
    "    return pat_predictions, pat_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5338ab53",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import cdist\n",
    "\n",
    "# get line between optimum and clinical op point\n",
    "def create_line(x1, x2, y1, y2, n_points=200):\n",
    "    slope = (y2 - y1) / (x2 - x1)\n",
    "\n",
    "    xs = np.linspace(x1, x2, n_points)\n",
    "    ys = np.linspace(y1, y2, n_points)\n",
    "\n",
    "    return xs, ys\n",
    "\n",
    "\n",
    "def find_intersect_idx(x1s, y1s, x2s, y2s):\n",
    "    \"\"\"Help find intersection points between two curves.\"\"\"\n",
    "    euc_dists = []\n",
    "    points = np.vstack((x2s, y2s)).T\n",
    "    for idx, (x1, y1) in enumerate(zip(x1s, y1s)):\n",
    "        point = np.array([x1, y1])[np.newaxis, :]\n",
    "        dists = cdist(points, point)\n",
    "        euc_dists.append(min(dists))\n",
    "    return np.argmin(euc_dists)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aff3331",
   "metadata": {},
   "source": [
    "# Define Data Directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43d31169",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set seed and randomness for downstream reproducibility\n",
    "seed = 12345\n",
    "random_state = 12345\n",
    "np.random.seed(seed)\n",
    "\n",
    "# proportion of subjects to use for training\n",
    "train_size = 0.6\n",
    "\n",
    "# classification model to use\n",
    "clf_type = \"mtmorf\"\n",
    "\n",
    "# BIDS related directories\n",
    "bids_root = Path(\"/Users/adam2392/Dropbox/morf-study/\")\n",
    "# bids_root = Path(\"/home/adam2392/hdd/Dropbox/morf-study/\")\n",
    "deriv_path = bids_root / \"derivatives\"\n",
    "source_path = bids_root / \"sourcedata\"\n",
    "\n",
    "# metadata table\n",
    "excel_fpath = source_path / \"organized_clinical_datasheet_raw.xlsx\"\n",
    "\n",
    "# where to store the cross-validation indices to split patients on\n",
    "intermed_fpath = Path(deriv_path)\n",
    "\n",
    "# where to save results\n",
    "figures_path = Path(deriv_path) / \"figures\"\n",
    "study_path = Path(deriv_path)\n",
    "\n",
    "# feature names\n",
    "feature_names = [\n",
    "    \"fragility\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf61245",
   "metadata": {},
   "source": [
    "# Read in the Participants.tsv File Containing Clinical Metadata\n",
    "\n",
    "The participants.tsv file contains clinical metadata. Let's get the clinical success rate of our dataset\n",
    "and total success/failure counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2dcb8980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participant_id</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>hand</th>\n",
       "      <th>outcome</th>\n",
       "      <th>engel_score</th>\n",
       "      <th>ilae_score</th>\n",
       "      <th>date_follow_up</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>years_follow_up</th>\n",
       "      <th>site</th>\n",
       "      <th>clinical_complexity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sub-jh101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>JHH</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sub-jh102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NR</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>JHH</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sub-jh103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>JHH</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sub-jh104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NR</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>JHH</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sub-jh105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>JHH</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  participant_id  age  sex hand outcome  engel_score  ilae_score  \\\n",
       "0      sub-jh101  NaN  NaN  NaN       F          4.0         6.0   \n",
       "1      sub-jh102  NaN  NaN  NaN      NR         -1.0        -1.0   \n",
       "2      sub-jh103  NaN  NaN  NaN       F          4.0         6.0   \n",
       "3      sub-jh104  NaN  NaN  NaN      NR         -1.0        -1.0   \n",
       "4      sub-jh105  NaN  NaN  NaN       S          1.0         1.0   \n",
       "\n",
       "  date_follow_up  ethnicity  years_follow_up site  clinical_complexity  \n",
       "0            NaN        NaN              1.0  JHH                  4.0  \n",
       "1            NaN        NaN              1.0  JHH                  4.0  \n",
       "2            NaN        NaN              1.0  JHH                  3.0  \n",
       "3            NaN        NaN              1.0  JHH                  3.0  \n",
       "4            NaN        NaN              1.0  JHH                  2.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pat_df = read_participants_tsv(bids_root)\n",
    "subjects = pat_df[\"participant_id\"]\n",
    "display(pat_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7db2ec89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['F', 'NR', 'F', 'NR', 'S', 'NR', 'S', 'F', 'S', 'F', 'S', 'F', 'F', 'F', 'F', 'S', 'F', 'F', 'F', 'F', 'F', 'S', 'F', 'F', 'F', 'F', 'S', 'F', 'S', 'F', 'F', 'F', 'F', 'F', 'F', 'NR', 'F', 'S', 'S', 'NR', 'S', 'S', 'S', 'F', 'NR', 'F', 'S', 'F', 'S', 'S', 'S', 'S', 'F', 'S', 'S', 'S', 'S', 'F', 'S', 'F', 'S', 'F', 'S', 'F', 'S', 'S', 'S', 'S', 'S', 'F', 'F', 'S', 'F', 'F', 'F', 'F', 'S', 'S', 'F', 'F', 'F', 'F', 'F', 'F', 'S', 'F', 'S', 'S', 'F', 'F', 'S', 'S', 'NR', 'S', 'S', 'S', 'S', 'S', 'NR', 'S', 'S']\n"
     ]
    }
   ],
   "source": [
    "outcomes = []\n",
    "\n",
    "# get a list of all the outcomes\n",
    "for subj in np.unique(subjects):\n",
    "    # use excel file to set various data points\n",
    "    pat_row = pat_df[pat_df[\"participant_id\"] == subj]\n",
    "    if pat_row[\"outcome\"].isnull().values.any():\n",
    "        continue\n",
    "    outcome = pat_row[\"outcome\"].values[0]\n",
    "    outcomes.append(outcome)\n",
    "print(outcomes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a511f8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical sensitivity: 0.44554455445544555\n",
      "Clinical False Positive Rate: 0.5544554455445545\n"
     ]
    }
   ],
   "source": [
    "n_success = len(np.argwhere(np.array(outcomes) == \"S\"))\n",
    "clinical_sensitivity = n_success / len(outcomes)\n",
    "clinical_fpr = (len(outcomes) - n_success) / len(outcomes)\n",
    "\n",
    "print(f\"Clinical sensitivity: {clinical_sensitivity}\")\n",
    "print(f\"Clinical False Positive Rate: {clinical_fpr}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b14c5d4",
   "metadata": {},
   "source": [
    "# Plotting Comparisons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5f46a915",
   "metadata": {},
   "outputs": [],
   "source": [
    "def average_roc(fpr, tpr):\n",
    "    \"\"\"Compute average ROC statistics.\"\"\"\n",
    "    tprs = []\n",
    "    aucs = []\n",
    "    mean_fpr = np.linspace(0, 1, 200)\n",
    "\n",
    "    n_splits = len(fpr)\n",
    "    print(f\"Computing average ROC over {n_splits} CV splits\")\n",
    "    for i in range(n_splits):\n",
    "        #         print(i, len(fpr[i]), len(tpr[i]))\n",
    "        interp_tpr = np.interp(mean_fpr, fpr[i], tpr[i])\n",
    "        interp_tpr[0] = 0.0\n",
    "        tprs.append(interp_tpr)\n",
    "        aucs.append(auc(mean_fpr, interp_tpr))\n",
    "\n",
    "    mean_tpr = np.mean(tprs, axis=0)\n",
    "    mean_tpr[-1] = 1.0\n",
    "    mean_auc = auc(mean_fpr, mean_tpr)\n",
    "    std_auc = np.std(aucs)\n",
    "\n",
    "    std_tpr = np.std(tprs, axis=0)\n",
    "    return mean_fpr, tprs, aucs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "930ba586",
   "metadata": {},
   "outputs": [],
   "source": [
    "# identifying strings for each feature representation of the data\n",
    "feature_name = \"fragility\"\n",
    "\n",
    "## Setup for run\n",
    "names = {\n",
    "    \"Log. Reg\": \"blue\",\n",
    "    # \"Lin. SVM\": \"firebrick\",\n",
    "    \"SVM\": \"purple\",\n",
    "    #     \"kNN\": \"black\",\n",
    "    \"RF\": \"#f86000\",\n",
    "    \"MLP\": \"green\",\n",
    "    \"cnn\": \"magenta\",\n",
    "    \"xgb\": \"black\",\n",
    "    \"dummby\": \"teal\",\n",
    "    \"sporf\": \"red\",\n",
    "    \"mtmorf\": \"orange\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a241fb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There should be 10 total CV for Log. Reg. We found 10\n",
      "There should be 10 total CV for SVM. We found 10\n",
      "There should be 10 total CV for RF. We found 10\n",
      "There should be 10 total CV for MLP. We found 10\n",
      "There should be 10 total CV for cnn. We found 10\n",
      "There should be 10 total CV for xgb. We found 10\n",
      "There should be 10 total CV for dummby. We found 10\n",
      "There should be 10 total CV for sporf. We found 10\n",
      "There should be 10 total CV for mtmorf. We found 10\n"
     ]
    }
   ],
   "source": [
    "# dictionary data structure to hold all the CV experiment results\n",
    "nested_scores_feature = dict()\n",
    "\n",
    "for clf_name in names:\n",
    "    # nested CV estimators\n",
    "    nested_scores_search_pattern = f\"study_cv_scores_{clf_name}_{feature_name}_*.json\"\n",
    "    nested_scores = None\n",
    "\n",
    "    # get all json files that follow that search pattern\n",
    "    fpaths = (\n",
    "        study_path\n",
    "        / \"fragility retrospective study - comparison_classifiers\"\n",
    "        / \"clf-train-vs-test\"\n",
    "    ).glob(nested_scores_search_pattern)\n",
    "\n",
    "    fpaths = natsorted(fpaths)\n",
    "    for idx, json_fpath in enumerate(fpaths):\n",
    "        with open(json_fpath, \"r\") as fin:\n",
    "            _nested_scores = json.load(fin)\n",
    "\n",
    "        # either append to the nested dictionary, or create it\n",
    "        # some magic done to cast numbers in case they were saved in different formats\n",
    "        if nested_scores is None:\n",
    "            for key, val in _nested_scores.items():\n",
    "                if key in [\n",
    "                    \"validate_ytrue\",\n",
    "                    \"validate_fpr\",\n",
    "                    \"validate_tpr\",\n",
    "                    \"validate_pat_true\",\n",
    "                    \"validate_pat_predictions\",\n",
    "                ]:\n",
    "                    while len(val) <= 10 and (\n",
    "                        isinstance(val[0], list) or isinstance(val[0], dict)\n",
    "                    ):\n",
    "                        #                         print([len(x) for x in val])\n",
    "                        val = val[-1]\n",
    "                #                         print(len(val))\n",
    "                else:\n",
    "                    continue\n",
    "            nested_scores = _nested_scores\n",
    "        else:\n",
    "            for key, val in _nested_scores.items():\n",
    "                #                 if key == [\n",
    "                #                     \"validate_ytrue\",\n",
    "                #                 ]:\n",
    "                #                     val = val[0]\n",
    "                #                     for idx, y in enumerate(val):\n",
    "                #                         val[idx] = y[0]\n",
    "                if key in [\n",
    "                    \"validate_ytrue\",\n",
    "                    \"validate_fpr\",\n",
    "                    \"validate_tpr\",\n",
    "                    \"validate_pat_true\",\n",
    "                    \"validate_pat_predictions\",\n",
    "                ]:\n",
    "                    while len(val) <= 10 and (\n",
    "                        isinstance(val[0], list) or isinstance(val[0], dict)\n",
    "                    ):\n",
    "                        #                         print([len(x) for x in val])\n",
    "                        val = val[-1]\n",
    "                #                         print(len(val))\n",
    "                nested_scores[key].append(np.array(val).squeeze())\n",
    "    print(f\"There should be 10 total CV for {clf_name}. We found {len(fpaths)}\")\n",
    "    nested_scores_feature[clf_name] = nested_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c4326e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log. Reg\n",
      "Computing average ROC over 10 CV splits\n",
      "SVM\n",
      "Computing average ROC over 10 CV splits\n",
      "RF\n",
      "Computing average ROC over 10 CV splits\n",
      "MLP\n",
      "Computing average ROC over 10 CV splits\n",
      "cnn\n",
      "Computing average ROC over 10 CV splits\n",
      "xgb\n",
      "Computing average ROC over 10 CV splits\n",
      "dummby\n",
      "Computing average ROC over 10 CV splits\n",
      "sporf\n",
      "Computing average ROC over 10 CV splits\n",
      "mtmorf\n",
      "Computing average ROC over 10 CV splits\n",
      "['mtmorf' 'sporf' 'RF' 'xgb' 'Log. Reg' 'SVM' 'cnn' 'MLP' 'dummby']\n"
     ]
    }
   ],
   "source": [
    "auc_feat_names = []\n",
    "auc_feat_scores = []\n",
    "\n",
    "for name, nested_scores in nested_scores_feature.items():\n",
    "    print(name)\n",
    "    # summarize the boot-strapped samples\n",
    "    fprs, tprs = nested_scores[\"validate_fpr\"], nested_scores[\"validate_tpr\"]\n",
    "    # aucs = [auc(fpr, tpr) for fpr, tpr in zip(fprs, tprs)]\n",
    "    mean_fpr, tprs, aucs = average_roc(fprs, tprs)\n",
    "    mean_tpr, std_tpr = np.mean(tprs, axis=0), np.std(tprs, axis=0)\n",
    "\n",
    "    auc_feat_names.append(name)\n",
    "    auc_feat_scores.append(np.mean(aucs))\n",
    "\n",
    "auc_sorted_inds = np.argsort(auc_feat_scores)[::-1]\n",
    "print(np.array(auc_feat_names)[auc_sorted_inds])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ff747198",
   "metadata": {},
   "outputs": [],
   "source": [
    "# store all AUCs and Average Precision metrics\n",
    "feature_aucs = dict()\n",
    "feature_prs = dict()\n",
    "feature_pred_names = []\n",
    "feature_pred_probs = []\n",
    "feature_pred_subs = []\n",
    "\n",
    "# show some summary statements?\n",
    "verbose = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c552e73b",
   "metadata": {},
   "outputs": [],
   "source": [
    "color_names = {\n",
    "    \"Log. Reg\": \"#999999\",\n",
    "    \"Lin. SVM\": \"#f781bf\",\n",
    "    \"SVM\": \"#a65628\",\n",
    "    \"kNN\": \"#ffff33\",\n",
    "    \"RF\": \"#ff7f00\",\n",
    "    \"MLP\": \"#984ea3\",\n",
    "    \"XGB\": \"#cc99ff\",\n",
    "    \"SPORF\": \"#4daf4a\",\n",
    "    \"MORF\": \"#e41a1c\",\n",
    "    \"ConvNet\": \"#377eb8\",\n",
    "    \"DUMMBY\": \"black\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b080d7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing average ROC over 10 CV splits\n",
      "Computing average ROC over 10 CV splits\n",
      "Computing average ROC over 10 CV splits\n",
      "Computing average ROC over 10 CV splits\n",
      "Computing average ROC over 10 CV splits\n",
      "Computing average ROC over 10 CV splits\n",
      "Computing average ROC over 10 CV splits\n",
      "Computing average ROC over 10 CV splits\n",
      "Computing average ROC over 10 CV splits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-16-5afe8072afed>:221: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"paper\", font_scale=2.0)\n",
    "\n",
    "# figure to show ROC\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "\n",
    "# color scheme\n",
    "# colors = [\n",
    "#     \"blue\",\n",
    "#     \"orange\",\n",
    "#     \"green\",\n",
    "#     \"magenta\",\n",
    "#     \"black\",\n",
    "#     \"green\",\n",
    "#     \"red\",\n",
    "#     \"gray\",\n",
    "#     \"brown\",\n",
    "#     \"teal\",\n",
    "#     \"purple\",\n",
    "# ]\n",
    "\n",
    "# for idx, (feature_name, nested_scores) in enumerate(nested_scores_feature.items()):\n",
    "for idx in range(len(auc_feat_names)):\n",
    "    # extract the feature name for plotting\n",
    "    feature_name = np.asarray(auc_feat_names)[auc_sorted_inds][idx]\n",
    "\n",
    "    # get the test set performances for this feature\n",
    "    nested_scores = nested_scores_feature[feature_name]\n",
    "\n",
    "    # extract the predicted probability of success, the true outcomes\n",
    "    # for each 10 fold CV\n",
    "    pat_predict_proba = nested_scores[\"validate_pat_predictions\"]\n",
    "    pat_true = nested_scores[\"validate_pat_true\"]\n",
    "\n",
    "    # initialize lists to store certain metrics we want to keep track of\n",
    "    fprs = []\n",
    "    tprs = []\n",
    "    accs = []\n",
    "    aps = []\n",
    "    npvs = []\n",
    "    ppvs = []\n",
    "    ratio_succ = []\n",
    "    ratio_fail = []\n",
    "    n_succ = []\n",
    "    n_fail = []\n",
    "\n",
    "    # for visual sake only show the top 3 performing based on average AUC\n",
    "    #     if idx > 3:\n",
    "    #         idx = 4\n",
    "    feature_name = (\n",
    "        feature_name.upper()\n",
    "        .replace(\"CNN\", \"ConvNet\")\n",
    "        .replace(\"LOG. REG\", \"Log. Reg\")\n",
    "        .replace(\"CONVNET\", \"ConvNet\")\n",
    "        .replace(\"MTMORF\", \"MORF\")\n",
    "    )\n",
    "    color = color_names[feature_name]\n",
    "\n",
    "    # loop through each CV fold (10 in total)\n",
    "    for jdx in range(len(pat_true)):\n",
    "        # in case data was stored in different formats, just pull them out\n",
    "        # byproduct of saving w/ different encoding...\n",
    "        if isinstance(pat_true[jdx], dict):\n",
    "            pat_trues = pat_true[jdx]\n",
    "            pat_predicts = pat_predict_proba[jdx]\n",
    "        else:\n",
    "            pat_trues = pat_true[jdx].item()\n",
    "            pat_predicts = pat_predict_proba[jdx].item()\n",
    "\n",
    "        # keep each patient's probability of success prediction, true outcome and list\n",
    "        # of subjects in the test dataset\n",
    "        pat_proba = []\n",
    "        pat_y = []\n",
    "        subjects = []\n",
    "        for patient, pat_y_true in pat_trues.items():\n",
    "            pat_probas = pat_predicts[patient]\n",
    "            pat_mean_proba = np.mean(np.array(pat_probas).squeeze())\n",
    "\n",
    "            pat_proba.append(pat_mean_proba)\n",
    "            pat_y.append(pat_y_true)\n",
    "            subjects.append(patient)\n",
    "\n",
    "        # set the patient predictions based on the success probability prediction\n",
    "        pat_predictions = np.where(np.array(pat_proba) > 0.5, 1, 0)\n",
    "\n",
    "        # compute a balanced accuracy score of the predicted outcomes vs true outcomes\n",
    "        acc = balanced_accuracy_score(pat_y, pat_predictions)\n",
    "\n",
    "        # compute the number of failures/success per CV fold and the ratio of each\n",
    "        n_true_succ = len(\n",
    "            [y_ for y_, ytrue_ in zip(pat_predictions, pat_y) if ytrue_ == 1]\n",
    "        )\n",
    "        n_true_fail = len(\n",
    "            [y_ for y_, ytrue_ in zip(pat_predictions, pat_y) if ytrue_ == 0]\n",
    "        )\n",
    "        num_fail = len(\n",
    "            [\n",
    "                y_\n",
    "                for y_, ytrue_ in zip(pat_predictions, pat_y)\n",
    "                if y_ == ytrue_\n",
    "                if ytrue_ == 0\n",
    "            ]\n",
    "        )\n",
    "        num_success = len(\n",
    "            [\n",
    "                y_\n",
    "                for y_, ytrue_ in zip(pat_predictions, pat_y)\n",
    "                if y_ == ytrue_\n",
    "                if ytrue_ == 1\n",
    "            ]\n",
    "        )\n",
    "        ratio_fail.append(num_fail / n_true_fail)\n",
    "        ratio_succ.append(num_success / n_true_succ)\n",
    "        n_fail.append(n_true_fail)\n",
    "        n_succ.append(n_true_succ)\n",
    "\n",
    "        # create the roc curve on this fold\n",
    "        fpr, tpr, thresholds = roc_curve(pat_y, pat_proba)\n",
    "        fprs.append(fpr)\n",
    "        tprs.append(tpr)\n",
    "        accs.append(acc)\n",
    "\n",
    "        # store the subjects, predicted probabilities and feature names\n",
    "        feature_pred_subs.extend(subjects)\n",
    "        feature_pred_probs.extend(pat_proba)\n",
    "        feature_pred_names.extend([feature_name] * len(pat_proba))\n",
    "\n",
    "    ytrues, ypreds = (\n",
    "        nested_scores[\"validate_ytrue\"],\n",
    "        nested_scores[\"validate_ypred_prob\"],\n",
    "    )\n",
    "    if verbose:\n",
    "        ### Now compute statistics for summary, such as accuracies, f1scores,\n",
    "        accs = []\n",
    "        f1s = []\n",
    "        for ytrue, ypred in zip(ytrues, ypreds):\n",
    "            ytrue = np.array(ytrue).squeeze()\n",
    "            ypred = np.array(ypred).squeeze()\n",
    "            ypred = np.array(ypred) > 0.5\n",
    "            acc = balanced_accuracy_score(ytrue, ypred)\n",
    "            f1score = f1_score(ytrue, ypred)\n",
    "            accs.append(acc)\n",
    "            f1s.append(f1score)\n",
    "\n",
    "        print(f\"\\n\\n{feature_name}\")\n",
    "        print(f\"Balanced accuracy score: {np.mean(accs):.2f} +/- {np.std(accs):.2f}\")\n",
    "        print(f\"F1 score: {np.mean(f1s):.2f} +/- {np.std(f1s):.2f}\")\n",
    "        print(f\"Sensitivity: {youden_point[1]} and FPR: {youden_point[0]}\")\n",
    "        print(\"Improvement in FPR: \", np.round(youden_point[0] - clinical_fpr, 2))\n",
    "        print(\n",
    "            \"Improvement in TPR: \", np.round(youden_point[1] - clinical_sensitivity, 2)\n",
    "        )\n",
    "        print(\n",
    "            \"Total distance: \",\n",
    "            np.linalg.norm(\n",
    "                (youden_point[0] - clinical_fpr, youden_point[1] - clinical_sensitivity)\n",
    "            ),\n",
    "        )\n",
    "        print(f\"Average Preicions: {np.mean(aps):.3f} +/- {np.std(aps):.3f}\")\n",
    "        print(f\"Average NPVS: {np.mean(npvs):.3f} +/- {np.std(npvs):.3f}\")\n",
    "        print(f\"Average PPVS: {np.mean(ppvs):.3f} +/- {np.std(ppvs):.3f}\")\n",
    "        print(\n",
    "            f\"Average failed ratio: {np.mean(ratio_fail):.3f} +/- {np.std(ratio_fail):.3f}\"\n",
    "        )\n",
    "        print(\n",
    "            f\"Average success ratio: {np.mean(ratio_succ):.3f} +/- {np.std(ratio_succ):.3f}\"\n",
    "        )\n",
    "\n",
    "    # compute the average ROC curve from the above loop over nested CV\n",
    "    mean_fpr, tprs, aucs = average_roc(fprs, tprs)\n",
    "    mean_tpr, std_tpr = np.mean(tprs, axis=0), np.std(tprs, axis=0)\n",
    "\n",
    "    # compute the avg/std of the AUC statistic\n",
    "    mean_auc = np.mean(aucs)\n",
    "    std_auc = np.std(aucs)\n",
    "    feature_aucs[feature_name] = aucs\n",
    "\n",
    "    # actually plot the ROC curve\n",
    "    ax = _plot_roc_curve(\n",
    "        mean_tpr,\n",
    "        mean_fpr,\n",
    "        mean_auc=mean_auc,\n",
    "        std_auc=std_auc,\n",
    "        label=feature_name,\n",
    "        ax=ax,\n",
    "        color=color,\n",
    "        plot_chance=False,\n",
    "    )\n",
    "\n",
    "    # plot youden point on the curve\n",
    "    xs, ys = create_line(clinical_fpr, 0, clinical_sensitivity, 1)\n",
    "    youden_idx = find_intersect_idx(mean_fpr, mean_tpr, xs, ys)\n",
    "    youden_point = (mean_fpr[youden_idx], mean_tpr[youden_idx])\n",
    "#     ax.plot(\n",
    "#         youden_point[0],\n",
    "#         youden_point[1],\n",
    "#         marker=\"*\",\n",
    "#         color=color,\n",
    "#         markersize=20,\n",
    "#         linestyle=\"None\",\n",
    "#     )\n",
    "\n",
    "######## format the ROC curve figure ########\n",
    "ax.set(\n",
    "    ylabel=\"Sensitivity\\n(Predicted Success Correctly)\",\n",
    "    xlabel=\"1-Specificity\\n(Predicted Success Incorrectly)\",\n",
    "    title=\"ROC Curve of Classification Models \\n Predicting Surgical Outcome on Test Set\",\n",
    "    #     xlim=[0, 0.05],\n",
    "    #     ylim=[0.4, 1],\n",
    ")\n",
    "# plot clinical point\n",
    "ax.plot(\n",
    "    clinical_fpr,\n",
    "    clinical_sensitivity,\n",
    "    marker=\"*\",\n",
    "    color=\"red\",\n",
    "    markersize=20,\n",
    "    linestyle=\"None\",\n",
    "    label=\"Clinical Operating Point\",\n",
    ")\n",
    "\n",
    "fig.tight_layout()\n",
    "ax.legend(\n",
    "    #     ncol=2,\n",
    "    loc=(1.04, 0)\n",
    ")\n",
    "\n",
    "# save figure to disc or not?\n",
    "fig.savefig(\n",
    "    figures_path / f\"morf_vs_classifiers_quantilefeatures.pdf\", bbox_inches=\"tight\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b82d4691",
   "metadata": {},
   "source": [
    "## Compare the AUC per feature\n",
    "\n",
    "First, we'll construct a dataframe of each of the 10 folds and features considered.\n",
    "\n",
    "Then, well create a plot of the AUCs per feature.\n",
    "\n",
    "Next, we'll evaluate the statistics of this performance. Note that AUC is a metric for us to measure how well each feature represents the underlying epileptic network based on our classification experiment setup. So the higher the AUC distributions are, the better. \n",
    "\n",
    "We analyze how much better the predictions are by comparing the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a4dd1f06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MORF</th>\n",
       "      <th>SPORF</th>\n",
       "      <th>RF</th>\n",
       "      <th>XGB</th>\n",
       "      <th>Log. Reg</th>\n",
       "      <th>SVM</th>\n",
       "      <th>ConvNet</th>\n",
       "      <th>MLP</th>\n",
       "      <th>DUMMBY</th>\n",
       "      <th>id_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.899884</td>\n",
       "      <td>0.907422</td>\n",
       "      <td>0.924623</td>\n",
       "      <td>0.931774</td>\n",
       "      <td>0.926556</td>\n",
       "      <td>0.831852</td>\n",
       "      <td>0.779474</td>\n",
       "      <td>0.874372</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.391307</td>\n",
       "      <td>0.411156</td>\n",
       "      <td>0.458415</td>\n",
       "      <td>0.452742</td>\n",
       "      <td>0.608693</td>\n",
       "      <td>0.547258</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.568051</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.921197</td>\n",
       "      <td>0.941297</td>\n",
       "      <td>0.609846</td>\n",
       "      <td>0.458337</td>\n",
       "      <td>0.541663</td>\n",
       "      <td>0.539772</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.458337</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.454634</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.456525</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.543475</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.844221</td>\n",
       "      <td>0.854271</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.535712</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.535712</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.464288</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MORF     SPORF        RF       XGB  Log. Reg       SVM   ConvNet  \\\n",
       "0  0.899884  0.907422  0.924623  0.931774  0.926556  0.831852  0.779474   \n",
       "1  0.391307  0.411156  0.458415  0.452742  0.608693  0.547258  0.500000   \n",
       "2  0.921197  0.941297  0.609846  0.458337  0.541663  0.539772  0.500000   \n",
       "3  0.500000  0.500000  0.454634  0.500000  0.500000  0.456525  0.500000   \n",
       "4  0.844221  0.854271  0.500000  0.535712  0.500000  0.535712  0.500000   \n",
       "\n",
       "        MLP  DUMMBY  id_col  \n",
       "0  0.874372     0.5       0  \n",
       "1  0.568051     0.5       1  \n",
       "2  0.458337     0.5       2  \n",
       "3  0.543475     0.5       3  \n",
       "4  0.464288     0.5       4  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_COL</th>\n",
       "      <th>Classifier</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Morf</td>\n",
       "      <td>0.899884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Morf</td>\n",
       "      <td>0.391307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Morf</td>\n",
       "      <td>0.921197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Morf</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Morf</td>\n",
       "      <td>0.844221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID_COL Classifier       AUC\n",
       "0       0       Morf  0.899884\n",
       "1       1       Morf  0.391307\n",
       "2       2       Morf  0.921197\n",
       "3       3       Morf  0.500000\n",
       "4       4       Morf  0.844221"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_aucs_df = pd.DataFrame.from_dict(feature_aucs)\n",
    "feature_aucs_df[\"id_col\"] = np.arange(10)\n",
    "\n",
    "display(feature_aucs_df.head())\n",
    "\n",
    "# melt the dataframe, so it can be plotted using seaborn\n",
    "feature_aucs_df = pd.melt(\n",
    "    feature_aucs_df, id_vars=\"id_col\", var_name=\"classifier\", value_name=\"auc\"\n",
    ")\n",
    "# display\n",
    "feature_aucs_df.columns = [\n",
    "    x.capitalize() if x in [\"classifier\"] else x.upper()\n",
    "    for x in feature_aucs_df.columns\n",
    "]\n",
    "feature_aucs_df[\"Classifier\"] = feature_aucs_df[\"Classifier\"].str.capitalize()\n",
    "display(feature_aucs_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cb3f9acd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MORF', 'SPORF', 'MLP', 'SVM', 'Log. Reg', 'RF', 'XGB', 'ConvNet', 'DUMMBY']\n"
     ]
    }
   ],
   "source": [
    "# map feature names to short-hand\n",
    "feature_aucs_df[\"Classifier\"] = [\n",
    "    x.replace(\"Cnn\", \"ConvNet\")\n",
    "    .upper()\n",
    "    .replace(\"LOG. REG\", \"Log. Reg\")\n",
    "    .replace(\"CONVNET\", \"ConvNet\")\n",
    "    for x in feature_aucs_df[\"Classifier\"]\n",
    "]\n",
    "\n",
    "order = (\n",
    "    feature_aucs_df.groupby(\"Classifier\")\n",
    "    .median()\n",
    "    .sort_values(by=\"AUC\", ascending=False)\n",
    ")\n",
    "print(order.index.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f1bdbc89",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = {\n",
    "    \"Log. Reg\": \"#999999\",\n",
    "    #     \"Lin. SVM\": \"#f781bf\",\n",
    "    \"SVM\": \"#a65628\",\n",
    "    #     \"kNN\": \"#ffff33\",\n",
    "    \"RF\": \"#ff7f00\",\n",
    "    \"MLP\": \"#984ea3\",\n",
    "    \"XGB\": \"#cc99ff\",\n",
    "    \"SPORF\": \"#4daf4a\",\n",
    "    \"MORF\": \"#e41a1c\",\n",
    "    \"ConvNet\": \"#377eb8\",\n",
    "    \"DUMMBY\": \"black\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c01c1aa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['MORF', 'SPORF', 'MLP', 'SVM', 'Log. Reg', 'RF', 'XGB', 'ConvNet',\n",
      "       'DUMMBY'],\n",
      "      dtype='object', name='Classifier')\n"
     ]
    }
   ],
   "source": [
    "print(order.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f9db202c",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = [names[x] for x in order.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6ac406fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"font.weight\"] = \"bold\"\n",
    "plt.rcParams[\"figure.titleweight\"] = \"bold\"\n",
    "\n",
    "sns.set_context(\"paper\", font_scale=1.5)\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "# sns.boxplot(\n",
    "#     x=\"Classifier\",\n",
    "#     y=\"AUC\",\n",
    "#     order=order.index,\n",
    "#     data=feature_aucs_df,\n",
    "#     ax=ax,\n",
    "#     color=\"gray\",\n",
    "# )\n",
    "sns.stripplot(\n",
    "    x=\"Classifier\",\n",
    "    y=\"AUC\",\n",
    "    order=order.index,\n",
    "    data=feature_aucs_df,\n",
    "    ax=ax,\n",
    "    palette=colors,\n",
    ")\n",
    "# ax.axhline(\n",
    "#     feature_aucs_df[feature_aucs_df[\"Classifier\"] == \"Morf\"][\"AUC\"].median(),\n",
    "#     color=\"red\",\n",
    "#     ls=\"--\",\n",
    "#     label=\"Median AUC (MORF)\",\n",
    "# )\n",
    "xticklabels = ax.get_xticklabels()\n",
    "ax.set_xticklabels(xticklabels, ha=\"right\", rotation=30)\n",
    "ax.legend()\n",
    "ax.set(\n",
    "    title=\"AUC On Held-out Test Set \\n Over 10-Fold Cross Validation\",\n",
    "    xlabel=\"Classifier\\n(Sorted by Median)\",\n",
    "    ylabel=\"Area Under the Curve \\n(AUC)\",\n",
    ")\n",
    "\n",
    "# Select which box you want to change\n",
    "# mybox = ax.artists[0]\n",
    "\n",
    "# # Change the appearance of that box\n",
    "# mybox.set_facecolor(\"red\")\n",
    "# mybox.set_edgecolor(\"black\")\n",
    "# mybox.set_linewidth(3)\n",
    "\n",
    "# distance across the \"X\" or \"Y\" stipplot column to span, in this case 40%\n",
    "median_width = 0.4\n",
    "df = feature_aucs_df\n",
    "for tick, text in zip(ax.get_xticks(), ax.get_xticklabels()):\n",
    "    sample_name = text.get_text()  # \"X\" or \"Y\"\n",
    "\n",
    "    # calculate the median value for all replicates of either X or Y\n",
    "    median_val = df[df[\"Classifier\"] == sample_name][\"AUC\"].median()\n",
    "\n",
    "    # plot horizontal lines across the column, centered on the tick\n",
    "    ax.plot(\n",
    "        [tick - median_width / 2, tick + median_width / 2],\n",
    "        [median_val, median_val],\n",
    "        lw=4,\n",
    "        color=\"k\",\n",
    "    )\n",
    "\n",
    "# figpath = Path(figdir) / f'figure4-discriminationandprecision/'\n",
    "fname = f\"delta_auc_plot_morf_vs_classifiers_quantilefeatures.pdf\"\n",
    "fig.savefig(figures_path / fname, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb691db9",
   "metadata": {},
   "source": [
    "# Compute Effect Size Differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d815bb78",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.pivot(index=\"ID_COL\", columns=\"Classifier\", values=\"AUC\")\n",
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d3bc5955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Classifier</th>\n",
       "      <th>ID_COL</th>\n",
       "      <th>ConvNet</th>\n",
       "      <th>DUMMBY</th>\n",
       "      <th>Log. Reg</th>\n",
       "      <th>MLP</th>\n",
       "      <th>MORF</th>\n",
       "      <th>RF</th>\n",
       "      <th>SPORF</th>\n",
       "      <th>SVM</th>\n",
       "      <th>XGB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.779474</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.926556</td>\n",
       "      <td>0.874372</td>\n",
       "      <td>0.899884</td>\n",
       "      <td>0.924623</td>\n",
       "      <td>0.907422</td>\n",
       "      <td>0.831852</td>\n",
       "      <td>0.931774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.608693</td>\n",
       "      <td>0.568051</td>\n",
       "      <td>0.391307</td>\n",
       "      <td>0.458415</td>\n",
       "      <td>0.411156</td>\n",
       "      <td>0.547258</td>\n",
       "      <td>0.452742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.541663</td>\n",
       "      <td>0.458337</td>\n",
       "      <td>0.921197</td>\n",
       "      <td>0.609846</td>\n",
       "      <td>0.941297</td>\n",
       "      <td>0.539772</td>\n",
       "      <td>0.458337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.543475</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.454634</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.456525</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.464288</td>\n",
       "      <td>0.844221</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.854271</td>\n",
       "      <td>0.535712</td>\n",
       "      <td>0.535712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.565215</td>\n",
       "      <td>0.597351</td>\n",
       "      <td>0.870330</td>\n",
       "      <td>0.520791</td>\n",
       "      <td>0.494529</td>\n",
       "      <td>0.573721</td>\n",
       "      <td>0.570888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.505768</td>\n",
       "      <td>0.598078</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.584617</td>\n",
       "      <td>0.929146</td>\n",
       "      <td>0.519228</td>\n",
       "      <td>0.456859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.580613</td>\n",
       "      <td>0.417310</td>\n",
       "      <td>0.819289</td>\n",
       "      <td>0.521057</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.559614</td>\n",
       "      <td>0.599997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.584118</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.838868</td>\n",
       "      <td>0.478264</td>\n",
       "      <td>0.543260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.400956</td>\n",
       "      <td>0.575237</td>\n",
       "      <td>0.867638</td>\n",
       "      <td>0.523806</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.507619</td>\n",
       "      <td>0.487531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Classifier  ID_COL   ConvNet  DUMMBY  Log. Reg       MLP      MORF        RF  \\\n",
       "0                0  0.779474     0.5  0.926556  0.874372  0.899884  0.924623   \n",
       "1                1  0.500000     0.5  0.608693  0.568051  0.391307  0.458415   \n",
       "2                2  0.500000     0.5  0.541663  0.458337  0.921197  0.609846   \n",
       "3                3  0.500000     0.5  0.500000  0.543475  0.500000  0.454634   \n",
       "4                4  0.500000     0.5  0.500000  0.464288  0.844221  0.500000   \n",
       "5                5  0.500000     0.5  0.565215  0.597351  0.870330  0.520791   \n",
       "6                6  0.500000     0.5  0.505768  0.598078  0.500000  0.584617   \n",
       "7                7  0.500000     0.5  0.580613  0.417310  0.819289  0.521057   \n",
       "8                8  0.500000     0.5  0.500000  0.584118  0.500000  0.500000   \n",
       "9                9  0.500000     0.5  0.400956  0.575237  0.867638  0.523806   \n",
       "\n",
       "Classifier     SPORF       SVM       XGB  \n",
       "0           0.907422  0.831852  0.931774  \n",
       "1           0.411156  0.547258  0.452742  \n",
       "2           0.941297  0.539772  0.458337  \n",
       "3           0.500000  0.456525  0.500000  \n",
       "4           0.854271  0.535712  0.535712  \n",
       "5           0.494529  0.573721  0.570888  \n",
       "6           0.929146  0.519228  0.456859  \n",
       "7           0.500000  0.559614  0.599997  \n",
       "8           0.838868  0.478264  0.543260  \n",
       "9           0.500000  0.507619  0.487531  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9aff82e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ID_COL', 'ConvNet', 'DUMMBY', 'Log. Reg', 'MLP', 'MORF', 'RF', 'SPORF',\n",
      "       'SVM', 'XGB'],\n",
      "      dtype='object', name='Classifier')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a25e0279",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_dabest = dabest.load(\n",
    "    df,\n",
    "    #                                  x='Classifier', y='AUC',\n",
    "    idx=[\"MORF\", \"MLP\"],\n",
    "    id_col=\"ID_COL\",\n",
    "    resamples=200,\n",
    "    paired=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "214376d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adam2392/Documents/morf-demo/.venv/lib/python3.8/site-packages/dabest/_classes.py:774: UserWarning: The lower limit of the interval was in the bottom 10 values. The result should be considered unstable.\n",
      "  warnings.warn(err_temp.substitute(lim_type=\"lower\",\n",
      "/Users/adam2392/Documents/morf-demo/.venv/lib/python3.8/site-packages/dabest/_classes.py:779: UserWarning: The upper limit of the interval was in the top 10 values. The result should be considered unstable.\n",
      "  warnings.warn(err_temp.substitute(lim_type=\"upper\",\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DABEST v0.3.1\n",
       "=============\n",
       "             \n",
       "Good morning!\n",
       "The current time is Tue May 25 11:32:03 2021.\n",
       "\n",
       "The paired Cohen's d between MORF and MLP is -0.83 [95%CI -2.32, 0.102].\n",
       "The p-value of the two-sided permutation t-test is 0.089. \n",
       "\n",
       "200 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
       "The p-value(s) reported are the likelihood(s) of observing the effect size(s),\n",
       "if the null hypothesis of zero difference is true.\n",
       "For each p-value, 5000 reshuffles of the control and test labels were performed.\n",
       "\n",
       "To get the results of all valid statistical tests, use `.cohens_d.statistical_tests`"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_dabest.cohens_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f6cdf6fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>control</th>\n",
       "      <th>test</th>\n",
       "      <th>control_N</th>\n",
       "      <th>test_N</th>\n",
       "      <th>effect_size</th>\n",
       "      <th>is_paired</th>\n",
       "      <th>difference</th>\n",
       "      <th>ci</th>\n",
       "      <th>bca_low</th>\n",
       "      <th>bca_high</th>\n",
       "      <th>pvalue_permutation</th>\n",
       "      <th>pvalue_wilcoxon</th>\n",
       "      <th>statistic_wilcoxon</th>\n",
       "      <th>pvalue_paired_students_t</th>\n",
       "      <th>statistic_paired_students_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MORF</td>\n",
       "      <td>MLP</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Cohen's d</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.829754</td>\n",
       "      <td>95</td>\n",
       "      <td>-2.317151</td>\n",
       "      <td>0.102003</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.193359</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.093382</td>\n",
       "      <td>1.876126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  control test  control_N  test_N effect_size  is_paired  difference  ci  \\\n",
       "0    MORF  MLP         10      10   Cohen's d       True   -0.829754  95   \n",
       "\n",
       "    bca_low  bca_high  pvalue_permutation  pvalue_wilcoxon  \\\n",
       "0 -2.317151  0.102003               0.089         0.193359   \n",
       "\n",
       "   statistic_wilcoxon  pvalue_paired_students_t  statistic_paired_students_t  \n",
       "0                14.0                  0.093382                     1.876126  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(feature_dabest.cohens_d.statistical_tests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "56b50310",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    },
    {
     "data": {
      "image/png": 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s2Nn40OmUwp3NbLCZKf/Ru2a3n3Sf9Ra897lMmf3m4AUOjqi18o+ZTQTuAE6WtLyZjcX/v/4lJlCF0P6KDPkOLtil1s5Uzs6SZkn6SNJISXvXrfEtppEB95O858sWeT61xHEX4J+exuIJ9NcF3gX+FfhFfZvYOczsXXwU4JAK+00CnqfCEHSFc7yID2OfKmkpvNc7AfjXKOEXQnsr7MCY2eCCXWrtTBnwKnAjPjI2E590OULSdr1veetpZMB9DM9IBLArgKSB+NAnwGhJy0vaTNJmecflMhi9YGYfplmxr6VtlYZMu939wNqSNqmw30j8w8yWtV7IzB7Bg+yJwKLAH/A/tDMkLVHreUMILe+TvOfVdKYeNLNNzew0MzsL2Bb4DF92eELdW9kCGhZwzexDvLcKcKGkS/FJNuC919vx9ZwvpUfOyPT1VElXS7qR+WtI7+3bVrc3M/sCH3o/vFzQSzmRhwKH9nIYeASehepYfI3unXjQPTmyUIXQsWrtTG1YZkJV1asn2kGjlwX9BB8KngqcAiwFXAwcnN70i/kn4Fd4r/Zo4CB8CPTvzex/+7zFbS4t2Xkd2L/Cfu8CT+CJM2odWjY8cPfHZz+PxP8Q5wDfrLZwQgih9fWiM3UuMFHSdZIuAp7GY8IcPHVsx5G/R3Y2SdaNk6dy0n3VHwI3mNmbZfZbFPgu8ISZPdXL630beAr/pLoyPsxMasO8Ws8dQmicrO+d6b3jn/G/+3XxTtUtwM/MbIqkM4BLwe8Jp2OOxufnbI2PhH2E95bPS5MvO04E3C4haQt8uc6FZUYTkLQacAZwsZmVuveS5XrLA9/B8y8fgE+k2gufGDHMuuEXL4Q2F++d9RW5lLuEmY3D1zPvVWG/ycBoPAtVzX9oZvYpKTEGPox0AF7MfgXgsEiMEULoNhFwu8sdZEu9+Aj+u7Fzby5mZu/jeZYH4sPKG+NBeC3ggAi6IYRuEgG3i6R0jPdRuaLQPDwhxl6SVunlNV8H7sJnLB6OT6K6Cg++e/bm3CGE0E4i4Hafp/FZgGV7r2Y2BV/He2y54JyFmT0H3A1sDuxhZrOAK/B0nbv05twhhNAuIuB2mbyKQntKKpUBJmcs8DmVU7NlMRq4B/iBpP5mNgMPurt2alaZEELIFwG3C6Xe62jgyAoVhQwvu7ezpDV6eU3DJ01NAv5JPv3xEzzo7psWyocQQseKgNu9HsHXyA4qt1OabTwCH1ruVeKKdG/4PHxo+Zi07WP8nu4htVYtCiGEdhABt0ul4HcrPlu4f4Xdn8MXsu9Th+t+ite7PFLSjmnbB8C1+GSuDXp7jRBCaEURcLtYWrbzFHBYhf1y930HSVqnDpcehZddPDqXW9XM3sHL+h1fp2uEEEJLiYAbRgGrFyQVX4iZzcRzoh4jqV9vLpgyXd0GfIr3atdJ2ycBNwMnSlqzN9cIIYRWEwG3y6Xgdyue/alshQ4zewl4hwqFEDJ6AS/rNQ4vbLBKusYEPBifImnVOlwnhBBaQgTckOtZvgocmGH3O4HNe3uvNQ1T340nwBiFB9j+6bWX8CVEp2VYuhRCCG0hAm7IuRfYWNKAcjuZ2Wd4j/joSj3iSlLlonfwklzP4nVz+6XXngUeAk6XtFzps4QQQnuIakHhK2lZzsHABal4fbl9jwAWNbNbennNlfCSgH/EqxktB1xrZl+m13cHtgUuTfeRQ2gbQ4YMOQgvQffHnp6eu5vdnmrFe2d9RQ83fMXMXgHeA/bOsPsIYEBv186mJBzPAvvik7IMOCKXkMPMRuP3eU/rbY86hCZYGi/WsVazGxKaLwJuKHQnsG2lWcJmNgcvcHCEpKV7ec0Hgc3wikI3AKuzYNC/H5gInNrbGdIhNMlKzW5AaL4IuGEBKcfxPVSoKJT2nQQ8j1cB6s01P8OD7kEpkF8DbJPLsZw3wWoycFJvM16F0AQRcEME3FDUs8AsYLcM+44EVqtDLuSxwIqSNkpB/ypgP0kbw1dB9zZgJvANSYv28nohNNLKzW5AaL4IuGEheZmldpNU9o0ireMdChwqadleXPNLvGd9kKRFUo7l6/Eczmulfealaxl1KBsYQgP1qq506AzxhhWKSpV8HqRCRaG077t4D/WoSvtW8Ares942nfctfAnSiWk2cy4w3wAsk6VtIbSI6OGGCLihrMeBxYAs9WofBPqTgmUtUs96BF6ub4m07eV07lMkLZO2zcWLHawCHBxBN7SB/kOGDIlZ9l0uAm4oKa+i0P6Vkk+knudQvPrQCr245rvAa8DueduewJcGnSRp8bRtDnA1MABfUhRCq1ut2Q0IzRUBN5RlZpOBJ/Bcy5WGlifjhe2P6WWvcySwo6TlC7ZNwasJLZKu9zlwJbBFSpARQiuL3OBdLgJuyOIh/B7UFhn2fQT/vdq51oulmrlP4JmnctsMuAVYHJ+glUuMMRO4AtghV183hBYVPdwuFwE3VJRXUegQSUtV2HcenhBjr1wFoBqNBjaQ9LW8c3+Jz1xeB9gjb/s0POjuKWmbXlwzhL60RrMbEJorAm7IJM0YHofnWq607xQ8O1TNS3fMbDbwAAWTotL2q/Ee7TZ526fiw8sHSsrSEw+h0aLGc5eLgBuqcR+eP3nDDPuOBT4nrydag6eBJYEF8jWb2XQ8McZB+W0xsw/xYHx4LmFGCC0kerhdLgJuyCzNDL4Nz59cNqdxuud6K7CzpJreaNLw9Ai817powWsfAn8Fvp5/fjN7D7gOn7g1oJbrhtBHvlZ5l9DJIuCGqpjZBOAtMizFSZOfRuBDyzXlPzaz14CpwEITolIu5zvwOror5G1/C7gROCGXpSqEFvC1IUOGxHtuF4v//FCLu4CtMgaz5/CAmaXkXykj8AlRC03YMrMXgTF4Yoyl8ra/gfewT5a0ei+uHUK99COWBnW1CLihamY2C6/ec3SlIgJ5RQe2lbROjdebDLwE7FXi9UeBCXgKyMXytr+Clxs8tVJO6BAapKa/gdAZIuCGWr0AfEKGSVGp+s8d+H3VxWu83gN4yb5SZc5GADPwe7r5s5pfwGdMn96bDFghVEuSDR48eGjB5gHNaEtoDRFwQ01Sz/V2fFJUxWEyMxsHvAMcUOP1ZuBJNYoen9ozFC9qcHDBa0+lY0/vTUWjEOpg/WY3IDRPBNxQszQp6gGyVwm6E9hcUq1vOo8Ca0lat0R75uIzlDeQtGvBa48CzwCnSVq6xuuH0FsRcLtYBNzQW0/g9WkrplU0s8/wiUxHS6q6coqZfYGvBS5ZIShd42pgV0kDC15+CBiP39Ndotrrh1AHGzW7AaF5IuCGXslbb7tPQbGBUvtPwKsBVcxYVcLzgIDCYJp/jU+Ba/CcywMK2novPrR9ci/uJ4dQq5WGDBkSE/i6VATc0Gtm9hE+3HtExqHlEXjGqk0r7rnwtQyfIX1AuYBpZu8DN+FrcVcrOP4OfMLXArOaQ2iQqn/vQ2eIgBvqZTSwHGV6njkpH/IwPEBXfT81Jbx4lwoViczsdTw4n5Jfzzev8tBs4Lha8z2HUKPNmt2A0BzxRhPqIlXyuRW/v1oxiKag+QJweI2XvBfYTdIyFa7zHH6f+ZT8+8YpbeRNeLm/3tbvDaEaFT+UthtJi0j6maTxkuZIek/ShZWW4knaU9IDkmamx/2dXNs6Am6oGzN7B7/HekjGQ+4DVisyuSnLtT7Gs1hVTDGJ977fBL6Zn6gjr9zf8njBgwi6oRG2GjJkSKf9rv0W+DdgJXzS4mzgbOCuUslxUv3q+/AsdPfhGeP2AUZK2q4BbW64CLih3u4H1slSrSct4xmKT26qZX3sKHyZUdl1wGkI+U78TWCB3mya+XwNXjrtgAi6oQFWBDomx3f6+/tR+vZsMzuT+SNXO1N6FOtf8RGm283sKDM7EK8y1g84tw+b3DQRcENdpYpCw/H7sxWX3pjZu/gfWda1vPnHfgY8DByUYd/cEPIKwP4Fr+Vq7G5MifSRIdTZts1uQB3tBOQmHz4KX+U4n5a2lcpGlxs6fjRv25gKxzREGhp/NcujmvPGDM02JukM4IwmN6OUlYEzJU3JuP+awE8kzajhWmtJ+hu8/m4liwBrSpoGTC94bVFgDUnTmf9mEUKvXHTRRQvNSv7888//MHjw4H9oRnuqJckKNg0xs8F536+Y93x6wfPlCl7Pt2LefoXHlzqmUa7Ke74E3oN/Ff+AD/5hYVPg/1Vz0gi4bczMLgMua3IzikqVe34I/DWVy6u0/2r4h4eLzOyTKq+1OX7v50+pJ1tp/xWBbwN3mNlLBa8tD5wJPJhSQoZQk1ygOuuss14p8vJk4PCenp7CYNZSJJmZVRp5+iTv+bLAp3nPwauFlTpu5bz9shzTEGY2JPdc0h/x96Wf5u8j6T/w9mcWQ8qhT6Th3jvxoeKKH+xSRaDR1DZj+GW8dzsoY9umAtcCRxZWMEpJM64E9pW0VZXtCCGr1YD1mt2IOnkMmJue7wqQJkLmluKNlrS8pM0k5S+JGp1/TLJbwWut4JvAxUW2X5Jey6yhAbeWqeNpyriVeAxuXOtDDV4CPgb2zLj/I/iwbtn1tYXSpKgReJDsl/GYd/EJW9+UtErBax/jQffgWpJzhJDRbpV3aX1m9iFwQfr2QkmX4iU5wedn3A4ci78f5I8onYcH6sMk3SrpHmAH4Iv0WqtYFNikyPaq3xsa3cOteuo4cCPw+7zH9Xmvvdx3TQ29lVdRaAdlKAKfhoOHAnsVBsEMx74DvMH8iRhZjhmPL0c4RVL/gtcm47OXj5K0QTVtCSGjpk4MqrOf4LOOpwKnAEvhvcKD02qEhZjZY3j1rwfxiYy75Z6b2RONaHRG1wJ/lvRtSZunx3eAi/BiKZnJ3xP7Xpo6/i5+3/gEM7tR0pZ48gOAo83s1gzn+R1wDp6Pd9O0lrLSMVnuQ4Q+Iml7YDvgkoz3WHfEh4cz7Z933PLA94ELzCzzpCdJe+PZfy5LM5bzX1sPHza6zszezHrOEHL3cAcPHvxkiV2+BPbr6emZ2bhWVSfeOyGttvh3/L2lH57LfQ5wIfAzM8syWRNobA+31qnjX0m9nrPSt/9eLNhKGlw49Nz7podeegofJso6VDwWH/2oKuNMuv86Ftivqtb5p+r38LzLC4y0pIxYN+NDz2tWed4QylmUKn/HQ+OZ2Wwz+wk+QWoQsA2wkpmdU02whcYG3HJTxwtfL+UcYGngbeCKYjuY2WAzU/6jlsaG+smrKLRnmiGcZf9b8BJ7a1R5uYeBjaoJjul6t+FlBhcqwJAqHN2GDz2XTbIRQpUOaHYDQjZmNsvMnk+PWbWco5EB95O851VPA0/DhT9O3/5XSrAQ2oSZTcFnHh6ZZRZy6q2OAI7NMss577jZwAPAQdXMdk5D1zcAq+NLjApffwm4By9g3+w1gqFz7DFkyJCqC3iE9tTIgFvr1PGcH+E5bydTfIp2aH2P4JMptsm4/7P4B7G9q7zOU0B/is8sLCl9iLsG2Drddy58/Vm8iP3pyqs+FEIv9KOzJk+FMhoWcHsxdRx59Zlz0re/S2s8Q5tJvchbgQMLZwWX2D831LutpLWrvM4IvJdbavZ7qWNn4Flm9pW0UMBOsyfH4kG3bKWiEDI6tNkNCI3R6GVBVU8dT74HrIoPS/+xj9sY+pCZvQc8TcY3mRQA78CHlksWnC9iAv77slBPNcM1P8an+x8jaaEk82Y2GngRH15eqtrzh1BgtyFDhsRtii7Q0IBrZl+a2XlmtpGZ9TOz1c3srHR/DzO7rNhEJzP7fdq+YjXLPULLGoXnLM5UiNvMxuFLyjJPMMlLhrG38urgVnH82/jErRMlrVRklweAifhEqopFGkIoY1EyFOAI1ZH0tayPLOfJ+35bSf8lz2VflUjtGBoulcQbjmeYyRoM78BL8a1fxXU+AF6hxgpAZvYK/uHg1MLh4xTQ78bnFJxYZe87hEJHNrsBHeht4K2Mj3KuIdX4lrQyMBIvOfi/kn5a7sBCVQVcSStL+ltJf0gXRtIuKTlACJmZ2URgPBl7rem+/XDg6Cp7lCOBQbXOLDazsfjw8UmFQTXvHvMMiqzhDaEKmw0ZMiTSiNbXvvia/P3wwigf4tkOv54ev8U/MJ9R4Txb4ZN+Sce9bmZbAN8CvltNgzIH3DSj+GXgb/GMG8unl44AflXNRUNI7gE2kTQgy84pFeNrpE+bGY+Zgf+x9Ga940g8J/Txkhb4m0kTtIbha3i/Xvh6CFU4utkN6CRmNir3AE4G/sXMfmpmt6THT4GfA6dVONXSzE/QtB/+wR988uQ6RY8ooZo3h9/gXeuNWbDu6F1kT04fwldSlpY78LW5WYdkRwADqiwqMAZYRwWVgbLKS9yxOD4MXjjH4Et8De9SZFxnHEIRhw4ZMiTmA/SNvfAlfYUeonK2rzfw/O79gQOBe9P2VVm4pnZZ1QTcHYH/ZwsnX34LqDYbUAgAmNnLwPtkXGubElvcgmeEypQwIN0zvg+v/lNTMExB9XpgbYqsm0yz7K8DVgEOiaAbarAskXmqr0zB77sWOiy9Vs5v8brjbwFvMr904F7MrwWQSTUBV/gn/ELrML+7HUIt7sTX2mZKx5ju/75A8T+gUp7DZ4NuWXXr5l93Nl7lantJCyXvSIkzrgbWxe8fhVCt45rdgA71n8B/S7pS0g/S40rgv9NrJZnZn4FdgDOBPfM6na8Dg6tpRDUB9z7gh/ntkNcePRcf5guhJuk+6z14Kbysv5MjgdXT3IIs18jNKj6gmlSRRc4zHQ+qB0nasMjrn+OJMzaXFBmEQrW2HjJkyEK/V6F3zOwPwPHAAOD/pscA4BvptUrHP2lmw8xsZt62W82s2DB1SdW88fwz8LCk7YAlgPPx3sKipFSNIfTCs/hswF2ZP2RTkpl9IWkocLKkSSkQVjpmoqT38apFFa9R5jwfSvorXkHoCjN7v+D1menT85mS5pjZ47VeK3Sl46jQ6wrVM7Nb8NtRmUg6veD4ogVzqpG5h2tmr+E5cEcwv0d7ObCdmVVaxxRCWXlLbHbPLTnLcMw7+EzBaiYq3ZOu0au0jKls3214wF+hyOvT8IpWu0sa1Jtrha5z+JAhQyKDWZ1J6ifpKEn/kIrhIGlAsb/fZEjeY3A92lDV0JqZTa7XhUMoZGZTJT2EB9DLi0zQK+ZBfC3ctnjRgkrX+FjS8/gkrTt62d5xqYjBqZIuKczxnX6eK4EzUk93XG+uF7rGMnjmqcy9sVCepHXxD9tr4yO0Q4FP8Rz9S+JLXRdgZpmT7GRVzTrcvco96t2w0LUewyfnbZdl5zR7eCh+b3aFjNcYBQyUtEpNLVzw+o/iCTxOKnZv2Mw+wu/pHi5p495eL3SNmDxVX78DngFWAvI/GN+Cr61tCGXrRICkefji/vyhu68ONrOWzbIjyQrzM4fWJWl14HTgwiz3ZtMxu+NrxDP1jCXtBqxnZtf2qrF+LuFvkIsANxS7fqp2dFJ6fWJvrxlanyQDGDx48JM1nuKUnp6eV+rYpKp1ynunpMnAvmb2oqTpwDZm9npKuvOimZW9xSRpI3zlweoUdFTN7JdZ21HNLOV18OUO66THBsA3gOepIvNPCJWkHMhj8V5h1j/2R/AJfDtl3P9xYLVqcjOXkgLsMDwjTdG1vqkYwo14CsjMpQZDVzu22Q3oIEsBc4psX5UFEzktRNK38JKxv8HTQJ6W9zi1mkZUM2nqnYLHRDO7CfgZ8ItqLhpCBg8BKwObZ9k5pVgcilcHqjhUnBJV3IsHyF6nY8xLfLEBJWbtm9kb+BDWSakXH0I5h8XkqboZg48w5eRGof4OnwdSzi/wWeMrpEp3G+c9FqqZXU498r5OIOP9thCySgHsVuBQZaw5a17m8QG8jm2W3+1xwBfA1rW2s+D6n+NrdHcptT7YzF7FE32cmnU2duhaSwP7N7sRHeLnwE8lXYNPFv4XSY/iVZrOrXDsGsAl6UN9r/Qq4EpaFfgXvC5oCHWVlpu9RHW1Qp/Ah44q5UfNT4axX0ri0mtm9ikedA8tVZTBzF7AE3ecXsVEr9Cdjmp2AzqBmT2J326ajRdA2QN4FdjZzF6scPhIfBVEr1UzS/kLSXPyH3gO3OOBf6xHY0Io4j5gA0kbZNk5BdFb8F5mxRzf6d7qm8BuvWrlguf8gPn3a1crsc/T+DDX6ZKWrde1Q8fZbsiQIWs1uxHtTNLikh7B3x7ONLOBZraFmZ2ecrlXcgXwH5L+TtI+knbLf1TTlmrW4X6PvFnJwDy8luDjZja1mouGkJWZzZZ0G74294KUr7jSMZ9Kugc4VtLFaXi6nHuBsyU9mXVWdIY2vCHpLuCUtEZ3oXzjZvaYvLbvaZIuM7NZ9bh26DiHAxc1uxHtKmWl2xio9D5QyvXp6++KnR6frJlJ5mVB7axTprZ3M0nHATPM7O6M+ws4EZhsZvdl2P9AYOmU/q1uUj7lrYBL0z3eYu3cH59sdUWxfUJ7qsOyoJw3geN6enoa/mbdKe+dks4HPqhmCU/eseuVez1lncuk5iTuITTYXcAPJL2QUjqWZWYmaTjwfUmvpKHjch4CfixpjcLcyL00GlgOz7t8dWFvO7XzPqAfnibyqiy9+NBV1gU2w+czhNpMAf5e0p74ksCZ+S+a2XmlDqwmoFZS9h5usfu2pR71alAIxaQqHSPwikKZhnDMqxDdgQ8tly1wn3qWo/AqQHX7RJ/uKd+Fr/U7usQaXcNnLk/BA3N8EA6FDm52A9rcqcBUYCPgZPwWae7x3UoHS9pX0s2Snsuto5f0HUn7VNOIskPKks5gwfu2JZnZ5dVcuJE6ZVik26VgdTLwlplVWjuXf9xxwCwzu7PCfovgJShHpOU7dZMC/unAm2Z2T5nrH8/8jFVf1rMNobHqOKQM8C5wdKOHleO9EyQdC1yLrz44BdgiZan6MXCYmR2W9VxlP0mb2WW9aWgI9ZSGX2/DJziNS3mKs7gDH45+OSWfKHX+eZJGAAdKmlCPdXd55/5C0rXAdyR9akVK9qXr34Tfez5a0tAsaSpDV/ganrq0rh8EQybnAj82sz9L+kbe9jH4+t7MYugqtJU0A/kBfGj50iwBycw+S/dzj04znWeX2X08sAuwPb6mt27MbJakq4BvS5puZgvdkzOzL+W1dk/BU1veHkE3JHsTATczST8Hfpf+/ssGxnL3cPH75/cW2T4VL4aQWTXrcPtJ6pH0iqTPJX2Z/6jmoiH00hN4EY0dsh5gZuPxBe9l836n4DYCTxG5ZG8aWeL8U/HhqSPlJcOK7fNF2mdNvLfd1UN64StRla0638OzdeWel3pUuoc7Ff9bLLQ1UHECZ75qMk31AGcBF+D3dc8FLsEnevxNNRcNoTdSULwV2FepkHRGI4D1JZXNf5pmKY/Hs9HUnZm9C9wMfEMl8j6nXvhVwIbEG21wmw0ZMmSFZjeiXZjZ+mb2cd7zUo9KSXVuAn6dl6DGJG0B/Afz1+hmUk3APRE428z+B19A/FczOwv4v9QxS08IWZjZh3jt3MwVhVIQG4b3LpeusPtIYHv1UepFM5uAZ9E6VVL/Evt8BlwJbC2paEGE0FUE7NjsRnShn+P/9h/gPeaxeJW8ScCQak5UzT3cNYHn0vOZ+NpCgOHVXjSEOnkYOBsYiP8BVGRmEyW9AByGp18std90SY/hSSluqkNbi13jaUnL4dmoLit2b9nMZki6AjhT0pyUEzZ0r52BorPcQ3mSVsRvKa2Hr3v/SrmEGGlJ4r5pCdAOeEd1rJmNrLYN1QTcd4HV8KwnE/FhrqfxN7taU2aFULM0wShX7u61KlIjjsRnOg80LyRQyhg8GcbaGRJn1OpB/MPrCZKuLbYUKE0Uyw+6mT5chI5UlyT63UbSjvh6eOF/bx/i8WwW8B5QMQOVmT2AVyOrWTVDyvcDR6fnlwD/KU8IfQ1wQ28aEUKtUtap56kwGargmC/w2rmHqkzhgJTx6X5KFJWvh3Q/+nY8N/mRpa5jXnrwytSWzfqiLaEtrBf3cWvyX/hI1SrAZ3g1sfXwTuM/VzpY0kaSvifpXEm/yH9U04hqAu5ZpELzZnYxnrnjidTYmDQVmul+YB1JG2U9IAXqJykT5JJngcWBzXvXxLJtmYcPb68G7FNmv8n4B9wjJW3YV+0JLW+rZjegDQ3ClwjNwz/c9kujVv8MlFsShKRv4Wk1fwOcAZyW9zi1mkZUE3DfAH4pr7qAmd1gZn9rZucX5ocNoZFST3Q4cIS8+k5Wo/DhpUFlzj0Pn918YF+mXEw/w9XAVpK2L7Pfu8BfgeNKLSsKHW9gsxvQhr4EvkjPJwPrpOcf4T3dcn4B/CewgpltZGYb5z3KrngoVE3AvQA4FnhZ0iOSzu6rGZwhVMvMXsfnFuxXxTFf4kPLB5b7XU7n/hAvYN1n0uSMq/EJGiX/kFMy9ZvwvMvF1geGzrZxsxvQG5K+K+kFSbMlfSTpmlx+4jLHDJBkJR5FZ/kXeI75H6wfBX4u6WDg34BXKhy7BnBJPTLPZQ64ZvYfZjYQnyX3BH6T+T1JN0g6orcNCaEO7ga2kLROxT0T82LxYyhRWCDPCGCPDMuJeiWtG7wOOEZSycLjZvYacBs+w3nVvmxTaDltG3AlnQNcjJejvA54HzgJeLDcfIo804HfFzyyFM/5DZAb/fo/+O2bO4E9gb+tcOxI6jRZreZ6uGl47VB8SdA2Zpa5CG+jKRJwdw1JW+L3Qf+U9VaHvGjAmcALZvZYmf0OB+ZVKoJQD5I2BY4E/pImTJXab2vgAOCycvuFxlN9ixcU2renp2d6H5x3AfV875RX+foAWBn4qZn9t6SV8FnC/YC/M7P/LXHsAPy25iQzG1DFNVcCLscnVS6C925PScsDVwKmFkudKik/t8RaeE/4D/icjgUCvJmNydqeaoaU8xuzCl5V5Rd4N/3ZWs4TQh8YB3yMf3LNJA0VDcPTORbN/JQ8gN9jXbk3DczYplfS9U6VtEyZ/Z7Dlxadntb0hu6QeRSnt4oM4Q6u8VSb4cEWPPDlZt+/nLZlyey2lqRpkj6R9Kikr1fY/9f4qGwP8FNgdeDC3LXL5Cl/GK+R/TCeTWoD4Hd4b/fhvMdDGdr8lWpzKZ8gTwL/DvAz/A1hGzPbrpqLhtBX0h/QHcCOklar4riP8d/nY1KPt9g+M/GC8gfWoalZ2jQWeBEvTN+vwn6P40G3ZHAOHaXsPc96MjMVPAYX7iPpsjL3WE3SjcCKeYdML/I8//Vi3sRn81+D54XYGbixwi3NQ4HvmNl5ZvZb4CjggAwTINfHg+z6FR6V0kIuoJpZl5Px5RG34utxR9TjJnII9WZm0yTdh1cU+ksVv6dP4J/Cd6f0J9fH8GA+wMwm9r61FY3EZ1IfL+m6Uj+LmY1JM7RPk3R5SgsZOlfDergZDcMnLZYyDvgk7/tlizyfWub4SWb21Wzi9Lv+KrAuXlnrthLHrYUv/wPAzMZJmoOXO3yz1MXSxMS6qybg/iOeP3laXzQkhDp7Cq/msRNp+KoSM7OUuepsSePNixgU7jNX0r14AoqLygxJ1UVq063AycBhKl+u7wH8Xtgpkq4slioydIySE+qawcyG4UG3pNSrnIKXtNsVeDjdR82tcR+d9lsaD6QAr6VENetJeq/E73S5ql6LMn85UM6XaXtmadXAPwFbpk0vAP9pXoUss2pmKf85gm1oFyko3QrsJc+hmvW4T/EZycemSR7FvIj/0W7d64Zma9OX+NrbtSlzbzr9zCPwiSknSVq8Ee0LTdF2M9PTJMZfp2+HSLocH0laHC8EcGl6bSc80cRLzP9gcQbwtqSbJF2IZ4haF69c95cKl75B0ojcAw/QVxRsK0nSgXg2u23xD++PAtsBz0vaP9tP72qaNBVCO0j3ZcfgCTGqmWn5LD78tU+J8xq+BGn/RgW19Mn+amA7SYPK7JdLFTkdL//XsqsHQq+0XcAFSPdRv4/POD4JL4pzPbBXhQ7dffjf8i74ioKV8SIOB5rZ8DLHXZ6u9U7e4ypgQsG2cs4DLjCz7c3sJ+mxPfAnfPZyZn2WOSeEFvEInplnG+CZLAekYdzhwPclvWJFCheY2VuS3sKHxh6sY3vLtWu6pKuBMyRNT2txi+03T9Iw4ATg65JuivkWHSfzhMBWY2Z/woNVqdcfwIsM5G97iCpnBKfjzqz2mCK2ongKxz/iKY8zix5u6GhpOPYWPJtUlow0ueNm4LOdjy3Ti70X2KWa8/aWeR3g6/FAWjLLVPq5bwSWwiePxTr0zrLskCFD4v27MaZRfJLaeum1zOI/LHQ8M3sP791mriiUjhuHLz8oep/GzKam8+7buxZWx8zexIeNT1b5lJRz8Ww+KwOHRNDtKGLBmb6h7wwFLpJ0sKSl0+MQfD3vzdWcKAJu6BYPAF9LGZyqcQeeLnL9Eq8/CGwmafXeNK5a6cPAaDwxxlJl9ssVRViXKvJMh7awfLMb0CX+AV9adCc+N2I6/oH3CTyZRmYRcENXSEsLbgUOl1RuGUHhcZ/hlYiOVpFKRGb2OR50D6pXW6to26P4WsSyM5JTG6/CPxhkyeYT2kNkFmsAM5thZifgOayPSY+Nzeyb6dZTZg0NuJIWkfQzSeMlzZH0nqQLyw2LpeOWkNQj6RV5hYmpKa1XQ5ZlhM6QElWMx3MPV3PceOA14OASu4wFVlAV9Xjr6B78PtLXS2XIgq+yZF2Bz3Lu06pHoWFKjmyE+jOz18xseHq8Xss5Gt3D/S0+jXolfJhrNnA2cFep5Qtp+3BgMH4v6nq8NJnwskkhVOMeYBNJlWpgFhoBbKAiZfPSBKURwEHlgl5fSMuAhuFvvgeXu09rZtPxoLt7uaVFoW1EwG0AeUW8fymy/WeSrq/mXA17c5CXEPtR+vbsNF378PT9znnPC30Dz137ETDQzE43s++a2c5mVnbBcgiF0vDqHfjM3czL4tI62GHAkSpeou9VYBa+IL6h8iZHrY8vUyq37yfAlfga4i3L7RtaXuTNboy98Pu3he5Mr2XWyE/jOzF/3W+uUsSLzJ9WXere0mHp64fArZJmSXpN0rnFesWSBhcmzq7jzxA6gJm9jGdj2rvK4ybiWaYOK/JaLhnGPsXu9fa19EHianyZ0lYV9v0o7XuSpBMb0b7QJ0oWtAh1tTxQ7F7tLCoXXFhAIwNurZUicsPGm+ND0DfgKe5+hZcHXICZDS6sbtG7ZocOdQd+P7Pa2xL3AWtIGlj4Qlp+NIFsZcbqLqWlvBpfAlR0VrWkZdPEqePxv6dtGz0MHuqm4R/sutRrFK8QdiCexSqzRmaa+iTv+bLAp3nPoXSliOl5X/c3szmSPsPv/X4Tr3MYQlXMbEYqQnC0pIuzZmIysy8kDcXXwE5K90XzjQR+IGlsCoANZWYfyEuhHS/pivT9YsAmeC7YtfEctbcAb6djYhSoPUUPtzH+CPxHWt1wT9p2ED6v6F+rOVEjA+5jwNx0zV3xhNIDmT+1fbSk5fHcmrlhP/Dh52PT88I3hkhXF3rjGTxt266kSiVZmNk7kp7E7+demx+wUmnAx/FkGVUtiq8XM3tD0l141aMXgQ3xIfRngBvS2tzQ/iLgNoCZnS+vrf1r4L/T5tnAb8zsD9Wcq2FDSSkl3QXp2wslXcr8GoZj8YXExzK/SkTOn/FsP8sCI1OFiVx+zAv7ut2hc6VAORyftbtSlYePwj8sDiry2mhgfUkNL6GWsuDsgtf0XRkf3r7CzC43s2cj2HaUyIXfIGbWA6yCF0/YBVjVzP5Ptedp9L2bn+Bd8Kl40eClgIuBg9NMy4WY2RS8JNlNeG/k63ippFPN7H8b0ejQuVJ6xofw3mrm+/1pKdBQPEfzCgWvzQHux5cJ9fkcgrS+fRNJ3wT+Fi+ufTdwDj4Edkg1M7JD24jyiw1kZrPM7In0mFnLORr6R5jepM5Lj2KvXwZcVmT76/gkjxD6wmN4RaFt8cL1maT7o2Pw+8BXFNwLfQZf7rYZC47Y1E1aajcIr4SUy+t8S5qxnNvnLrxq0NGSbo77tR0lhpTbTMxODF0vTZi6FV+bWm1C+DF4T2OB7E3pnCPwHnDdatJKWlLSDpK+B5yOz2u4zMwuMbMn84NtXjtuBlagygxboeXFqEWbiYAbAt5bxROUL7TGtsJx8/Ch5b0lrVzw2mvAx8COvWmb3IaSjsOHidfHizH8zszuTetqy7XxC+BaYFNJO6dzrihpN0nHRxWhthVDym0mPiGFMN+DeNH5LVI1nkzM7GNJD+C1c/9SsMRoBF4w/tlUCCGzNJFrED5kPAt4GrjTzGZVc57UxlmS7gR+kkqLzQJewYehQ3uK9+82E/9hISRmNlfSrcAJkt6oMkA+gSdn2R2fhJU754eSXsKzWt1V6SSS+gFb4PeTV8EnCF6TeuBVST3X1dL5NscnKT4CbIkPQ0+q9pyhpUQPt81EwA0hj5m9KellfGH7LVUcZ5KG4WtfXy0IkPcDP5L0eJp1v4AUGNfFg+xmwCR8/fmraaJhZulca+JBdgtgUWAcvvzp7dTOjYBvSLq00nB0aGkRcNtMBNwQFnYv8ENJG1RThsvMPpU0Ah9avjgXLM1spqRH8FRwX1UXSYletsGHjb/Eh4zvrbbGZgqy6+C92M3TuV4CbgTeK5yZbGYTJN2DF6+/pEi2rNAeIuC2mQi4IRQws9mSbsfX5v4xTTrK6lk86O2Np3nMeRT4saQNgaXxIPs1vBjCTcC71SzZSfmP18N7sZsBn+E92WuByZXOZWbPpIB/cjWpLUM2kr7KDT9mzJhVttlmm6nLLLNMVaMVGUTAbTPqhmV5kiyKGIRqpVnB06stAympP/B94Dozezv1QL8GHAPsh5f5ewZ4uZpgnpYXrY8H2U3xSlvjgJdqGRpO7VrLzN6u9thQXPo3HQL8FFgSYLHFFptnZtphhx3eP+SQQ96t46TwJ3t6es6u18mKiffO+ooebgil3YUXInjBzN7NelAqjHAn8M2UV3lr/F7qM+nrs2b2fJZzSVocz4W8OV6A4CN8uPihlCWrZqkXHMG2voYA/0AKtgBz585dBODJJ59cHeDQQw/N/LtUQVQLajMRcEMoId17HYFnabooywSm1AvdBA+y2+I5wC8F3kwTliYCx0l6qVTvNs1U3hjvyW4IvI8H2fvMbFqxY0LzpWHkr3q2hebOnbvI2LFj19h7770/WHrppesxvBwBt81EwA2hvOfxHN67kbfcp5Ck1fEAuxXeC30aL8jxXWCR3D3VNAv6HTwB+kN5xy+JB+ot8GHjt/Hh4jtqzdtaiaR18EpJN8Y93Lo4Hp+wVpIke+aZZ1bcbbfd6jE7PAJum4l7uCFUkIoTnAX8Jf9eqaSl8QA7CJ8I9SzwTP7SH0kbA4cDF5jZ7LRtJTwQX4rXp90CXxY0CQ+yr1SbJKPKn2cxYJ/U7sWBg/vqWl1mPWBApZ369+8/Z7nllut11aZ58+bNef/99zPdmuiFveO9s34i4IaQQUqJuCVeXGNDvDe7ATAevzf7RqleoqSjAMzs1jShanO8oMDqeC/4JXzN7ey+/SlA0pp4GcwpwPC+6j13o5Tf+nfAMqX2WWyxxebtt99+b9Wphzujp6dnnzqcp6R476yvGFIOIZvX8SD5W3wpz9PArYXFAkp4BPi5pC3xYgPjgWvwnuUDZja5b5o8X1pGtAdewWgE8FxUDqq7G4GyJUPNTIMGDerVZLc8S9XpPKFBoocbQgmSlsDL9g0CVsSHfAcCv6k0eSkNG2+ODxevhJfPWx84L5doIhWK39DMru6rnyFdZxW8V/s5/iHh0768XjeT9Et8lvLSha8ttthi87bffvsP6jhLGWC3np6eXg9PlxLvnfUVPdwQ8qR1lAPwIeNNgDfwyU2vmdmXkiYDR0i6trCHmOrT5vIWL4sPFY8EJqZjD8F7tTemQ54AdpK0Yaos1Bc/y87AXnh6ybHRq+1zPenrQutwt99++w8OOeSQegZb8F5unwXcUF8RcEPgqyUdg/BUi5/j92XvLnKP82HgbGBLSS/i92FzQXYJPMjehS8DKrynex+ea3lLM3sxBeF7gIMk/ameM4XTRK9j8BKcfy6WwznUX/pA8wtJv8Pvk7Pffvu9NWjQoKl1WgpUaGkgRizaRATc0LXSetfN8d7savgSoOvN7L0yh80DHgf+Br8X+wUeZG8B3inXgzSzLyQNxdMpTko5k1/GlwgNAp6qw8+k9PMcAIwGHoklP41nZlNzGaXqNEGqlIWGrkPrioAbukpeov9BeM/0LTyAvmpmc0scs0g6JteTnYMPNU8BLq1mmNbM3pH0JHBUblha0t3ASZJe7M1MZUnLAkcB/fHye30+GSs0XQTcNhIBN3QFScsxvzKP4UPG55eqlJOC7ADmFweYia+RvQr4EF+/+kN8idCEKpszCvheasvTZvaupDfwWrojyx1YiqSBwKHAWODBasv6hbYVAbeNRMANHSsleNgUH2JdCw+Yw0h1YUvsvwHei90U+CQdc6mZfVyw+xxJw5lfUSjzxJV073Yo8K1U6P4T/P7u9yWNrSZ9Y0q+cTh+L/kaM3sn67GhI0TAbSMRcENHySvAPghfwvM+3pu9vlju4lQcYCPmFweYjN+THZUCYUlm9lrKjbw/cGc17TSzDySNwfM0X5Fq6Y5N5xqa5RySNgGOBF4AhlVZRjB0hqJ5m0NrioAbOoKkZfCCAYOAfniQvahY0Ezra/OLA7yL92TvqaEY+wi8WP0LZvZWlceOwYerdwIew2dA/42kr5WrTpTafwg+5H2TmU2s8rqhc0Q+5TYSATe0rVSZZ2M8yA4AXsF7mpOKrJFdivnFAQbgk6XGAbf3Jr2hmc2SdBc+CepPpSZelTh2Xhpa/o6kCWb2saT78WVCl5cY9l4fOBp4Dbiwt+kgJS0Ss5jbWvRw20gE3NB2JK3G/Mo8U/De7NDC4JN6vZvhw8Xr4DOLx6V9s6RkzOrF1JY9gAeqOTAF2VHAsZL+gqeM3Bm/h/xybr809H0A/rMMN7Px1VwnfThZCVgVXwKV+9oPz/8b2lO/ZjegGpL2B36Of0heKW0+0sxuy3DsnsCvgB3TpseBc81sdB80tU9EwA1tIfVQB+KBtj9emWehyUxpaUwupeKa+AziZ4Ab+qo4QFraczs+6WlcDctxHsc/GOxmZg+nGryHShqfJlitjadmfBevOlSyklCaXb0SCwbVVdO2afgM68n4aMBDeCnB0L4WbXYDqrQZPsFvDHBE1oMk7YhPLFwcGI5n2DoAGClpVzPr9Rr2RoiAG1pWCh4b4J+GN8KD50jg9fxh0JRVKRdkVwVeBR7F0zE2ZCKRmU2TNBIfWv5LNcO0KWDfApyVguwESVPxtI9LA9vhdXFfzB2T/m1WZOHAujIwnfmB9VX83vBHMamqI7XVe7iZnQ+cL2kAVQRc4F/xYHu7mR0FIOkJYAfgXODrdW5qn2ir/6zQHSStzPw0izNIxdzze3Zpn1yQXQHvsT2Il8nLfB+1zp7Eh5Z3wgN+Zmb2SUrzeKyki/Gf+Vd4BqvrgGXSkFp+YJ2JB9UP8axXY/DAGrl1u0ef93AlFc4lGGJmg/v6ugV2T1/z/67G4AF3jwa3pWYRcENLSDNvt8QD7crAc8DVZvZBel3p3m0u29My+PKde/HiAE2f+JN6qrfik6BerrSsKF9azjQR2A/4DT4EPAcvFL8S8wPra/ibzocRWAOearRPZakWJOky4FtldrnJzI7vRTNWTF/zVxFML3it5UXADU2Tgsx6+H3ZTfGAMwbI3buUpK8xvye7GB5kb8eTVzQ9yBZKk6AewSsKXV1ktrSA5Vl4KHhVQPjQ+QC8ruoLwHF4msZ61VANnaVZozmFhuF/v6WM6+X5P8E/iC+bty33vG3+NiLghoZL91xzaRa/wIdPR5jZzBSQ1paUC7Lz8CB7M/Bum5SXG4P31neX9AELB9bZzO+xTsLTMQ4AdgX+iA8V74dPDhmNTw65oaE/QWgXLZHC08yG4UG31yQtj094xMxyM/VH43nCd83bdbe819pCBNzQEGlZy+Z4kF0T773dALyH9+zWlbQ3PotxNv6J+Drgg1YOsukDwnIsvNxmPTzl4i3AO/i636fwoeD8e9Er4OtqFwcuyc26Th849sdnZv5Y0rpm9maDfqzQPvpk5n1fkbQH8F18pUHOOZKOx8tIPozPyL80d0j6eh5wGHBYum2zFH7/9ov0WluIgBv6TApGa+FDxlvggecpfH2p4b26I/AgOx0Pslea2YfNaG856WdZloUD66r4H31uVvA7+DKkyfhkjuXNbKG0j+l8g4ADgUeA0QVD5LfjxRFewWdmHyTpklb+8BGaotrMaM22EQvf690/fX0An1G/EDN7TNIBwC/z9n8QX4f7RB+0s09EwA11l9bCbo0HWuEB6AJgFp5K8Qj8nu3H+HDxJa1SID0Fwv4UD6xfMj+wvoevBf7QzGaVONcDwA8kbWJmr+Zt74/nQF4BuDw3MSyfmX2WPskfjf/b7YwPU79Qlx80dIq2CrhmdhlwWS37mNkoYO8+aFbDRMANdZEq7WyC99rWxXurt+LFAzYCDkpfP8CD7P1m9mlTGstXgXUZigdWY35g/QAvTP9htSkgU8H54cAx8oLzsyVtiZfRexr4a7kyemY2XtLrwMF4zuZj0uznVpkoE5qvrQJut4uAG3pFUq4yz1Z4gHoaD7Tr4xMcNsCHWccBd5nZjCa0cRkWnLSUey7mB9YP8RSNHwIz6zV0a2ZvSHoNv/e0CPA14DozezvjKe4GfoAPw78P7EKJYbfQlRYaHQmtKwJuqFrKfpSrzLMUPmR8JbAGPux5GPAmHmSHlxpy7aN2FfZWVwMWYcHA+lL6OqNB90Rfwyd23IwXHMic8Sn1iofhy4OuB06W9HRvCi6EjpEbiQltIgJuyCT1zjbC78uuz/yUgUvis493AV7H7zHeXOfiAIVtWYrigXUx5gfVD/Fe4YfA9GZMNkrJPA7C71v/Af93qrodZjZR0ov4v/HzeDKM2+vX0tCmpvT09ES6zjYSATeUJWlV5qdZ/AQPtO/iwfcIPL/xk3iB97pmPpK0JMUD6+LMD6q5fMGTaVJgLSblij0Gr1B0QeqproRP+rivhlPeB5yNz2jeT9LjrTibOzRULBNrMxFww0JSoBuIB9rl8Z7rOPz+4654gBuDFwfo9QSedL3C+6ur4cW18wPrhPR1WqsE1kJpvfF++L/fbWb2St7Ld+Czll80s/erOW+agDUMOAn/gHMgcE19Wh3a1IRmNyBUJwJuAL6atZurzLMJHthm4b8jG+HDsw/gxQFqym6ThliLBdalWDCwvp6+ftqqgbUYSWvhi/Y/wHu1C9y7NrMZku7FKwr9udrUlGb2tqQn8Q8+K0vawMxer1f7Q9t5rdkNCNWJgNvl0jDnIHzIeFE8yH6GrxF9Dx/CnFRNcJDUj+LLbZbG66/mAuvE9PWTdgqshVJx973wzDd3mlm5tbLP4BPOdsFHCao1CvgenrnqIEkXtWJO6dAQr1beJbSSCLhdKAXELfAJUBvguXsXTV9fw4eP364UBNN5VmHhwLoMntQiN4FpLPMDa0cFh1TB6Fi8jOCFZlZ2XWSqKDQc+G5aU1tVwo9U1GEonq1nJv5B6enaWh/a2Bf4qFNoIxFwu0QaMl4X783ugq9BNbwXOy493isWZNN9yWKBdVkWDKxPpq9TOy2wFkqztnfF63TeCzydtZduZlMkPQwcKemKanv3ZvZBqki0HT6B6sUo1dd1xvX09MT/eZuJgNvhUuWNQcC+eKCch/din8bXo07OveFLWkxSscC6HDCF+YH16fR1SqcH1mLSMPyxeKrHi6qpe5vnUXxi1bZ4fulqjcbTY66MV015oIZzhPb1bLMbEKoXAbcD5VXmOQh/U5+HJ8G/Fu/Jfoq/Ua8GbJmGRVfFZyRPZX5gfZb5gbUlyoA1Uxol2AH/8PIg8Fit957NbF7KlXyapPGVhqJLHD8U+BGwr6Qnqz1HaGuPNrsBoXoNDbhpGO6fgO/g5cs+xsuX/axUL0HSPsD9RV762MxW6ZOGtqG8IeND8R7PEnhwvRmfqLQkPrt1EB5YP2F+YH0+ff04AmtxaaTgKPzf8S9m9lFvz2lm70t6Cs/MdX0Nx38s6W7gG3gFlWG9bVNoC5/hk+9Cm2l0D/e3wN/hw5NX4z2Fs4FBknav8Gb/DnBj3vcNz8nbiiStiNddPRhYB09K8Sr+77Mk/sEmt+zmRXzo8eNIgJ9N+iCzNT5a8BjwcJ2H0UcB35e0uZm9VMPxj+OjGAdIerTa9b2hLT0W92/bU8MCbspY9KP07dlmdmOqnPICXnrscDzpfSkTzOycvm1l+0g9rmvxgPopPvx7Kb5cJLfsJgJrL6SiB0cCK+F1eusezMxsbpq1fLykifnF6TMeb5JuwmcrnyDp/7XzEquQyUPNbkCozSINvNZOzA/wjwKY2YvAtLRtjwrH7yxplqSPJI2UVLQuoqTBkiz/UZfWt55peD7dY4C9zewHZna5mY00s+fN7IMItrWTtDlepecjfGJUn/UczWwSvsTjwBqP/wSvH7obPpEqdK4vKX6LLbSBRgbcFfOeTy/yPP/1fIYPkd4IXIWvPdwXGCFpu4V2NhtsZsp/9L7prcfc+WY2vprqM6E8SUtJ+joe/K43s3sb9MHlXmBDSevXePzT+PDyD1MijtCZHuvp6ZlWebfQihoZcD/Je75skedTSxz3oJltamanmdlZ+DKKz4B+wAl1b2XoWpI2wnu1n+NJLN5q1LXNbDY+YnFkmmVe7fEG/BmfeX5onZsXWseIZjcg1K6RAfcxINdT2BVA0kB8jSfAaEnLS9pM0mZ5x21Y5hP7kn3T1NBNJPWTdAR+v3aYmd3RjEQSZparxLRPjcdPB/4IfEdS/zo2LbSGz4GRzW5EqF3DAm4qJXZB+vZCSZcCt6Xvx+Kf7o/FkzHkz9Y8F5go6TpJF+FDZ0sBc/Ci5yHUTNK6eK92MbzgQLOLAdyJz9r/Wi0Hm9mD+Jrrv6lrq0IrGNnT0zOr8m6hVTWyhwvwE+Bf8eHjU/DAeTFwcJn7ZEPxALwPnj92sbRtdzOrJUNPCLmsWgfha1jvNrNhZvZ5s9tlZjPxYcOjenEv9rfAbpK2rl/LQgsY3uwGhN5p6DrctM72vPQo9vpl+GzL/G234MkxQqgLSWvioykf4b3amU1uUqHngK3wWcdVLwExs8mSrgN+Iun76f5waG/v4rnKQxtrdA83hKaRtGjKXHYq8DBwQwsG29wEqNuAXVNu61rcgKf0PLluDQvNNKynp6fr8pZ3mgi4oSukxCvfAdYG/mRmz7Vygoi0tnYUPmu56qVtadLXhXjN3I3r3LzQWPOI4eSOEAE3dDRJi0jaDTgTr8pztZm1yzrGJ/A6xdvXePxYPE/2dyUtVbdWhUZ7qKen58NmNyL0XgTc0LFSnulvAZsBfzazsa3cqy2Ucjbfite8Xa7S/kWONzz953LAEXVuXmicm5rdgFAfEXBDx5HbAfgevkTmMjOb0uRm1cTMJuMZpA6vcWj5Dbx27q4pd3loL+8Spfg6RgTc0FFST/AUYDvgUjMbU+fqPs3wEJ76tNaAeReene3oSIjRdmKyVAeJgBs6QurVbo2Xe3wbuCQlW2l7aTndcOAQSUvXcPxHeC93HjVOwgpN8SWxJLKjRMANbS+V0TsB2BOfFPVAhdrKbSfldX4Rr3tciwfwQiBr4aX8Qusb1dPT83GzGxHqJwJuaGsp7/YP8OIYfzKzd5vboj51H7BeKrJQFTObha89noEvFVq+3o0LdTes2Q0I9RUBN7QlSUtKOgbv8d1gZiM6vf5vWlt7G3CEpH41nOIxYAngDeCYGFpuae8Tk6U6TgTc0HYkbYD3aufiqRknNblJDWNmE4BJwH41HDsXr7u7MrA4sGN9WxfqaHhMluo8EXBD20hl9A4DjgGGm9ltzSij1wLuBgZKWruGY8cBXwDjgX0krVzXloV6ub3ZDQj1FwE3tAVJ6+AzkJfEe7UTmtykpkn3Y++ihopCKRnGCDx71cP40HK8D7SWp3p6et5udiNC/cUfWmhpqYzeAcA3gXvN7GYz+6zZ7WoBL+ITxfas9sA04/lNfFj5YXz2cmgd0bvtUBFwQ8uStAZwFrAKcKGZvdTkJrWMvIpCO0larYZT3AfsArzbTukuu8Bc4P5mNyL0jQi4oeWkggN7AafjCRuuN7MZTW5Wy0lFGO7Hh5ar+ls2s6l4MYd9+6JtoWZjenp62qW4RqhSBNzQUlL91+8AA/B1tc9GD6yssXhGolpmHD8ELBL3cFvKPc1uQF+StL+k+yR9LMnSo2JhDUn75O2f//ioEe2ul8Wa3YAQwFMzAjsDe+G9traq7NMsZmaShgPflvRKqqOb9djPieQKrWQu8GCzG9HHNgNWB8ZQWwWrd4Ab875vq5GvCLih6SStgC/1WRQvo9eWlX2axcw+kvQInhDj6vig0rae6OnpmdnsRvQlMzsfOF/SAGoLuBPM7Jy6NqqBYigpNE0qOLAdPjFqPF7dJ4JtbcYA/YGtmt2QULNRzW5AG9hZ0ixJH0kaKWnvZjeoGhFwQ1NIWhY4CdgJuNzMRndAGb2mScUabsXzJC/T7PaEmjzU7AYUU+S+6eAi+1xW4h5r7nFjkVNXw4BX8eHkq4CZ+IS/EelDe1uIIeXQcJIGAofiE36u77TKPs1iZu9Keg44BLip2e0JVXm9p6fng2Y3ohgzy5Jzexgwsczr43rZjAfNbNPcN5JWwstwLoVXCnuql+dviAi4oWFSLdfD8UkT15jZO01uUie6H/iBpE3M7NVmNyZk1taFCsxsGHWagJcqWa2Zzvty2ryhpDdKfDhfsh7XbYQIuKEhJG0CHAm8AAwzsy+a3KSOZGZfpFnLx0iaZGazm92mkMljzW5AI0jaA/guPt8g5xxJx+MTJh8GjgUuzR2Svp4L7C9pNDANrxK2FDAHuLIRba+HCLihT0laAh/iXB+4ycwmNrdFnc/M3pD0GnAAkSawHcwDnm52IxpkI+BbBdv2T18fwFONFjMU+BqwD7Ai8FHadp6ZtcVwMoC6YQWBJMt4HyLUkaT1gaOB14AR0dtqHElLAT/EawW/2ez2dCNJBjB48OAnK+z6Uk9Pz2kNaFLV4r2zvmKWcqg7SYtLOhT4OnC7mQ2PYNtYqcDDnXjaxxjJam3d0rvtehFwQ12lGq3fB5YG/mhm45vcpK5lZuOAD/HsXaF1PdPsBoTGiIAb6kLSopL2x9fWjjSzm6KMXku4A9hB0urNbkgo6YVmNyA0RgTc0Gvpzfx7+HKfC8zsxSY3KSRmNh24Fzg6ihS0pI/wUYjQBeIPMNQsldHbA591+BhwbZTRa0lPA7Px+rehtYzr6enp/JmrAYiAG2okaWXg28CGwEVm9nQkzW9N6f9lOLBHytATWsdLzW5AaJwIuKEqqeDATnjN2ueBK6opCReaIxWFGA0cmUohhtbwSrMbEBonlguEzFLKtWOAxYFLzOzj5rYoVOkRYEtgELEUpVVE+s0uEgE3VJR6RNsAB+Fv2lHZpw2Z2TxJtwKnSZqQJlSF5pkOtGTBgtA3IuCGsiT1x3Mgr4APH7/f3BaF3jCz9yU9hVdr+muz29PlJsSEqe4S93BDSZK2wJNYTAYujmDbMUYBq0vavNkN6XKvNbsBobGihxsWkvLwHoYnC7/OzN5ucpNCHZnZ3DS0fFwqefZ5s9vUpV5vdgNCY0UPNyxA0sbAD4BZwIURbDuTmU3CJ+wc2Oy2dLHo4XaZ6OEG4Ksyegfh62qHmtkbTW5S6Hv3AAdIWiQmwTVF/I11mQi4AUnr4ct9JuK92hhi7AKpglPUy22OT4GpzW5EaKwIuF1M0uLAfsBA4DYzi0X4ITTGxJih3H0i4HYpSV8DjsVnIF9gZrOa3KQQusnEZjcgNF4E3C4jaVG8PuoOwJ1mFqXBQmi8ic1uQGi8CLhdRNJqeK92Bn6vNjINhdAck5rdgNB4EXC7QKqDuiuwO14bNSr7hNBcE5vdgNB4DV2Hm+qn/kzSeElzJL0n6UJJK2Q8/neSLD1u7OPmdoRUju0MYGM8W9RTEWxDaKovgXeb3YjQeI1OfPFb4N+AlYCr8aLYZwN3pXuLJUn6BnAOMLeP29gRUhm9HYHv4jU3LzezWIYQQvO93dPTE+9jXahhAVfSqsCP0rdnm9mZwOHp+53znhc7dnPgEmAoMKYv29kJJC0HnIqXYbvUzB6JXm0ILePNZjcgNEcje7g7Mf+e8aMAZvYiMC1t26PYQalazU3AO/jQaNnAIWlw3rCzSeqaQJN6tdvgowaT8Jq1Hza5WSGEBUXA7VKNDLgr5j2fXuR5/uv5LgHWBb5uZtNK7PMVMxtsZsp/1Nbc9iJpGeAb+MSoq8zswUjXF0JLihnKXaqRs5Q/yXu+LJ7aLPcciqQ5k7Q8HkTeAv7T66CzVXp5d0m3mdkRfdLaNpKG3A8HngFuMrO4PxRC64qCIF2qkQH3MXzC02L4EpUbJA0Elkuvj04Bdk0AM3sZyPVO10mPfGtQ5r5vN5C0JF5Gb23gejN7q8lNCiFUFkPKXaphQ8rpXuIF6dsLJV0K3Ja+H4snUT8Wn1H7UjrmkyLDw6PSMTd1y3BxMam6zw/wmd4XRrANoS3MwdOphi7U6MQXPwHeB74NnIIPI18M/CwVxW5wc9qXmc2WdIWZfdzstoQQMnu7p6cn5lZ0qYYGXDP7EjgvPYq9fhlwWYVz7FPvdrWrCLYhtJ13mt2A0DyNTnwRQgjdLCZMdbEIuCGE0DjRw+1iEXBDCKFxIodyF4uAG0IIjRMBt4tFwA0hhMZ5r9kNCM0TATeEEBpjek9Pz8xmNyI0TwTcEEJojA+a3YDQXBFwQwihMSLDVJeLgBtCCI0RpTK7XATcEEJojK4PuJJ+KelxSR9Kmi1poqTzJa2a4dg9JT0gaWZ63C9p90a0u14anUs5hBC6VaRihdOBL4C7gKWAY4AfAlsDe5Y6SNKOwH3A4sDwdOwBwEhJu5rZU33b7PqIHm4IITRGBFw4ycw2NrPTzOx44N/T9j0krVjmuH/Fg+3tZnaUmR2IV5nrB5zbt02un27p4Y6SZM1uRAih14aY2eBmN6JGU5vdgFoUee+s+f/AzB4p2LRE+voJMKPMobmh40fzto0BdgD2qKUtzSCziEPtStLgNn7zCS2gm36HJA0GeprdjjYzKkuFNkmXAd8qs8tNqUebf8wxwHV4L/U0M7u6zPnnAosC55jZ79O2/4v3fOea2eKV2tgKIuC2MUlmZlFEONQsfof6Vrf8+6bgOajMLuPM7K9pX+HDwEOA2cDpZnZDhfN/BKwM/B8z+79p2++BvwU+NLPVevszNEK3DCmHEELoI2Y2DBhWaT9JywCXA8cBbwHHmtmTBfssD6yZzvty2jwaOArYNW/X3fJeawvRw21j3fLpOfSd+B3qW/HvuyBJo4C90rfXsmAykF+a2RRJZwCXAuT+7STtDDyMdxLzZyl/AexuZk805AfopejhhhBCaJT18p6fVPDa/wBTih1kZo9JOgD4JbB/2vwgcG67BFuIgNvuhjS7AaHtxe9Q34p/3zxmNiDDPpcBlxXZPgrYu+6NaqAYUg4hhBAaIBJfhBBCCA0QATeEEEJogAi4XUTuN5Lel2TpsUKz2xWaR9Jl6ffggWa3pRPFv2/IFwG3iVLli1zgu6XgtbPyXjNJ/etwyWOAvwdWxycl/B5feB46RBN+p7pK/PuG3oiA2zqOlLQJfJWJ5R/qdWJJ/dLTLdPXt8zsTDM7x8w+q9d1Qsvps9+pAMS/b6hSBNzW8DEgvPcJnlFlEwqqi6Qh4W9JekLSp5ImS7pH0h55+5yR9wn7TEkTgc9TrtNfpd3WSa9P7OOfKzRPpt+pYvJ+f36Zfr8+k/SypBP7sL3tJv59Q9Ui4LaGUcBLwOmpEPNPgXnAhQX7/RofCh4E3AY8g2dbGSXp4CLn/T3wADAUGAE8lrZPT6/9pX4/QmgxWX+nyvk5HkAeAzYFrs3/cNfl4t83VC0Cbmsw4Ld4urK/4KWohgET8vZZBPi79Pw8MzvFzA4CRqbXflrkvN80szPM7DgzuwYv+gwwJQ0n/7L+P0poEVl+pyq5wMxOTNVink/bzq5jG9tZ/PuGqkXAbR1XAh8AR6Tv/7vg9RWBpdPz5/K25/5Q1y1yzvvr1rrQjir9TlXyQt7zF9PX9Yrt2KXi3zdUJQJuizCz2cD56dvRRQo1TwVmpedb5W0fmL6+WeScn9e1kaGtZPidqmRg3vPchLuFfs+6Vfz7hmpFLuXW8j/AIxQflpoH/AH4Z+DnkjYCVsUTeRvwmwa1MbSX/6H071Ql35e0Cl4qLfch76I6tatT/A/x7xsyih5uCzGz6WZ2r5lNLLHLz4Hv4sPIRwHbAfcB+5rZnY1pZWgnGX6nyvkVfitjZ+BV4FQze7Ce7Wt38e8bqhHFC0IIC5CUe1M4M1VuCXUU/77dK3q4IYQQQgNEwA0hhBAaIIaUQwghhAaIHm4IIYTQABFwQwihBpIG5OVF3qfZ7QmtLwJuHyso53Vy3vbBadtHfXjttSTdImlSXhsWyoYjaaCk21JBhM8kPS7pyL5qV6hO/A61rGl4TvLfA283uS2hDUTAbazzJC3RwOutAmyDFzmYXmwHSesBDwOHA08DdwA7Ard0wRtmO4rfoRZhZrmc5OeYWS2JL0KXiYDbOPPwPKl/V2nHejGzZ81sgJkdDUwpsdtPgOXxvK77mtlxwI146bEhjWlpyCh+h/qQpImpB3++pFtTT/0lSXtKOlnSG6kHf62k/sWGlPNGHSZK+kdJb0uaJumvkpZN++yTd9yAMtuOljQ2HT9L0gRJVzXnXyfUQwTcxrkHT3T+c0krZz0o702g1OOMXrZr9/T1MZs/ZX1M+jpI0jK9PH+on/gdaowfAHOA14HNgOF4ZaAHgCWBE4FzKpxjPbzyz/140ZET8A8mmUhaErgezyZ3J14o4TVg78w/RWg5kUu5cWYAPXi9zB5K9xYK/QVYqczr43rZrhXT1/zhwtxzASsAM3t5jVAf8TvUGMPN7HhJXwduwnvvZ5rZUEnLA8fi6RjL9Tbn4b39t9MxR+LD7Fktir8/f4EH/Ofx9I9fVP3ThJYRPdzG+jP+5vZ9YOMmtyXnk/R12bxtueeW93poDfE71PdyJS+nFtk2LX3N/1mLed/MchOpcucpd8wCnR8zmwmciVcPuhK/hz4NuC71fkMbih5uA5nZl5L+CbgNH5bK4tuUr5H5DPB4L5o1Gtge2FmS0pBgbojw2fSHH1pE/A41xNz01Ypsyyq/J1qYXWhG3vOVgInA1kXOcYOZXZl6yJsBNwDHAUOBq6tsT2gBEXAbzMxulzQS2C/j/gNqvZa89FduCccq6eshafvDZvZnvLzYGXhtzpGSpuJDZkYbTXjpJvE71PZexnurywHnS3oEOKvIfh9JehB4C793vHranvVWQmgxEXCb4x+BsfT9kH5/4FsF27ZkfrHrP5vZG5L2BP4d2APol9r2azMb1sftC7WL36E2ZWYzJJ2Gf5AZiPeefw2cV7Drbfh9333wDy+vA5dEKc72FbmUQwghhAaISVMhhBBCA0TADSGEEBogAm4IIYTQABFwQwghhAaIgBtCCC1C7jeS3s9LvblCs9sV6iMCbgghlFBQGvGWgtfOKshJ3b8OlzwG+Ht8ze1leOm/2XU4b2gBsQ43hBCyOVLSJmb2qiQB/1CvE0vqZ2ZzmL+++S0zO7Ne5w+tIXq4IYRQ2cd4IYa/T98fBWyStn8lDQl/S9ITqZTfZEn3SNojb58z8nrFZ0qaCHwu6TLgV2m3dXJl/vr45woNFAE3hBAqGwW8BJwuaVXgp3hFoAsL9vs1PhQ8CM8U9QxwADBK0sFFzvt7vOzfUGAE8FjaPj299pf6/Qih2WJIOYQQKjO8Ju7FeBDcHbgZmJC3zyLA36Xn55lZD4Ck+/C81z8F7i447zfzUzVK2gQv/TfFzM6p/48Rmil6uCGEkM2VwAfAEen7/y54fUW82DzAc3nbc6X91i1yzvvr1rrQ8iLghhBCBmY2Gzg/fTvazB4p2GUqMCs93ypv+8D09c0i5/y8ro0MLS2GlEMIIbv/AR5hwaHknHnAH4B/Bn4uaSNgVWB/fEj6Nw1qY2hREXBDCCEjM5sO3Ftml58D44Ef4jOZZwP3Ab8ys1F938LQyqI8XwghhNAAcQ83hBBCaIAIuCGEEEIDRMANIYQQGiACbgghhNAAEXBDCCGEBoiAG0IIITRABNwQQgihASLghhBCCA3w/wE/5foEv0hyMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(font_scale=2.0)\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "feature_dabest.cohens_d.plot(ax=ax)\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e99076af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log. Reg 0.09289194088370532\n",
      "SVM 0.02734375\n",
      "RF 0.08583095844428565\n",
      "MLP 0.193359375\n",
      "XGB 0.08583095844428565\n",
      "SPORF 0.7670968684102772\n",
      "ConvNet 0.027991815485665747\n",
      "DUMMBY 0.027991815485665747\n"
     ]
    }
   ],
   "source": [
    "for feature_name in names.keys():\n",
    "    if feature_name == \"MORF\":\n",
    "        continue\n",
    "\n",
    "    stat, pval = wilcoxon(df[\"MORF\"], df[feature_name])\n",
    "    print(feature_name, pval)\n",
    "#     feature_dabest = dabest.load(\n",
    "#         df,\n",
    "#         #                                  x='Classifier', y='AUC',\n",
    "#         idx=[\"MORF\", feature_name],\n",
    "#         id_col=\"ID_COL\",\n",
    "#         resamples=500,\n",
    "#         paired=True,\n",
    "#     )\n",
    "\n",
    "#     display(feature_dabest.cohens_d.statistical_tests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f73f493",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "morfdemo",
   "language": "python",
   "name": "morfdemo"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}