Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8cd30fee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import torch\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from itertools import chain\n",
    "from numpy import linalg as LA\n",
    "from numpy import dot\n",
    "from numpy.linalg import norm\n",
    "from collections import Counter\n",
    "import sklearn.metrics as metrics\n",
    "np.set_printoptions(suppress=True)\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bc9d19f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "The_Dataset_Name = 'LH_ASDNC'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2776efd1",
   "metadata": {},
   "source": [
    " ## RG-Select scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ef435c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def PrintRGResults(rep_dict_name):\n",
    "    with open(rep_dict_name,'rb') as f: \n",
    "        rep_dict = pickle.load(f)\n",
    "    final_matrix = rep_dict['views average'] + rep_dict['rank correlation']    \n",
    "    \n",
    "    repr_scores = (np.sum(final_matrix, axis=0) - 2) / 4\n",
    "    views_scores = (np.sum(rep_dict['views average'], axis=0) - 1) / 4\n",
    "    rank_scores = (np.sum(rep_dict['rank correlation'], axis=0) - 1) / 4\n",
    "    \n",
    "    model_orders = ['diffpool', 'gat', 'gcn', 'gunet', 'sag']\n",
    "    \n",
    "    model_sort_with_repr_scores = np.array(model_orders)[list(reversed(np.argsort(repr_scores)))]\n",
    "    model_sort_with_rank_scores = np.array(model_orders)[list(reversed(np.argsort(rank_scores)))] \n",
    "    model_sort_with_views_scores = np.array(model_orders)[list(reversed(np.argsort(views_scores)))]    \n",
    "    \n",
    "    repr_score_dict = {model_sort_with_repr_scores[i]: np.round(np.sort(repr_scores)[::-1][i], 3) for i in range(len(model_orders))}\n",
    "    rank_score_dict = {model_sort_with_rank_scores[i]: np.round(np.sort(rank_scores)[::-1][i], 3) for i in range(len(model_orders))}\n",
    "    views_score_dict = {model_sort_with_views_scores[i]: np.round(np.sort(views_scores)[::-1][i], 3) for i in range(len(model_orders))}\n",
    "\n",
    "    print('REPR SCORE : ', repr_score_dict)\n",
    "    print('VIEWS SCORE: ', views_score_dict)\n",
    "    print('RANK SCORE : ', rank_score_dict)\n",
    "    \n",
    "    print('REPR MATRIX: '), print(final_matrix)\n",
    "    print('RANK MATRIX: '), print(rep_dict['rank correlation'].sum(axis=0))    \n",
    "    print('VIEWS MATRIX: '), print(rep_dict['views average'].sum(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "af65d81a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REPR SCORE :  {'diffpool': 0.602, 'gcn': 0.588, 'gat': 0.383, 'gunet': 0.196, 'sag': -0.202}\n",
      "VIEWS SCORE:  {'gunet': 0.339, 'gcn': 0.331, 'diffpool': 0.331, 'gat': 0.297, 'sag': 0.069}\n",
      "RANK SCORE :  {'diffpool': 0.271, 'gcn': 0.257, 'gat': 0.086, 'gunet': -0.143, 'sag': -0.271}\n",
      "REPR MATRIX: \n",
      "[[ 2.          1.33015873  0.95396825  0.39365079 -0.26984127]\n",
      " [ 1.33015873  2.          0.48730159  0.2015873  -0.48730159]\n",
      " [ 0.95396825  0.48730159  2.          0.57460317  0.33492063]\n",
      " [ 0.39365079  0.2015873   0.57460317  2.         -0.38571429]\n",
      " [-0.26984127 -0.48730159  0.33492063 -0.38571429  2.        ]]\n",
      "RANK MATRIX: \n",
      "[ 2.08571429  1.34285714  2.02857143  0.42857143 -0.08571429]\n",
      "VIEWS MATRIX: \n",
      "[2.32222222 2.18888889 2.32222222 2.35555556 1.27777778]\n"
     ]
    }
   ],
   "source": [
    "PrintRGResults(The_Dataset_Name + '_Ks_15_cv.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2f1d2039",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REPR SCORE :  {'gcn': 0.453, 'diffpool': 0.267, 'sag': 0.182, 'gunet': 0.176, 'gat': 0.159}\n",
      "VIEWS SCORE:  {'gunet': 0.204, 'gcn': 0.196, 'diffpool': 0.196, 'gat': 0.188, 'sag': 0.025}\n",
      "RANK SCORE :  {'gcn': 0.257, 'sag': 0.157, 'diffpool': 0.071, 'gunet': -0.029, 'gat': -0.029}\n",
      "REPR MATRIX: \n",
      "[[ 2.         -0.01428571  0.78333333  0.21428571  0.08571429]\n",
      " [-0.01428571  2.          0.00952381 -0.09047619  0.73095238]\n",
      " [ 0.78333333  0.00952381  2.          0.84285714  0.17619048]\n",
      " [ 0.21428571 -0.09047619  0.84285714  2.         -0.26428571]\n",
      " [ 0.08571429  0.73095238  0.17619048 -0.26428571  2.        ]]\n",
      "RANK MATRIX: \n",
      "[1.28571429 0.88571429 2.02857143 0.88571429 1.62857143]\n",
      "VIEWS MATRIX: \n",
      "[1.78333333 1.75       1.78333333 1.81666667 1.1       ]\n"
     ]
    }
   ],
   "source": [
    "PrintRGResults(The_Dataset_Name + '_Ks_10_cv.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c65dcfac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REPR SCORE :  {'gcn': 0.087, 'sag': 0.082, 'diffpool': 0.069, 'gat': -0.119, 'gunet': -0.169}\n",
      "VIEWS SCORE:  {'gunet': 0.117, 'diffpool': 0.083, 'gat': 0.067, 'gcn': 0.058, 'sag': 0.025}\n",
      "RANK SCORE :  {'sag': 0.057, 'gcn': 0.029, 'diffpool': -0.014, 'gat': -0.186, 'gunet': -0.286}\n",
      "REPR MATRIX: \n",
      "[[ 2.         -0.2047619  -0.2         0.70952381 -0.02857143]\n",
      " [-0.2047619   2.          0.4047619  -0.87619048  0.2       ]\n",
      " [-0.2         0.4047619   2.         -0.26190476  0.4047619 ]\n",
      " [ 0.70952381 -0.87619048 -0.26190476  2.         -0.24761905]\n",
      " [-0.02857143  0.2         0.4047619  -0.24761905  2.        ]]\n",
      "RANK MATRIX: \n",
      "[ 0.94285714  0.25714286  1.11428571 -0.14285714  1.22857143]\n",
      "VIEWS MATRIX: \n",
      "[1.33333333 1.26666667 1.23333333 1.46666667 1.1       ]\n"
     ]
    }
   ],
   "source": [
    "PrintRGResults(The_Dataset_Name + '_Ks_5_cv.pickle')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b33682e",
   "metadata": {},
   "source": [
    "## Our Methodology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2519c90c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def GetBiomarkers(model, weights):\n",
    "    weights_vector = None\n",
    "\n",
    "    if model == 'sag':\n",
    "        weights_vector = torch.mean(weights['w'], 0).detach().cpu().numpy() \n",
    "    elif model == 'diffpool':\n",
    "        weights_vector = torch.mean(weights['w'], 1).detach().numpy()\n",
    "    elif model == 'gcn':\n",
    "        weights_vector = weights['w'].squeeze().detach().numpy()\n",
    "        #weights_vector = torch.mean(weights['w'], 1).detach().numpy()\n",
    "    elif model == 'gat':\n",
    "        weights_vector = weights['w'].squeeze().detach().numpy()\n",
    "    elif model == 'gunet':\n",
    "        weights_vector = torch.mean(weights['w'], 0).detach().numpy()  \n",
    "    else:\n",
    "        exit()\n",
    "            \n",
    "    return weights_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e53b009a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def GetModelDf(model_name):\n",
    "    df = pd.DataFrame(columns=['model', 'dataset_type', 'dataset', 'view', 'cv_no', 'weights', 'labels', 'preds'])\n",
    "    subdir_names = os.listdir(model_name)\n",
    "    for subdir in subdir_names:\n",
    "        if subdir == 'Labels_and_preds':\n",
    "            file_names = os.listdir(os.path.join(model_name, subdir))\n",
    "            for file in file_names:\n",
    "                if 'test' not in file:\n",
    "                    pred_label_path = os.path.join(model_name, subdir, file)\n",
    "                    with open(pred_label_path, 'rb') as f:\n",
    "                        pred_label_file = pickle.load(f)\n",
    "                    with open(pred_label_path.replace('train', 'test'), 'rb') as f:\n",
    "                        test_pred_label_file = pickle.load(f)\n",
    "\n",
    "                        \n",
    "                    weight_path = pred_label_path.replace('MainModel', 'W_MainModel').replace('Labels_and_preds', 'weights').replace('_train.pickle', '.pickle')\n",
    "                    #print('weight_path: ', weight_path)\n",
    "\n",
    "                    with open(weight_path, 'rb') as f:\n",
    "                        weight_file = pickle.load(f)\n",
    "\n",
    "                    cv_no = str(re.findall(\"CV_.\", pred_label_path))[5]\n",
    "                    view_no = str(re.findall(\"view_.\", pred_label_path))[7]\n",
    "                    #dataset_no = str(re.findall(\"_d._\", pred_label_path))[4]\n",
    "\n",
    "                    #print('cv: ', cv_no, 'view: ', view_no, 'dataset: ', dataset_no)\n",
    "                    new_row = {\n",
    "                               'model': model_name,\n",
    "                               'dataset_type': The_Dataset_Name, \n",
    "                               #'dataset': dataset_no, \n",
    "                               'view': view_no, \n",
    "                               'cv_no': cv_no, \n",
    "                               'weights': GetBiomarkers(model_name, weight_file), \n",
    "                               'train_labels': pred_label_file['labels'], \n",
    "                               'train_preds': pred_label_file['preds'],\n",
    "                               'test_labels': test_pred_label_file['labels'], \n",
    "                               'test_preds': test_pred_label_file['preds'],\n",
    "                                }\n",
    "\n",
    "                    df = pd.concat([df, pd.DataFrame.from_records([new_row])], ignore_index=True)\n",
    "                    #df = pd.concat([df, pd.DataFrame.from_records([{ 'a': 1, 'b': 2 }])])\n",
    "                    #print(opened_file_path, model, cv_no, view_no, dataset_no)\n",
    "                    \n",
    "                    df['w'] = df['weights'].map(lambda x: np.abs(x))\n",
    "                    all_biomarkers = list(chain.from_iterable(df['w'].map(lambda x: x).to_numpy()))\n",
    "                    min_val, max_val = min(all_biomarkers), max(all_biomarkers)\n",
    "                    df['n'] = df['w'].map(lambda x: (x - min_val) / (max_val - min_val))  \n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "325a2288",
   "metadata": {},
   "outputs": [],
   "source": [
    "def GetDatasetsByView(df):\n",
    "    df_dataset0 = df[df['view'] == '0']\n",
    "    df_dataset1 = df[df['view'] == '1']\n",
    "    df_dataset2 = df[df['view'] == '2']\n",
    "    df_dataset3 = df[df['view'] == '3']\n",
    "    df_dataset4 = df[df['view'] == '4']\n",
    "    df_dataset5 = df[df['view'] == '5']\n",
    "    \n",
    "    df_dataset0 = df_dataset0.sort_values(by=['cv_no'])\n",
    "    df_dataset1 = df_dataset1.sort_values(by=['cv_no'])\n",
    "    df_dataset2 = df_dataset2.sort_values(by=['cv_no'])\n",
    "    df_dataset3 = df_dataset3.sort_values(by=['cv_no'])\n",
    "    df_dataset4 = df_dataset4.sort_values(by=['cv_no'])\n",
    "    df_dataset5 = df_dataset5.sort_values(by=['cv_no'])\n",
    "    \n",
    "    return [df_dataset0, df_dataset1, df_dataset2, df_dataset3, df_dataset4, df_dataset5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8fd129ec",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>dataset</th>\n",
       "      <th>view</th>\n",
       "      <th>cv_no</th>\n",
       "      <th>weights</th>\n",
       "      <th>labels</th>\n",
       "      <th>preds</th>\n",
       "      <th>train_labels</th>\n",
       "      <th>train_preds</th>\n",
       "      <th>test_labels</th>\n",
       "      <th>test_preds</th>\n",
       "      <th>w</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.16066833, 0.13505433, 0.15165433, -0.060330...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0.16066833, 0.13505433, 0.15165433, 0.0603303...</td>\n",
       "      <td>[0.838708, 0.704667, 0.7915367, 0.3136275, 0.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.15732002, 0.13605203, 0.1512491, -0.0636897...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, ...</td>\n",
       "      <td>[0.15732002, 0.13605203, 0.1512491, 0.06368978...</td>\n",
       "      <td>[0.82118595, 0.70988804, 0.7894161, 0.3312079,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.15922722, 0.15374441, 0.17657454, -0.061664...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, ...</td>\n",
       "      <td>[0.15922722, 0.15374441, 0.17657454, 0.0616645...</td>\n",
       "      <td>[0.8311665, 0.8024744, 0.9219471, 0.32060942, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.16292083, 0.13645568, 0.144762, -0.05808449...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.16292083, 0.13645568, 0.144762, 0.058084495...</td>\n",
       "      <td>[0.85049564, 0.7120004, 0.7554683, 0.30187476,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.16302794, 0.13638008, 0.14201143, -0.057977...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[0.16302794, 0.13638008, 0.14201143, 0.0579771...</td>\n",
       "      <td>[0.85105616, 0.7116047, 0.74107426, 0.30131298...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.16094077, 0.13922432, 0.14928113, -0.059993...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0.16094077, 0.13922432, 0.14928113, 0.0599935...</td>\n",
       "      <td>[0.8401337, 0.726489, 0.77911747, 0.3118649, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.16348527, 0.13709262, 0.14959766, -0.057465...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0.16348527, 0.13709262, 0.14959766, 0.0574652...</td>\n",
       "      <td>[0.8534494, 0.7153336, 0.7807739, 0.298634, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.15837951, 0.13525918, 0.15121678, -0.062630...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, ...</td>\n",
       "      <td>[0.15837951, 0.13525918, 0.15121678, 0.0626301...</td>\n",
       "      <td>[0.8267304, 0.70573896, 0.7892469, 0.32566294,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.16344443, 0.13798764, 0.14870547, -0.057564...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0.16344443, 0.13798764, 0.14870547, 0.0575646...</td>\n",
       "      <td>[0.85323566, 0.7200173, 0.7761049, 0.2991544, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.16864496, 0.16324726, 0.1731666, -0.0521947...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0.16864496, 0.16324726, 0.1731666, 0.05219473...</td>\n",
       "      <td>[0.88045067, 0.85220385, 0.90411294, 0.2710529...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.15780546, 0.13712685, 0.13740453, -0.063061...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0.15780546, 0.13712685, 0.13740453, 0.0630613...</td>\n",
       "      <td>[0.8237263, 0.7155127, 0.71696585, 0.32791907,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.15822014, 0.13861144, 0.15274994, -0.062784...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, ...</td>\n",
       "      <td>[0.15822014, 0.13861144, 0.15274994, 0.0627842...</td>\n",
       "      <td>[0.8258964, 0.7232817, 0.7972702, 0.3264692, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.16152766, 0.14119557, 0.15068255, -0.059480...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0.16152766, 0.14119557, 0.15068255, 0.0594808...</td>\n",
       "      <td>[0.84320503, 0.7368047, 0.7864513, 0.30918205,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.1643773, 0.13279654, 0.14943255, -0.0564641...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0.1643773, 0.13279654, 0.14943255, 0.05646418...</td>\n",
       "      <td>[0.8581175, 0.69285166, 0.7799099, 0.29339546,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.16247007, 0.13601503, 0.14947107, -0.058517...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0.16247007, 0.13601503, 0.14947107, 0.0585173...</td>\n",
       "      <td>[0.8481367, 0.7096944, 0.7801115, 0.3041401, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.15714587, 0.13768107, 0.14977053, -0.063779...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, ...</td>\n",
       "      <td>[0.15714587, 0.13768107, 0.14977053, 0.0637793...</td>\n",
       "      <td>[0.8202746, 0.718413, 0.78167856, 0.3316765, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.15896358, 0.13792183, 0.15179127, -0.062042...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.15896358, 0.13792183, 0.15179127, 0.0620425...</td>\n",
       "      <td>[0.82978684, 0.71967286, 0.7922533, 0.3225877,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.16587204, 0.1291279, 0.13736813, -0.0551098...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, ...</td>\n",
       "      <td>[0.16587204, 0.1291279, 0.13736813, 0.05510981...</td>\n",
       "      <td>[0.8659396, 0.67365324, 0.7167753, 0.28630793,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.15837501, 0.1356627, 0.12671615, -0.0626011...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.15837501, 0.1356627, 0.12671615, 0.06260115...</td>\n",
       "      <td>[0.8267068, 0.70785064, 0.66103226, 0.32551095...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.18261975, 0.12798643, 0.14506856, -0.038341...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, ...</td>\n",
       "      <td>[0.18261975, 0.12798643, 0.14506856, 0.0383411...</td>\n",
       "      <td>[0.95358235, 0.6676798, 0.75707257, 0.19855566...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.15242025, 0.13071504, 0.1492608, -0.0685871...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0.15242025, 0.13071504, 0.1492608, 0.06858719...</td>\n",
       "      <td>[0.79554486, 0.681959, 0.7790111, 0.35683662, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.1584692, 0.13599494, 0.15081663, -0.0625404...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, ...</td>\n",
       "      <td>[0.1584692, 0.13599494, 0.15081663, 0.06254046...</td>\n",
       "      <td>[0.8271997, 0.7095893, 0.78715295, 0.32519335,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.15982582, 0.13770749, 0.15005179, -0.061138...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0.15982582, 0.13770749, 0.15005179, 0.0611384...</td>\n",
       "      <td>[0.834299, 0.7185512, 0.7831504, 0.31785655, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.15852232, 0.1370207, 0.14746836, -0.0624634...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, ...</td>\n",
       "      <td>[0.15852232, 0.1370207, 0.14746836, 0.06246343...</td>\n",
       "      <td>[0.8274777, 0.71495724, 0.769631, 0.32479024, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.1609174, 0.13063934, 0.13813792, -0.0600783...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, ...</td>\n",
       "      <td>[0.1609174, 0.13063934, 0.13813792, 0.06007838...</td>\n",
       "      <td>[0.8400114, 0.6815628, 0.72080374, 0.312309, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.14357023, 0.119312726, 0.14590594, -0.07743...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, ...</td>\n",
       "      <td>[0.14357023, 0.119312726, 0.14590594, 0.077431...</td>\n",
       "      <td>[0.74923164, 0.6222893, 0.7614547, 0.40312007,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.1615522, 0.14057843, 0.15191266, -0.0594561...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, ...</td>\n",
       "      <td>[0.1615522, 0.14057843, 0.15191266, 0.05945617...</td>\n",
       "      <td>[0.8433334, 0.7335752, 0.7928886, 0.3090529, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.16378255, 0.13800617, 0.14994726, -0.057226...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0.16378255, 0.13800617, 0.14994726, 0.0572266...</td>\n",
       "      <td>[0.8550051, 0.72011423, 0.7826034, 0.29738545,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.16348529, 0.13356523, 0.14213037, -0.057520...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, ...</td>\n",
       "      <td>[0.16348529, 0.13356523, 0.14213037, 0.0575201...</td>\n",
       "      <td>[0.85344946, 0.6968743, 0.7416967, 0.2989216, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>gcn</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.15930878, 0.13866591, 0.15225281, -0.061695...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0.15930878, 0.13866591, 0.15225281, 0.0616956...</td>\n",
       "      <td>[0.83159333, 0.7235668, 0.7946686, 0.32077214,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model dataset_type dataset view cv_no  \\\n",
       "0    gcn     LH_ASDNC     NaN    2     3   \n",
       "1    gcn     LH_ASDNC     NaN    0     3   \n",
       "2    gcn     LH_ASDNC     NaN    5     2   \n",
       "3    gcn     LH_ASDNC     NaN    4     2   \n",
       "4    gcn     LH_ASDNC     NaN    1     1   \n",
       "5    gcn     LH_ASDNC     NaN    3     3   \n",
       "6    gcn     LH_ASDNC     NaN    3     1   \n",
       "7    gcn     LH_ASDNC     NaN    0     2   \n",
       "8    gcn     LH_ASDNC     NaN    1     3   \n",
       "9    gcn     LH_ASDNC     NaN    5     3   \n",
       "10   gcn     LH_ASDNC     NaN    4     1   \n",
       "11   gcn     LH_ASDNC     NaN    2     2   \n",
       "12   gcn     LH_ASDNC     NaN    5     4   \n",
       "13   gcn     LH_ASDNC     NaN    3     4   \n",
       "14   gcn     LH_ASDNC     NaN    2     1   \n",
       "15   gcn     LH_ASDNC     NaN    3     0   \n",
       "16   gcn     LH_ASDNC     NaN    2     0   \n",
       "17   gcn     LH_ASDNC     NaN    1     2   \n",
       "18   gcn     LH_ASDNC     NaN    4     4   \n",
       "19   gcn     LH_ASDNC     NaN    1     0   \n",
       "20   gcn     LH_ASDNC     NaN    0     1   \n",
       "21   gcn     LH_ASDNC     NaN    0     4   \n",
       "22   gcn     LH_ASDNC     NaN    3     2   \n",
       "23   gcn     LH_ASDNC     NaN    5     0   \n",
       "24   gcn     LH_ASDNC     NaN    4     0   \n",
       "25   gcn     LH_ASDNC     NaN    0     0   \n",
       "26   gcn     LH_ASDNC     NaN    5     1   \n",
       "27   gcn     LH_ASDNC     NaN    1     4   \n",
       "28   gcn     LH_ASDNC     NaN    4     3   \n",
       "29   gcn     LH_ASDNC     NaN    2     4   \n",
       "\n",
       "                                              weights labels preds  \\\n",
       "0   [0.16066833, 0.13505433, 0.15165433, -0.060330...    NaN   NaN   \n",
       "1   [0.15732002, 0.13605203, 0.1512491, -0.0636897...    NaN   NaN   \n",
       "2   [0.15922722, 0.15374441, 0.17657454, -0.061664...    NaN   NaN   \n",
       "3   [0.16292083, 0.13645568, 0.144762, -0.05808449...    NaN   NaN   \n",
       "4   [0.16302794, 0.13638008, 0.14201143, -0.057977...    NaN   NaN   \n",
       "5   [0.16094077, 0.13922432, 0.14928113, -0.059993...    NaN   NaN   \n",
       "6   [0.16348527, 0.13709262, 0.14959766, -0.057465...    NaN   NaN   \n",
       "7   [0.15837951, 0.13525918, 0.15121678, -0.062630...    NaN   NaN   \n",
       "8   [0.16344443, 0.13798764, 0.14870547, -0.057564...    NaN   NaN   \n",
       "9   [0.16864496, 0.16324726, 0.1731666, -0.0521947...    NaN   NaN   \n",
       "10  [0.15780546, 0.13712685, 0.13740453, -0.063061...    NaN   NaN   \n",
       "11  [0.15822014, 0.13861144, 0.15274994, -0.062784...    NaN   NaN   \n",
       "12  [0.16152766, 0.14119557, 0.15068255, -0.059480...    NaN   NaN   \n",
       "13  [0.1643773, 0.13279654, 0.14943255, -0.0564641...    NaN   NaN   \n",
       "14  [0.16247007, 0.13601503, 0.14947107, -0.058517...    NaN   NaN   \n",
       "15  [0.15714587, 0.13768107, 0.14977053, -0.063779...    NaN   NaN   \n",
       "16  [0.15896358, 0.13792183, 0.15179127, -0.062042...    NaN   NaN   \n",
       "17  [0.16587204, 0.1291279, 0.13736813, -0.0551098...    NaN   NaN   \n",
       "18  [0.15837501, 0.1356627, 0.12671615, -0.0626011...    NaN   NaN   \n",
       "19  [0.18261975, 0.12798643, 0.14506856, -0.038341...    NaN   NaN   \n",
       "20  [0.15242025, 0.13071504, 0.1492608, -0.0685871...    NaN   NaN   \n",
       "21  [0.1584692, 0.13599494, 0.15081663, -0.0625404...    NaN   NaN   \n",
       "22  [0.15982582, 0.13770749, 0.15005179, -0.061138...    NaN   NaN   \n",
       "23  [0.15852232, 0.1370207, 0.14746836, -0.0624634...    NaN   NaN   \n",
       "24  [0.1609174, 0.13063934, 0.13813792, -0.0600783...    NaN   NaN   \n",
       "25  [0.14357023, 0.119312726, 0.14590594, -0.07743...    NaN   NaN   \n",
       "26  [0.1615522, 0.14057843, 0.15191266, -0.0594561...    NaN   NaN   \n",
       "27  [0.16378255, 0.13800617, 0.14994726, -0.057226...    NaN   NaN   \n",
       "28  [0.16348529, 0.13356523, 0.14213037, -0.057520...    NaN   NaN   \n",
       "29  [0.15930878, 0.13866591, 0.15225281, -0.061695...    NaN   NaN   \n",
       "\n",
       "                                         train_labels  \\\n",
       "0   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "1   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "2   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "3   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "4   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "5   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "6   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "7   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "8   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "9   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "10  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "11  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "12  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "13  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "14  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "15  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "16  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "17  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "18  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "19  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "20  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "21  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "22  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "23  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "24  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "25  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "26  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "27  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "28  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "29  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "\n",
       "                                          train_preds  \\\n",
       "0   [0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, ...   \n",
       "1   [0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, ...   \n",
       "2   [0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, ...   \n",
       "3   [0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, ...   \n",
       "4   [1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, ...   \n",
       "5   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "6   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "7   [0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, ...   \n",
       "8   [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, ...   \n",
       "9   [0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, ...   \n",
       "10  [0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, ...   \n",
       "11  [0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, ...   \n",
       "12  [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "13  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "14  [1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, ...   \n",
       "15  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "16  [1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, ...   \n",
       "17  [1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, ...   \n",
       "18  [0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, ...   \n",
       "19  [1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, ...   \n",
       "20  [0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, ...   \n",
       "21  [0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, ...   \n",
       "22  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "23  [0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, ...   \n",
       "24  [1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "25  [0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, ...   \n",
       "26  [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "27  [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, ...   \n",
       "28  [0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, ...   \n",
       "29  [0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, ...   \n",
       "\n",
       "                                          test_labels  \\\n",
       "0   [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "1   [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "2   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "3   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "4   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "5   [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "6   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "7   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "8   [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "9   [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "10  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "11  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "12  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "13  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "14  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "15  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "16  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "17  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "18  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "19  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "20  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "21  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "22  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "23  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "24  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "25  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "26  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "27  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "28  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "29  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "\n",
       "                                           test_preds  \\\n",
       "0   [0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, ...   \n",
       "1   [1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, ...   \n",
       "2   [0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, ...   \n",
       "3   [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, ...   \n",
       "4   [1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, ...   \n",
       "5   [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...   \n",
       "6   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "7   [0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, ...   \n",
       "8   [1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, ...   \n",
       "9   [1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, ...   \n",
       "10  [0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "11  [1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, ...   \n",
       "12  [0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, ...   \n",
       "13  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "14  [0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...   \n",
       "15  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, ...   \n",
       "16  [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, ...   \n",
       "17  [1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, ...   \n",
       "18  [1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, ...   \n",
       "19  [0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, ...   \n",
       "20  [1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, ...   \n",
       "21  [0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, ...   \n",
       "22  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "23  [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, ...   \n",
       "24  [0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, ...   \n",
       "25  [1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, ...   \n",
       "26  [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, ...   \n",
       "27  [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "28  [1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, ...   \n",
       "29  [1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "\n",
       "                                                    w  \\\n",
       "0   [0.16066833, 0.13505433, 0.15165433, 0.0603303...   \n",
       "1   [0.15732002, 0.13605203, 0.1512491, 0.06368978...   \n",
       "2   [0.15922722, 0.15374441, 0.17657454, 0.0616645...   \n",
       "3   [0.16292083, 0.13645568, 0.144762, 0.058084495...   \n",
       "4   [0.16302794, 0.13638008, 0.14201143, 0.0579771...   \n",
       "5   [0.16094077, 0.13922432, 0.14928113, 0.0599935...   \n",
       "6   [0.16348527, 0.13709262, 0.14959766, 0.0574652...   \n",
       "7   [0.15837951, 0.13525918, 0.15121678, 0.0626301...   \n",
       "8   [0.16344443, 0.13798764, 0.14870547, 0.0575646...   \n",
       "9   [0.16864496, 0.16324726, 0.1731666, 0.05219473...   \n",
       "10  [0.15780546, 0.13712685, 0.13740453, 0.0630613...   \n",
       "11  [0.15822014, 0.13861144, 0.15274994, 0.0627842...   \n",
       "12  [0.16152766, 0.14119557, 0.15068255, 0.0594808...   \n",
       "13  [0.1643773, 0.13279654, 0.14943255, 0.05646418...   \n",
       "14  [0.16247007, 0.13601503, 0.14947107, 0.0585173...   \n",
       "15  [0.15714587, 0.13768107, 0.14977053, 0.0637793...   \n",
       "16  [0.15896358, 0.13792183, 0.15179127, 0.0620425...   \n",
       "17  [0.16587204, 0.1291279, 0.13736813, 0.05510981...   \n",
       "18  [0.15837501, 0.1356627, 0.12671615, 0.06260115...   \n",
       "19  [0.18261975, 0.12798643, 0.14506856, 0.0383411...   \n",
       "20  [0.15242025, 0.13071504, 0.1492608, 0.06858719...   \n",
       "21  [0.1584692, 0.13599494, 0.15081663, 0.06254046...   \n",
       "22  [0.15982582, 0.13770749, 0.15005179, 0.0611384...   \n",
       "23  [0.15852232, 0.1370207, 0.14746836, 0.06246343...   \n",
       "24  [0.1609174, 0.13063934, 0.13813792, 0.06007838...   \n",
       "25  [0.14357023, 0.119312726, 0.14590594, 0.077431...   \n",
       "26  [0.1615522, 0.14057843, 0.15191266, 0.05945617...   \n",
       "27  [0.16378255, 0.13800617, 0.14994726, 0.0572266...   \n",
       "28  [0.16348529, 0.13356523, 0.14213037, 0.0575201...   \n",
       "29  [0.15930878, 0.13866591, 0.15225281, 0.0616956...   \n",
       "\n",
       "                                                    n  \n",
       "0   [0.838708, 0.704667, 0.7915367, 0.3136275, 0.3...  \n",
       "1   [0.82118595, 0.70988804, 0.7894161, 0.3312079,...  \n",
       "2   [0.8311665, 0.8024744, 0.9219471, 0.32060942, ...  \n",
       "3   [0.85049564, 0.7120004, 0.7554683, 0.30187476,...  \n",
       "4   [0.85105616, 0.7116047, 0.74107426, 0.30131298...  \n",
       "5   [0.8401337, 0.726489, 0.77911747, 0.3118649, 0...  \n",
       "6   [0.8534494, 0.7153336, 0.7807739, 0.298634, 0....  \n",
       "7   [0.8267304, 0.70573896, 0.7892469, 0.32566294,...  \n",
       "8   [0.85323566, 0.7200173, 0.7761049, 0.2991544, ...  \n",
       "9   [0.88045067, 0.85220385, 0.90411294, 0.2710529...  \n",
       "10  [0.8237263, 0.7155127, 0.71696585, 0.32791907,...  \n",
       "11  [0.8258964, 0.7232817, 0.7972702, 0.3264692, 0...  \n",
       "12  [0.84320503, 0.7368047, 0.7864513, 0.30918205,...  \n",
       "13  [0.8581175, 0.69285166, 0.7799099, 0.29339546,...  \n",
       "14  [0.8481367, 0.7096944, 0.7801115, 0.3041401, 0...  \n",
       "15  [0.8202746, 0.718413, 0.78167856, 0.3316765, 0...  \n",
       "16  [0.82978684, 0.71967286, 0.7922533, 0.3225877,...  \n",
       "17  [0.8659396, 0.67365324, 0.7167753, 0.28630793,...  \n",
       "18  [0.8267068, 0.70785064, 0.66103226, 0.32551095...  \n",
       "19  [0.95358235, 0.6676798, 0.75707257, 0.19855566...  \n",
       "20  [0.79554486, 0.681959, 0.7790111, 0.35683662, ...  \n",
       "21  [0.8271997, 0.7095893, 0.78715295, 0.32519335,...  \n",
       "22  [0.834299, 0.7185512, 0.7831504, 0.31785655, 0...  \n",
       "23  [0.8274777, 0.71495724, 0.769631, 0.32479024, ...  \n",
       "24  [0.8400114, 0.6815628, 0.72080374, 0.312309, 0...  \n",
       "25  [0.74923164, 0.6222893, 0.7614547, 0.40312007,...  \n",
       "26  [0.8433334, 0.7335752, 0.7928886, 0.3090529, 0...  \n",
       "27  [0.8550051, 0.72011423, 0.7826034, 0.29738545,...  \n",
       "28  [0.85344946, 0.6968743, 0.7416967, 0.2989216, ...  \n",
       "29  [0.83159333, 0.7235668, 0.7946686, 0.32077214,...  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gcn = GetModelDf(\"gcn\")\n",
    "gcn_datasets = GetDatasetsByView(df_gcn)\n",
    "df_gcn#.sort_values(by=['cv_no'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "910be8f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>dataset</th>\n",
       "      <th>view</th>\n",
       "      <th>cv_no</th>\n",
       "      <th>weights</th>\n",
       "      <th>labels</th>\n",
       "      <th>preds</th>\n",
       "      <th>train_labels</th>\n",
       "      <th>train_preds</th>\n",
       "      <th>test_labels</th>\n",
       "      <th>test_preds</th>\n",
       "      <th>w</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>[-0.0029166692, -0.0062247044, 0.0037626843, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, ...</td>\n",
       "      <td>[0.0029166692, 0.0062247044, 0.0037626843, 0.0...</td>\n",
       "      <td>[0.13565315, 0.2898861, 0.17509753, 0.20213477...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.005145098, -0.0068195397, 0.0011850551, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.005145098, 0.0068195397, 0.0011850551, 0.00...</td>\n",
       "      <td>[0.23955081, 0.31761953, 0.05491884, 0.1272695...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>[-0.0060435925, -0.008809334, 0.0016019796, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.0060435925, 0.008809334, 0.0016019796, 0.00...</td>\n",
       "      <td>[0.281442, 0.41039118, 0.07435742, 0.11634573,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>[-0.0063220286, -0.008333623, -0.000533672, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, ...</td>\n",
       "      <td>[0.0063220286, 0.008333623, 0.000533672, 0.001...</td>\n",
       "      <td>[0.2944237, 0.3882118, 0.024548933, 0.06429927...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>[-0.0020681017, -0.008586129, 0.0029449721, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[0.0020681017, 0.008586129, 0.0029449721, 0.00...</td>\n",
       "      <td>[0.096089765, 0.39998454, 0.13697274, 0.111257...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>[-0.003797527, -0.0057990015, 0.0026738192, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.003797527, 0.0057990015, 0.0026738192, 0.00...</td>\n",
       "      <td>[0.17672202, 0.27003825, 0.12433058, 0.1606735...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>[-0.00041077472, -0.0038210708, 0.005822016, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.00041077472, 0.0038210708, 0.005822016, 0.0...</td>\n",
       "      <td>[0.018819004, 0.17781973, 0.27111125, 0.356099...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>[-0.005519814, -0.00743053, 0.0023314713, 0.00...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0.005519814, 0.00743053, 0.0023314713, 0.0027...</td>\n",
       "      <td>[0.25702146, 0.3461062, 0.108369045, 0.1257673...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[-0.004585604, -0.007636104, 5.4586446e-05, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, ...</td>\n",
       "      <td>[0.004585604, 0.007636104, 5.4586446e-05, 0.00...</td>\n",
       "      <td>[0.21346512, 0.3556908, 0.0022121773, 0.136847...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0009810537, -0.0028527197, 0.004825263, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.0009810537, 0.0028527197, 0.004825263, 0.00...</td>\n",
       "      <td>[0.045407537, 0.1326716, 0.22463891, 0.2207488...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>[-0.0036144685, -0.00604772, 0.0021207675, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[0.0036144685, 0.00604772, 0.0021207675, 0.003...</td>\n",
       "      <td>[0.16818716, 0.28163442, 0.098545246, 0.163732...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.0057675773, -0.0072187334, 0.0009133383, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, ...</td>\n",
       "      <td>[0.0057675773, 0.0072187334, 0.0009133383, 0.0...</td>\n",
       "      <td>[0.26857314, 0.33623144, 0.042250395, 0.127481...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>[-0.002657703, -0.005554449, 0.0022467666, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, ...</td>\n",
       "      <td>[0.002657703, 0.005554449, 0.0022467666, 0.004...</td>\n",
       "      <td>[0.123579174, 0.25863627, 0.10441979, 0.209696...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>[-0.0010166403, -0.004328287, 0.0047224197, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, ...</td>\n",
       "      <td>[0.0010166403, 0.004328287, 0.0047224197, 0.00...</td>\n",
       "      <td>[0.047066715, 0.20146805, 0.21984397, 0.301937...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>[-0.005483614, -0.008129545, -7.138995e-06, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, ...</td>\n",
       "      <td>[0.005483614, 0.008129545, 7.138995e-06, 0.002...</td>\n",
       "      <td>[0.2553337, 0.37869686, 0.0, 0.10213039, 0.389...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[-0.0044927225, -0.00569662, 0.0033310056, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.0044927225, 0.00569662, 0.0033310056, 0.002...</td>\n",
       "      <td>[0.20913464, 0.2652648, 0.15497106, 0.11937872...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[-0.006192635, -0.007898917, 0.00080107665, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, ...</td>\n",
       "      <td>[0.006192635, 0.007898917, 0.00080107665, 0.00...</td>\n",
       "      <td>[0.2883909, 0.36794418, 0.037016336, 0.0962545...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>[-0.00035886606, -0.0037141524, 0.005754319, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, ...</td>\n",
       "      <td>[0.00035886606, 0.0037141524, 0.005754319, 0.0...</td>\n",
       "      <td>[0.016398828, 0.1728348, 0.26795498, 0.3198185...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>[-0.0054171276, -0.007382497, 0.0011290761, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[0.0054171276, 0.007382497, 0.0011290761, 0.00...</td>\n",
       "      <td>[0.25223386, 0.3438667, 0.05230889, 0.13365507...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.0006975462, -0.005658211, 0.0043981024, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0.0006975462, 0.005658211, 0.0043981024, 0.00...</td>\n",
       "      <td>[0.032189358, 0.26347408, 0.20472309, 0.238198...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.006575159, -0.008525202, -0.00065370224, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.006575159, 0.008525202, 0.00065370224, 0.00...</td>\n",
       "      <td>[0.3062256, 0.3971439, 0.03014519, 0.10333351,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>[-0.004787416, -0.008523967, -0.00062834175, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0.004787416, 0.008523967, 0.00062834175, 0.00...</td>\n",
       "      <td>[0.22287436, 0.39708632, 0.028962791, 0.144446...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>[-0.004236703, -0.0060848114, 0.002284678, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0.004236703, 0.0060848114, 0.002284678, 0.003...</td>\n",
       "      <td>[0.19719805, 0.28336376, 0.10618736, 0.1851842...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>[-0.0057786894, -0.00823538, 0.0013626656, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0.0057786894, 0.00823538, 0.0013626656, 0.002...</td>\n",
       "      <td>[0.26909122, 0.38363132, 0.06319971, 0.1115829...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>[-0.006035719, -0.0077517154, 6.38839e-05, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, ...</td>\n",
       "      <td>[0.006035719, 0.0077517154, 6.38839e-05, 0.002...</td>\n",
       "      <td>[0.28107488, 0.36108103, 0.002645659, 0.116305...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>[-0.0029497698, -0.005293661, 0.0031222668, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0.0029497698, 0.005293661, 0.0031222668, 0.00...</td>\n",
       "      <td>[0.13719642, 0.24647737, 0.14523888, 0.2491804...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.0059949234, -0.008597415, -0.0006942473, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.0059949234, 0.008597415, 0.0006942473, 0.00...</td>\n",
       "      <td>[0.27917284, 0.40051073, 0.032035552, 0.109785...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.0031113247, -0.0056898003, 0.0018527659, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, ...</td>\n",
       "      <td>[0.0031113247, 0.0056898003, 0.0018527659, 0.0...</td>\n",
       "      <td>[0.14472872, 0.26494688, 0.08605001, 0.1564426...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.0025484022, -0.0065051853, 0.0019366695, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.0025484022, 0.0065051853, 0.0019366695, 0.0...</td>\n",
       "      <td>[0.11848317, 0.30296317, 0.08996191, 0.2742476...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>diffpool</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>[-0.0068919584, -0.009452552, -0.00010372553, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0.0068919584, 0.009452552, 0.00010372553, 0.0...</td>\n",
       "      <td>[0.32099596, 0.4403804, 0.0045032245, 0.073725...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model dataset_type dataset view cv_no  \\\n",
       "0   diffpool     LH_ASDNC     NaN    2     4   \n",
       "1   diffpool     LH_ASDNC     NaN    2     0   \n",
       "2   diffpool     LH_ASDNC     NaN    0     1   \n",
       "3   diffpool     LH_ASDNC     NaN    1     4   \n",
       "4   diffpool     LH_ASDNC     NaN    4     3   \n",
       "5   diffpool     LH_ASDNC     NaN    2     2   \n",
       "6   diffpool     LH_ASDNC     NaN    5     2   \n",
       "7   diffpool     LH_ASDNC     NaN    3     4   \n",
       "8   diffpool     LH_ASDNC     NaN    1     1   \n",
       "9   diffpool     LH_ASDNC     NaN    4     2   \n",
       "10  diffpool     LH_ASDNC     NaN    2     3   \n",
       "11  diffpool     LH_ASDNC     NaN    3     0   \n",
       "12  diffpool     LH_ASDNC     NaN    4     1   \n",
       "13  diffpool     LH_ASDNC     NaN    5     1   \n",
       "14  diffpool     LH_ASDNC     NaN    1     3   \n",
       "15  diffpool     LH_ASDNC     NaN    2     1   \n",
       "16  diffpool     LH_ASDNC     NaN    0     2   \n",
       "17  diffpool     LH_ASDNC     NaN    5     4   \n",
       "18  diffpool     LH_ASDNC     NaN    3     3   \n",
       "19  diffpool     LH_ASDNC     NaN    4     4   \n",
       "20  diffpool     LH_ASDNC     NaN    0     0   \n",
       "21  diffpool     LH_ASDNC     NaN    1     2   \n",
       "22  diffpool     LH_ASDNC     NaN    3     1   \n",
       "23  diffpool     LH_ASDNC     NaN    0     4   \n",
       "24  diffpool     LH_ASDNC     NaN    0     3   \n",
       "25  diffpool     LH_ASDNC     NaN    5     3   \n",
       "26  diffpool     LH_ASDNC     NaN    1     0   \n",
       "27  diffpool     LH_ASDNC     NaN    4     0   \n",
       "28  diffpool     LH_ASDNC     NaN    5     0   \n",
       "29  diffpool     LH_ASDNC     NaN    3     2   \n",
       "\n",
       "                                              weights labels preds  \\\n",
       "0   [-0.0029166692, -0.0062247044, 0.0037626843, 0...    NaN   NaN   \n",
       "1   [-0.005145098, -0.0068195397, 0.0011850551, 0....    NaN   NaN   \n",
       "2   [-0.0060435925, -0.008809334, 0.0016019796, 0....    NaN   NaN   \n",
       "3   [-0.0063220286, -0.008333623, -0.000533672, 0....    NaN   NaN   \n",
       "4   [-0.0020681017, -0.008586129, 0.0029449721, 0....    NaN   NaN   \n",
       "5   [-0.003797527, -0.0057990015, 0.0026738192, 0....    NaN   NaN   \n",
       "6   [-0.00041077472, -0.0038210708, 0.005822016, 0...    NaN   NaN   \n",
       "7   [-0.005519814, -0.00743053, 0.0023314713, 0.00...    NaN   NaN   \n",
       "8   [-0.004585604, -0.007636104, 5.4586446e-05, 0....    NaN   NaN   \n",
       "9   [0.0009810537, -0.0028527197, 0.004825263, 0.0...    NaN   NaN   \n",
       "10  [-0.0036144685, -0.00604772, 0.0021207675, 0.0...    NaN   NaN   \n",
       "11  [-0.0057675773, -0.0072187334, 0.0009133383, 0...    NaN   NaN   \n",
       "12  [-0.002657703, -0.005554449, 0.0022467666, 0.0...    NaN   NaN   \n",
       "13  [-0.0010166403, -0.004328287, 0.0047224197, 0....    NaN   NaN   \n",
       "14  [-0.005483614, -0.008129545, -7.138995e-06, 0....    NaN   NaN   \n",
       "15  [-0.0044927225, -0.00569662, 0.0033310056, 0.0...    NaN   NaN   \n",
       "16  [-0.006192635, -0.007898917, 0.00080107665, 0....    NaN   NaN   \n",
       "17  [-0.00035886606, -0.0037141524, 0.005754319, 0...    NaN   NaN   \n",
       "18  [-0.0054171276, -0.007382497, 0.0011290761, 0....    NaN   NaN   \n",
       "19  [0.0006975462, -0.005658211, 0.0043981024, 0.0...    NaN   NaN   \n",
       "20  [-0.006575159, -0.008525202, -0.00065370224, 0...    NaN   NaN   \n",
       "21  [-0.004787416, -0.008523967, -0.00062834175, 0...    NaN   NaN   \n",
       "22  [-0.004236703, -0.0060848114, 0.002284678, 0.0...    NaN   NaN   \n",
       "23  [-0.0057786894, -0.00823538, 0.0013626656, 0.0...    NaN   NaN   \n",
       "24  [-0.006035719, -0.0077517154, 6.38839e-05, 0.0...    NaN   NaN   \n",
       "25  [-0.0029497698, -0.005293661, 0.0031222668, 0....    NaN   NaN   \n",
       "26  [-0.0059949234, -0.008597415, -0.0006942473, 0...    NaN   NaN   \n",
       "27  [-0.0031113247, -0.0056898003, 0.0018527659, 0...    NaN   NaN   \n",
       "28  [-0.0025484022, -0.0065051853, 0.0019366695, 0...    NaN   NaN   \n",
       "29  [-0.0068919584, -0.009452552, -0.00010372553, ...    NaN   NaN   \n",
       "\n",
       "                                         train_labels  \\\n",
       "0   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "1   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "2   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "3   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "4   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "5   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "6   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "7   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "8   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "9   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "10  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "11  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "12  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "13  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "14  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "15  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "16  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "17  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "18  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "19  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "20  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "21  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "22  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "23  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "24  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "25  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "26  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "27  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "28  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "29  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "\n",
       "                                          train_preds  \\\n",
       "0   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "1   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, ...   \n",
       "2   [0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "3   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "4   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "5   [0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "6   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "7   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "8   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "9   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "10  [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "11  [1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, ...   \n",
       "12  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "13  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "14  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "15  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "16  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "17  [0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, ...   \n",
       "18  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "19  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "20  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...   \n",
       "21  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "22  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "23  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "24  [1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "25  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "26  [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, ...   \n",
       "27  [0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, ...   \n",
       "28  [1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, ...   \n",
       "29  [0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "\n",
       "                                          test_labels  \\\n",
       "0   [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "1   [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "2   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "3   [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "4   [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "5   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "6   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "7   [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "8   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "9   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "10  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "11  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "12  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "13  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "14  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "15  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "16  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "17  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "18  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "19  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "20  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "21  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "22  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "23  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "24  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "25  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "26  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "27  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "28  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "29  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "\n",
       "                                           test_preds  \\\n",
       "0   [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, ...   \n",
       "1   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "2   [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...   \n",
       "3   [1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, ...   \n",
       "4   [1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, ...   \n",
       "5   [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...   \n",
       "6   [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "7   [1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "8   [1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, ...   \n",
       "9   [1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, ...   \n",
       "10  [1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, ...   \n",
       "11  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, ...   \n",
       "12  [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, ...   \n",
       "13  [0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, ...   \n",
       "14  [1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, ...   \n",
       "15  [0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, ...   \n",
       "16  [1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, ...   \n",
       "17  [1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, ...   \n",
       "18  [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, ...   \n",
       "19  [1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, ...   \n",
       "20  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "21  [0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, ...   \n",
       "22  [0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "23  [0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, ...   \n",
       "24  [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, ...   \n",
       "25  [1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, ...   \n",
       "26  [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, ...   \n",
       "27  [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, ...   \n",
       "28  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, ...   \n",
       "29  [1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, ...   \n",
       "\n",
       "                                                    w  \\\n",
       "0   [0.0029166692, 0.0062247044, 0.0037626843, 0.0...   \n",
       "1   [0.005145098, 0.0068195397, 0.0011850551, 0.00...   \n",
       "2   [0.0060435925, 0.008809334, 0.0016019796, 0.00...   \n",
       "3   [0.0063220286, 0.008333623, 0.000533672, 0.001...   \n",
       "4   [0.0020681017, 0.008586129, 0.0029449721, 0.00...   \n",
       "5   [0.003797527, 0.0057990015, 0.0026738192, 0.00...   \n",
       "6   [0.00041077472, 0.0038210708, 0.005822016, 0.0...   \n",
       "7   [0.005519814, 0.00743053, 0.0023314713, 0.0027...   \n",
       "8   [0.004585604, 0.007636104, 5.4586446e-05, 0.00...   \n",
       "9   [0.0009810537, 0.0028527197, 0.004825263, 0.00...   \n",
       "10  [0.0036144685, 0.00604772, 0.0021207675, 0.003...   \n",
       "11  [0.0057675773, 0.0072187334, 0.0009133383, 0.0...   \n",
       "12  [0.002657703, 0.005554449, 0.0022467666, 0.004...   \n",
       "13  [0.0010166403, 0.004328287, 0.0047224197, 0.00...   \n",
       "14  [0.005483614, 0.008129545, 7.138995e-06, 0.002...   \n",
       "15  [0.0044927225, 0.00569662, 0.0033310056, 0.002...   \n",
       "16  [0.006192635, 0.007898917, 0.00080107665, 0.00...   \n",
       "17  [0.00035886606, 0.0037141524, 0.005754319, 0.0...   \n",
       "18  [0.0054171276, 0.007382497, 0.0011290761, 0.00...   \n",
       "19  [0.0006975462, 0.005658211, 0.0043981024, 0.00...   \n",
       "20  [0.006575159, 0.008525202, 0.00065370224, 0.00...   \n",
       "21  [0.004787416, 0.008523967, 0.00062834175, 0.00...   \n",
       "22  [0.004236703, 0.0060848114, 0.002284678, 0.003...   \n",
       "23  [0.0057786894, 0.00823538, 0.0013626656, 0.002...   \n",
       "24  [0.006035719, 0.0077517154, 6.38839e-05, 0.002...   \n",
       "25  [0.0029497698, 0.005293661, 0.0031222668, 0.00...   \n",
       "26  [0.0059949234, 0.008597415, 0.0006942473, 0.00...   \n",
       "27  [0.0031113247, 0.0056898003, 0.0018527659, 0.0...   \n",
       "28  [0.0025484022, 0.0065051853, 0.0019366695, 0.0...   \n",
       "29  [0.0068919584, 0.009452552, 0.00010372553, 0.0...   \n",
       "\n",
       "                                                    n  \n",
       "0   [0.13565315, 0.2898861, 0.17509753, 0.20213477...  \n",
       "1   [0.23955081, 0.31761953, 0.05491884, 0.1272695...  \n",
       "2   [0.281442, 0.41039118, 0.07435742, 0.11634573,...  \n",
       "3   [0.2944237, 0.3882118, 0.024548933, 0.06429927...  \n",
       "4   [0.096089765, 0.39998454, 0.13697274, 0.111257...  \n",
       "5   [0.17672202, 0.27003825, 0.12433058, 0.1606735...  \n",
       "6   [0.018819004, 0.17781973, 0.27111125, 0.356099...  \n",
       "7   [0.25702146, 0.3461062, 0.108369045, 0.1257673...  \n",
       "8   [0.21346512, 0.3556908, 0.0022121773, 0.136847...  \n",
       "9   [0.045407537, 0.1326716, 0.22463891, 0.2207488...  \n",
       "10  [0.16818716, 0.28163442, 0.098545246, 0.163732...  \n",
       "11  [0.26857314, 0.33623144, 0.042250395, 0.127481...  \n",
       "12  [0.123579174, 0.25863627, 0.10441979, 0.209696...  \n",
       "13  [0.047066715, 0.20146805, 0.21984397, 0.301937...  \n",
       "14  [0.2553337, 0.37869686, 0.0, 0.10213039, 0.389...  \n",
       "15  [0.20913464, 0.2652648, 0.15497106, 0.11937872...  \n",
       "16  [0.2883909, 0.36794418, 0.037016336, 0.0962545...  \n",
       "17  [0.016398828, 0.1728348, 0.26795498, 0.3198185...  \n",
       "18  [0.25223386, 0.3438667, 0.05230889, 0.13365507...  \n",
       "19  [0.032189358, 0.26347408, 0.20472309, 0.238198...  \n",
       "20  [0.3062256, 0.3971439, 0.03014519, 0.10333351,...  \n",
       "21  [0.22287436, 0.39708632, 0.028962791, 0.144446...  \n",
       "22  [0.19719805, 0.28336376, 0.10618736, 0.1851842...  \n",
       "23  [0.26909122, 0.38363132, 0.06319971, 0.1115829...  \n",
       "24  [0.28107488, 0.36108103, 0.002645659, 0.116305...  \n",
       "25  [0.13719642, 0.24647737, 0.14523888, 0.2491804...  \n",
       "26  [0.27917284, 0.40051073, 0.032035552, 0.109785...  \n",
       "27  [0.14472872, 0.26494688, 0.08605001, 0.1564426...  \n",
       "28  [0.11848317, 0.30296317, 0.08996191, 0.2742476...  \n",
       "29  [0.32099596, 0.4403804, 0.0045032245, 0.073725...  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_diffpool = GetModelDf(\"diffpool\")\n",
    "diffpool_datasets = GetDatasetsByView(df_diffpool)\n",
    "df_diffpool#.sort_values(by=['cv_no'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5e9da2ef",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>dataset</th>\n",
       "      <th>view</th>\n",
       "      <th>cv_no</th>\n",
       "      <th>weights</th>\n",
       "      <th>labels</th>\n",
       "      <th>preds</th>\n",
       "      <th>train_labels</th>\n",
       "      <th>train_preds</th>\n",
       "      <th>test_labels</th>\n",
       "      <th>test_preds</th>\n",
       "      <th>w</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.7936466, -0.576373, -0.7680454, -0.87525296...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.7936466, 0.576373, 0.7680454, 0.87525296, 2...</td>\n",
       "      <td>[0.2532239, 0.18357389, 0.2450171, 0.27938393,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.3839899, -0.76080054, -2.6513946, -0.59395...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.3839899, 0.76080054, 2.6513946, 0.5939501, ...</td>\n",
       "      <td>[0.12190287, 0.24269466, 0.84875035, 0.1892084...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[-0.8047803, -0.15785794, -1.2666754, -1.63858...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.8047803, 0.15785794, 1.2666754, 1.6385876, ...</td>\n",
       "      <td>[0.25679296, 0.049413193, 0.4048597, 0.5240812...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>[-0.85870034, 0.7786339, -0.54228014, -0.81213...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.85870034, 0.7786339, 0.54228014, 0.8121369,...</td>\n",
       "      <td>[0.27407777, 0.24841139, 0.17264497, 0.2591512...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>[-2.0228937, -1.5795254, -0.066711344, -1.7795...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[2.0228937, 1.5795254, 0.066711344, 1.7795426,...</td>\n",
       "      <td>[0.6472758, 0.505148, 0.020194909, 0.56926626,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>[-0.88142085, 0.060984373, -0.38065705, -0.808...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.88142085, 0.060984373, 0.38065705, 0.808803...</td>\n",
       "      <td>[0.28136113, 0.018359052, 0.12083448, 0.258082...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>[-0.6182384, 0.31137642, 0.087907195, -0.73404...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.6182384, 0.31137642, 0.087907195, 0.7340436...</td>\n",
       "      <td>[0.19699442, 0.09862563, 0.026989527, 0.234117...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>[-2.271541, 0.0037132103, -0.4133792, -0.94544...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[2.271541, 0.0037132103, 0.4133792, 0.945443, ...</td>\n",
       "      <td>[0.72698313, 0.0, 0.13132401, 0.3018843, 0.227...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>[-0.12964441, -0.096764006, -0.100969315, -0.1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.12964441, 0.096764006, 0.100969315, 0.13016...</td>\n",
       "      <td>[0.040368963, 0.029828697, 0.031176768, 0.0405...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.85134214, -0.8951563, -0.9114397, -0.653786...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.85134214, 0.8951563, 0.9114397, 0.653786, 0...</td>\n",
       "      <td>[0.271719, 0.28576422, 0.2909841, 0.20838968, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.18301475, -0.6264444, -1.9211636, -0.58567...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.18301475, 0.6264444, 1.9211636, 0.58567375,...</td>\n",
       "      <td>[0.057477552, 0.19962497, 0.6146648, 0.1865553...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>[-1.7513441, -0.27948096, -0.39311448, -0.3180...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[1.7513441, 0.27948096, 0.39311448, 0.31807315...</td>\n",
       "      <td>[0.56022686, 0.0884011, 0.12482788, 0.10077236...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>[-0.20153983, -0.6479829, -0.74934393, -0.7093...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.20153983, 0.6479829, 0.74934393, 0.70935374...</td>\n",
       "      <td>[0.06341602, 0.20652942, 0.23902209, 0.2262026...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>[-1.2378198, -1.4392046, -0.37824976, -0.57844...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[1.2378198, 1.4392046, 0.37824976, 0.5784436, ...</td>\n",
       "      <td>[0.39560965, 0.46016628, 0.12006279, 0.1842376...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>[-1.5413958, -0.9371311, -1.5738192, 0.0717039...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[1.5413958, 0.9371311, 1.5738192, 0.07170399, ...</td>\n",
       "      <td>[0.49292508, 0.29921982, 0.50331885, 0.0217953...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[-1.4483935, -0.7050891, -2.0286963, -1.348254...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[1.4483935, 0.7050891, 2.0286963, 1.3482548, 0...</td>\n",
       "      <td>[0.4631119, 0.22483559, 0.64913595, 0.4310111,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.38105837, 0.64761645, -1.0781126, -0.288224...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.38105837, 0.64761645, 1.0781126, 0.28822473...</td>\n",
       "      <td>[0.12096313, 0.20641196, 0.34441334, 0.0912040...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.18198545, -0.6597841, -1.5256075, -0.143518...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.18198545, 0.6597841, 1.5256075, 0.1435181, ...</td>\n",
       "      <td>[0.057147596, 0.21031246, 0.4878639, 0.0448163...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>[-1.0592666, -0.097429216, 0.01773572, -0.5135...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[1.0592666, 0.097429216, 0.01773572, 0.5135835...</td>\n",
       "      <td>[0.338372, 0.03004194, 0.004495106, 0.16344588...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>[-0.25313136, 0.029231826, -0.71386105, -1.275...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.25313136, 0.029231826, 0.71386105, 1.275749...</td>\n",
       "      <td>[0.079954386, 0.00818034, 0.22764756, 0.407768...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.5120717, -0.14325662, 0.52214026, 0.8745692...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.5120717, 0.14325662, 0.52214026, 0.8745692,...</td>\n",
       "      <td>[0.16296124, 0.04473254, 0.16618885, 0.2791647...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.05543685, -0.93763906, -0.79282665, 0.12342...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0.05543685, 0.93763906, 0.79282665, 0.1234248...</td>\n",
       "      <td>[0.016580718, 0.29938266, 0.25296107, 0.038375...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[-1.0789748, -2.8108594, -1.4514246, -0.973314...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[1.0789748, 2.8108594, 1.4514246, 0.9733145, 1...</td>\n",
       "      <td>[0.34468973, 0.89986897, 0.46408358, 0.3108189...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>[-1.0034512, -1.2424487, -0.6539394, 1.5061889...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, ...</td>\n",
       "      <td>[1.0034512, 1.2424487, 0.6539394, 1.5061889, 1...</td>\n",
       "      <td>[0.32047963, 0.3970935, 0.20843887, 0.48163903...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>[-1.1101376, -1.9952235, 0.435011, -0.22167659...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[1.1101376, 1.9952235, 0.435011, 0.22167659, 0...</td>\n",
       "      <td>[0.35467938, 0.63840574, 0.13825837, 0.0698711...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>[-2.4483228, -0.8982536, -0.11262385, 0.891669...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[2.4483228, 0.8982536, 0.11262385, 0.8916693, ...</td>\n",
       "      <td>[0.7836529, 0.2867571, 0.034912787, 0.28464642...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.91749936, -0.57201684, 0.21181762, -0.7913...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.91749936, 0.57201684, 0.21181762, 0.7913403...</td>\n",
       "      <td>[0.2929266, 0.18217747, 0.0667107, 0.25248462,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.026674032, 0.28386125, -0.2624103, -0.48332...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.026674032, 0.28386125, 0.2624103, 0.4833296...</td>\n",
       "      <td>[0.007360404, 0.08980527, 0.08292887, 0.153747...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>[-0.29777285, -0.20334117, -0.17459981, 0.1497...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.29777285, 0.20334117, 0.17459981, 0.1497344...</td>\n",
       "      <td>[0.09426482, 0.06399346, 0.05478003, 0.0468090...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>gunet</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>[-0.12712127, 0.11284148, 0.4379985, -0.753838...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.12712127, 0.11284148, 0.4379985, 0.7538388,...</td>\n",
       "      <td>[0.03956013, 0.03498255, 0.13921607, 0.2404629...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    model dataset_type dataset view cv_no  \\\n",
       "0   gunet     LH_ASDNC     NaN    5     1   \n",
       "1   gunet     LH_ASDNC     NaN    5     0   \n",
       "2   gunet     LH_ASDNC     NaN    1     1   \n",
       "3   gunet     LH_ASDNC     NaN    1     4   \n",
       "4   gunet     LH_ASDNC     NaN    3     1   \n",
       "5   gunet     LH_ASDNC     NaN    1     3   \n",
       "6   gunet     LH_ASDNC     NaN    4     1   \n",
       "7   gunet     LH_ASDNC     NaN    5     4   \n",
       "8   gunet     LH_ASDNC     NaN    5     2   \n",
       "9   gunet     LH_ASDNC     NaN    2     3   \n",
       "10  gunet     LH_ASDNC     NaN    2     0   \n",
       "11  gunet     LH_ASDNC     NaN    4     0   \n",
       "12  gunet     LH_ASDNC     NaN    0     4   \n",
       "13  gunet     LH_ASDNC     NaN    1     2   \n",
       "14  gunet     LH_ASDNC     NaN    2     4   \n",
       "15  gunet     LH_ASDNC     NaN    1     0   \n",
       "16  gunet     LH_ASDNC     NaN    0     3   \n",
       "17  gunet     LH_ASDNC     NaN    3     3   \n",
       "18  gunet     LH_ASDNC     NaN    4     2   \n",
       "19  gunet     LH_ASDNC     NaN    5     3   \n",
       "20  gunet     LH_ASDNC     NaN    3     0   \n",
       "21  gunet     LH_ASDNC     NaN    4     3   \n",
       "22  gunet     LH_ASDNC     NaN    2     1   \n",
       "23  gunet     LH_ASDNC     NaN    3     4   \n",
       "24  gunet     LH_ASDNC     NaN    3     2   \n",
       "25  gunet     LH_ASDNC     NaN    2     2   \n",
       "26  gunet     LH_ASDNC     NaN    0     0   \n",
       "27  gunet     LH_ASDNC     NaN    0     2   \n",
       "28  gunet     LH_ASDNC     NaN    0     1   \n",
       "29  gunet     LH_ASDNC     NaN    4     4   \n",
       "\n",
       "                                              weights labels preds  \\\n",
       "0   [0.7936466, -0.576373, -0.7680454, -0.87525296...    NaN   NaN   \n",
       "1   [-0.3839899, -0.76080054, -2.6513946, -0.59395...    NaN   NaN   \n",
       "2   [-0.8047803, -0.15785794, -1.2666754, -1.63858...    NaN   NaN   \n",
       "3   [-0.85870034, 0.7786339, -0.54228014, -0.81213...    NaN   NaN   \n",
       "4   [-2.0228937, -1.5795254, -0.066711344, -1.7795...    NaN   NaN   \n",
       "5   [-0.88142085, 0.060984373, -0.38065705, -0.808...    NaN   NaN   \n",
       "6   [-0.6182384, 0.31137642, 0.087907195, -0.73404...    NaN   NaN   \n",
       "7   [-2.271541, 0.0037132103, -0.4133792, -0.94544...    NaN   NaN   \n",
       "8   [-0.12964441, -0.096764006, -0.100969315, -0.1...    NaN   NaN   \n",
       "9   [0.85134214, -0.8951563, -0.9114397, -0.653786...    NaN   NaN   \n",
       "10  [-0.18301475, -0.6264444, -1.9211636, -0.58567...    NaN   NaN   \n",
       "11  [-1.7513441, -0.27948096, -0.39311448, -0.3180...    NaN   NaN   \n",
       "12  [-0.20153983, -0.6479829, -0.74934393, -0.7093...    NaN   NaN   \n",
       "13  [-1.2378198, -1.4392046, -0.37824976, -0.57844...    NaN   NaN   \n",
       "14  [-1.5413958, -0.9371311, -1.5738192, 0.0717039...    NaN   NaN   \n",
       "15  [-1.4483935, -0.7050891, -2.0286963, -1.348254...    NaN   NaN   \n",
       "16  [0.38105837, 0.64761645, -1.0781126, -0.288224...    NaN   NaN   \n",
       "17  [0.18198545, -0.6597841, -1.5256075, -0.143518...    NaN   NaN   \n",
       "18  [-1.0592666, -0.097429216, 0.01773572, -0.5135...    NaN   NaN   \n",
       "19  [-0.25313136, 0.029231826, -0.71386105, -1.275...    NaN   NaN   \n",
       "20  [0.5120717, -0.14325662, 0.52214026, 0.8745692...    NaN   NaN   \n",
       "21  [0.05543685, -0.93763906, -0.79282665, 0.12342...    NaN   NaN   \n",
       "22  [-1.0789748, -2.8108594, -1.4514246, -0.973314...    NaN   NaN   \n",
       "23  [-1.0034512, -1.2424487, -0.6539394, 1.5061889...    NaN   NaN   \n",
       "24  [-1.1101376, -1.9952235, 0.435011, -0.22167659...    NaN   NaN   \n",
       "25  [-2.4483228, -0.8982536, -0.11262385, 0.891669...    NaN   NaN   \n",
       "26  [-0.91749936, -0.57201684, 0.21181762, -0.7913...    NaN   NaN   \n",
       "27  [0.026674032, 0.28386125, -0.2624103, -0.48332...    NaN   NaN   \n",
       "28  [-0.29777285, -0.20334117, -0.17459981, 0.1497...    NaN   NaN   \n",
       "29  [-0.12712127, 0.11284148, 0.4379985, -0.753838...    NaN   NaN   \n",
       "\n",
       "                                         train_labels  \\\n",
       "0   [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "1   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "2   [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "3   [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "4   [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "5   [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "6   [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "7   [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "8   [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "9   [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "10  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "11  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "12  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "13  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "14  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "15  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "16  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "17  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "18  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "19  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "20  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "21  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "22  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "23  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "24  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "25  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "26  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "27  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "28  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "29  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "\n",
       "                                          train_preds  \\\n",
       "0   [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, ...   \n",
       "1   [1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, ...   \n",
       "2   [0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, ...   \n",
       "3   [1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, ...   \n",
       "4   [1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, ...   \n",
       "5   [0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, ...   \n",
       "6   [1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, ...   \n",
       "7   [0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, ...   \n",
       "8   [0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, ...   \n",
       "9   [0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, ...   \n",
       "10  [0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, ...   \n",
       "11  [0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, ...   \n",
       "12  [1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, ...   \n",
       "13  [1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, ...   \n",
       "14  [1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "15  [1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, ...   \n",
       "16  [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "17  [1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, ...   \n",
       "18  [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "19  [1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, ...   \n",
       "20  [0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, ...   \n",
       "21  [0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, ...   \n",
       "22  [1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, ...   \n",
       "23  [0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, ...   \n",
       "24  [0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, ...   \n",
       "25  [1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, ...   \n",
       "26  [0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, ...   \n",
       "27  [0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, ...   \n",
       "28  [0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, ...   \n",
       "29  [1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, ...   \n",
       "\n",
       "                                          test_labels  \\\n",
       "0   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "1   [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "2   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "3   [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "4   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "5   [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "6   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "7   [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "8   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "9   [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "10  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "11  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "12  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "13  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "14  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "15  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "16  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "17  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "18  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "19  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "20  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "21  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "22  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "23  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "24  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "25  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "26  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "27  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "28  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "29  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "\n",
       "                                           test_preds  \\\n",
       "0   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "1   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "2   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "3   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "4   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "5   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "6   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "7   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "8   [1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, ...   \n",
       "9   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "10  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "11  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "12  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "13  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "14  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "15  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "16  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "17  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "18  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "19  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "20  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "21  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, ...   \n",
       "22  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "23  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, ...   \n",
       "24  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "25  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "26  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "27  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "28  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "29  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "\n",
       "                                                    w  \\\n",
       "0   [0.7936466, 0.576373, 0.7680454, 0.87525296, 2...   \n",
       "1   [0.3839899, 0.76080054, 2.6513946, 0.5939501, ...   \n",
       "2   [0.8047803, 0.15785794, 1.2666754, 1.6385876, ...   \n",
       "3   [0.85870034, 0.7786339, 0.54228014, 0.8121369,...   \n",
       "4   [2.0228937, 1.5795254, 0.066711344, 1.7795426,...   \n",
       "5   [0.88142085, 0.060984373, 0.38065705, 0.808803...   \n",
       "6   [0.6182384, 0.31137642, 0.087907195, 0.7340436...   \n",
       "7   [2.271541, 0.0037132103, 0.4133792, 0.945443, ...   \n",
       "8   [0.12964441, 0.096764006, 0.100969315, 0.13016...   \n",
       "9   [0.85134214, 0.8951563, 0.9114397, 0.653786, 0...   \n",
       "10  [0.18301475, 0.6264444, 1.9211636, 0.58567375,...   \n",
       "11  [1.7513441, 0.27948096, 0.39311448, 0.31807315...   \n",
       "12  [0.20153983, 0.6479829, 0.74934393, 0.70935374...   \n",
       "13  [1.2378198, 1.4392046, 0.37824976, 0.5784436, ...   \n",
       "14  [1.5413958, 0.9371311, 1.5738192, 0.07170399, ...   \n",
       "15  [1.4483935, 0.7050891, 2.0286963, 1.3482548, 0...   \n",
       "16  [0.38105837, 0.64761645, 1.0781126, 0.28822473...   \n",
       "17  [0.18198545, 0.6597841, 1.5256075, 0.1435181, ...   \n",
       "18  [1.0592666, 0.097429216, 0.01773572, 0.5135835...   \n",
       "19  [0.25313136, 0.029231826, 0.71386105, 1.275749...   \n",
       "20  [0.5120717, 0.14325662, 0.52214026, 0.8745692,...   \n",
       "21  [0.05543685, 0.93763906, 0.79282665, 0.1234248...   \n",
       "22  [1.0789748, 2.8108594, 1.4514246, 0.9733145, 1...   \n",
       "23  [1.0034512, 1.2424487, 0.6539394, 1.5061889, 1...   \n",
       "24  [1.1101376, 1.9952235, 0.435011, 0.22167659, 0...   \n",
       "25  [2.4483228, 0.8982536, 0.11262385, 0.8916693, ...   \n",
       "26  [0.91749936, 0.57201684, 0.21181762, 0.7913403...   \n",
       "27  [0.026674032, 0.28386125, 0.2624103, 0.4833296...   \n",
       "28  [0.29777285, 0.20334117, 0.17459981, 0.1497344...   \n",
       "29  [0.12712127, 0.11284148, 0.4379985, 0.7538388,...   \n",
       "\n",
       "                                                    n  \n",
       "0   [0.2532239, 0.18357389, 0.2450171, 0.27938393,...  \n",
       "1   [0.12190287, 0.24269466, 0.84875035, 0.1892084...  \n",
       "2   [0.25679296, 0.049413193, 0.4048597, 0.5240812...  \n",
       "3   [0.27407777, 0.24841139, 0.17264497, 0.2591512...  \n",
       "4   [0.6472758, 0.505148, 0.020194909, 0.56926626,...  \n",
       "5   [0.28136113, 0.018359052, 0.12083448, 0.258082...  \n",
       "6   [0.19699442, 0.09862563, 0.026989527, 0.234117...  \n",
       "7   [0.72698313, 0.0, 0.13132401, 0.3018843, 0.227...  \n",
       "8   [0.040368963, 0.029828697, 0.031176768, 0.0405...  \n",
       "9   [0.271719, 0.28576422, 0.2909841, 0.20838968, ...  \n",
       "10  [0.057477552, 0.19962497, 0.6146648, 0.1865553...  \n",
       "11  [0.56022686, 0.0884011, 0.12482788, 0.10077236...  \n",
       "12  [0.06341602, 0.20652942, 0.23902209, 0.2262026...  \n",
       "13  [0.39560965, 0.46016628, 0.12006279, 0.1842376...  \n",
       "14  [0.49292508, 0.29921982, 0.50331885, 0.0217953...  \n",
       "15  [0.4631119, 0.22483559, 0.64913595, 0.4310111,...  \n",
       "16  [0.12096313, 0.20641196, 0.34441334, 0.0912040...  \n",
       "17  [0.057147596, 0.21031246, 0.4878639, 0.0448163...  \n",
       "18  [0.338372, 0.03004194, 0.004495106, 0.16344588...  \n",
       "19  [0.079954386, 0.00818034, 0.22764756, 0.407768...  \n",
       "20  [0.16296124, 0.04473254, 0.16618885, 0.2791647...  \n",
       "21  [0.016580718, 0.29938266, 0.25296107, 0.038375...  \n",
       "22  [0.34468973, 0.89986897, 0.46408358, 0.3108189...  \n",
       "23  [0.32047963, 0.3970935, 0.20843887, 0.48163903...  \n",
       "24  [0.35467938, 0.63840574, 0.13825837, 0.0698711...  \n",
       "25  [0.7836529, 0.2867571, 0.034912787, 0.28464642...  \n",
       "26  [0.2929266, 0.18217747, 0.0667107, 0.25248462,...  \n",
       "27  [0.007360404, 0.08980527, 0.08292887, 0.153747...  \n",
       "28  [0.09426482, 0.06399346, 0.05478003, 0.0468090...  \n",
       "29  [0.03956013, 0.03498255, 0.13921607, 0.2404629...  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gunet = GetModelDf(\"gunet\")\n",
    "gunet_datasets = GetDatasetsByView(df_gunet)\n",
    "df_gunet#.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d8de7e51",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>dataset</th>\n",
       "      <th>view</th>\n",
       "      <th>cv_no</th>\n",
       "      <th>weights</th>\n",
       "      <th>labels</th>\n",
       "      <th>preds</th>\n",
       "      <th>train_labels</th>\n",
       "      <th>train_preds</th>\n",
       "      <th>test_labels</th>\n",
       "      <th>test_preds</th>\n",
       "      <th>w</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.023063622, 0.069272585, -0.048519336, 0.038...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.023063622, 0.069272585, 0.048519336, 0.0380...</td>\n",
       "      <td>[0.16870715, 0.50685394, 0.3549863, 0.27826822...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>[-0.108309016, 0.00027426274, -0.02464876, -0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.108309016, 0.00027426274, 0.02464876, 0.007...</td>\n",
       "      <td>[0.7925137, 0.0019397533, 0.18030682, 0.054347...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[-0.051672842, -0.009522428, -0.055882152, -0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.051672842, 0.009522428, 0.055882152, 0.0205...</td>\n",
       "      <td>[0.37806293, 0.06961574, 0.40886572, 0.1501554...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.03156741, 0.02219344, -0.010648272, 0.00766...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.03156741, 0.02219344, 0.010648272, 0.007668...</td>\n",
       "      <td>[0.23093593, 0.16233934, 0.07785441, 0.0560454...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0017266019, -0.039406057, -0.047196496, -0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.0017266019, 0.039406057, 0.047196496, 0.008...</td>\n",
       "      <td>[0.012567643, 0.28829738, 0.34530607, 0.063048...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.042616848, -0.0037744057, -0.012649039, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.042616848, 0.0037744057, 0.012649039, 0.000...</td>\n",
       "      <td>[0.3117932, 0.02755301, 0.092495576, 0.0071186...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>[-0.005307767, 0.0027488156, 0.0034871516, -0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.005307767, 0.0027488156, 0.0034871516, 0.01...</td>\n",
       "      <td>[0.0387738, 0.020047974, 0.025450949, 0.142872...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>[-0.050600514, 0.0061190575, 0.01297032, -0.00...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.050600514, 0.0061190575, 0.01297032, 0.0088...</td>\n",
       "      <td>[0.3702159, 0.044710644, 0.094846636, 0.064457...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.023131752, -0.004338759, -0.022537038, 0.05...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, ...</td>\n",
       "      <td>[0.023131752, 0.004338759, 0.022537038, 0.0523...</td>\n",
       "      <td>[0.16920571, 0.03168282, 0.16485372, 0.3831763...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>[-0.017749099, 0.014494464, 0.0029230614, 0.04...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.017749099, 0.014494464, 0.0029230614, 0.047...</td>\n",
       "      <td>[0.12981667, 0.10599998, 0.021323064, 0.347674...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.031264756, 0.00841499, -0.039814778, 0.0578...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.031264756, 0.00841499, 0.039814778, 0.05784...</td>\n",
       "      <td>[0.22872117, 0.061511762, 0.29128832, 0.423230...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>[-0.010666133, 0.0030575842, -0.004067133, -0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.010666133, 0.0030575842, 0.004067133, 0.001...</td>\n",
       "      <td>[0.077985115, 0.022307472, 0.029695123, 0.0142...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>[-0.017663302, 0.015754057, 0.06232989, 0.0536...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, ...</td>\n",
       "      <td>[0.017663302, 0.015754057, 0.06232989, 0.05363...</td>\n",
       "      <td>[0.12918882, 0.115217395, 0.45604882, 0.392406...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.061478738, 0.06555518, -0.112809025, 0.0929...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.061478738, 0.06555518, 0.112809025, 0.09299...</td>\n",
       "      <td>[0.44982028, 0.47965077, 0.82544374, 0.6804615...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.019046407, 0.014021384, -0.028657744, 0.038...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.019046407, 0.014021384, 0.028657744, 0.0385...</td>\n",
       "      <td>[0.13931008, 0.10253808, 0.20964366, 0.2823377...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.04214116, 0.06575665, -0.0540955, 0.0520823...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0.04214116, 0.06575665, 0.0540955, 0.05208239...</td>\n",
       "      <td>[0.30831227, 0.4811251, 0.3957914, 0.38105994,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.028422246, 0.050325863, -0.07190567, 0.0929...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.028422246, 0.050325863, 0.07190567, 0.09295...</td>\n",
       "      <td>[0.20792034, 0.36820605, 0.5261223, 0.6801431,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>[-0.02415741, 0.0051953755, -0.009138549, 0.04...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.02415741, 0.0051953755, 0.009138549, 0.0413...</td>\n",
       "      <td>[0.17671123, 0.037951346, 0.0668066, 0.3022499...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.024552684, -0.010689903, 0.011926948, -0.04...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.024552684, 0.010689903, 0.011926948, 0.0414...</td>\n",
       "      <td>[0.17960377, 0.07815906, 0.087211475, 0.303369...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0027924145, -0.002052237, -0.03421058, -0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.0027924145, 0.002052237, 0.03421058, 0.0057...</td>\n",
       "      <td>[0.02036702, 0.014950569, 0.25027806, 0.042004...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>[-0.0010905978, 0.01231589, -0.033384707, -0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.0010905978, 0.01231589, 0.033384707, 0.0563...</td>\n",
       "      <td>[0.007913509, 0.09005766, 0.2442345, 0.4120872...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.035445973, 0.02735742, -0.06592268, 0.04089...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.035445973, 0.02735742, 0.06592268, 0.040891...</td>\n",
       "      <td>[0.25931838, 0.20012818, 0.48234004, 0.2991661...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.06187687, 0.051233497, -0.04808743, 0.04567...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.06187687, 0.051233497, 0.04808743, 0.045678...</td>\n",
       "      <td>[0.45273373, 0.37484792, 0.3518257, 0.33420134...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.029146882, 0.045134187, -0.030990837, 0.030...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, ...</td>\n",
       "      <td>[0.029146882, 0.045134187, 0.030990837, 0.0302...</td>\n",
       "      <td>[0.21322307, 0.33021453, 0.22671671, 0.2211413...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>[-0.005796872, 0.027155058, -0.013468403, 0.01...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.005796872, 0.027155058, 0.013468403, 0.0142...</td>\n",
       "      <td>[0.042352963, 0.19864735, 0.0984915, 0.1041421...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.03205173, -0.021878846, -0.03327563, 0.0542...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, ...</td>\n",
       "      <td>[0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.03205173, 0.021878846, 0.03327563, 0.054254...</td>\n",
       "      <td>[0.23448008, 0.16003722, 0.2434363, 0.39695638...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.018299174, 0.019921727, -0.030628204, 0.017...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.018299174, 0.019921727, 0.030628204, 0.0174...</td>\n",
       "      <td>[0.13384198, 0.14571546, 0.22406305, 0.1278723...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.05653427, 0.05368885, -0.039951637, 0.03726...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, ...</td>\n",
       "      <td>[0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.05653427, 0.05368885, 0.039951637, 0.037267...</td>\n",
       "      <td>[0.4136378, 0.39281565, 0.29228982, 0.2726478,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.031359278, 0.005032076, -0.044264615, 0.017...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0.031359278, 0.005032076, 0.044264615, 0.0177...</td>\n",
       "      <td>[0.22941287, 0.03675636, 0.3238512, 0.12998265...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>gat</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>[-0.004642924, -0.0034772607, 0.007625134, -0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0.004642924, 0.0034772607, 0.007625134, 0.007...</td>\n",
       "      <td>[0.03390863, 0.02537857, 0.055731773, 0.056263...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model dataset_type dataset view cv_no  \\\n",
       "0    gat     LH_ASDNC     NaN    1     2   \n",
       "1    gat     LH_ASDNC     NaN    2     2   \n",
       "2    gat     LH_ASDNC     NaN    0     2   \n",
       "3    gat     LH_ASDNC     NaN    0     4   \n",
       "4    gat     LH_ASDNC     NaN    0     1   \n",
       "5    gat     LH_ASDNC     NaN    0     3   \n",
       "6    gat     LH_ASDNC     NaN    3     4   \n",
       "7    gat     LH_ASDNC     NaN    3     1   \n",
       "8    gat     LH_ASDNC     NaN    1     1   \n",
       "9    gat     LH_ASDNC     NaN    5     2   \n",
       "10   gat     LH_ASDNC     NaN    4     4   \n",
       "11   gat     LH_ASDNC     NaN    5     3   \n",
       "12   gat     LH_ASDNC     NaN    1     4   \n",
       "13   gat     LH_ASDNC     NaN    5     0   \n",
       "14   gat     LH_ASDNC     NaN    5     1   \n",
       "15   gat     LH_ASDNC     NaN    0     0   \n",
       "16   gat     LH_ASDNC     NaN    4     3   \n",
       "17   gat     LH_ASDNC     NaN    5     4   \n",
       "18   gat     LH_ASDNC     NaN    4     1   \n",
       "19   gat     LH_ASDNC     NaN    2     1   \n",
       "20   gat     LH_ASDNC     NaN    3     0   \n",
       "21   gat     LH_ASDNC     NaN    2     0   \n",
       "22   gat     LH_ASDNC     NaN    3     2   \n",
       "23   gat     LH_ASDNC     NaN    4     0   \n",
       "24   gat     LH_ASDNC     NaN    3     3   \n",
       "25   gat     LH_ASDNC     NaN    2     4   \n",
       "26   gat     LH_ASDNC     NaN    4     2   \n",
       "27   gat     LH_ASDNC     NaN    1     0   \n",
       "28   gat     LH_ASDNC     NaN    2     3   \n",
       "29   gat     LH_ASDNC     NaN    1     3   \n",
       "\n",
       "                                              weights labels preds  \\\n",
       "0   [0.023063622, 0.069272585, -0.048519336, 0.038...    NaN   NaN   \n",
       "1   [-0.108309016, 0.00027426274, -0.02464876, -0....    NaN   NaN   \n",
       "2   [-0.051672842, -0.009522428, -0.055882152, -0....    NaN   NaN   \n",
       "3   [0.03156741, 0.02219344, -0.010648272, 0.00766...    NaN   NaN   \n",
       "4   [0.0017266019, -0.039406057, -0.047196496, -0....    NaN   NaN   \n",
       "5   [0.042616848, -0.0037744057, -0.012649039, 0.0...    NaN   NaN   \n",
       "6   [-0.005307767, 0.0027488156, 0.0034871516, -0....    NaN   NaN   \n",
       "7   [-0.050600514, 0.0061190575, 0.01297032, -0.00...    NaN   NaN   \n",
       "8   [0.023131752, -0.004338759, -0.022537038, 0.05...    NaN   NaN   \n",
       "9   [-0.017749099, 0.014494464, 0.0029230614, 0.04...    NaN   NaN   \n",
       "10  [0.031264756, 0.00841499, -0.039814778, 0.0578...    NaN   NaN   \n",
       "11  [-0.010666133, 0.0030575842, -0.004067133, -0....    NaN   NaN   \n",
       "12  [-0.017663302, 0.015754057, 0.06232989, 0.0536...    NaN   NaN   \n",
       "13  [0.061478738, 0.06555518, -0.112809025, 0.0929...    NaN   NaN   \n",
       "14  [0.019046407, 0.014021384, -0.028657744, 0.038...    NaN   NaN   \n",
       "15  [0.04214116, 0.06575665, -0.0540955, 0.0520823...    NaN   NaN   \n",
       "16  [0.028422246, 0.050325863, -0.07190567, 0.0929...    NaN   NaN   \n",
       "17  [-0.02415741, 0.0051953755, -0.009138549, 0.04...    NaN   NaN   \n",
       "18  [0.024552684, -0.010689903, 0.011926948, -0.04...    NaN   NaN   \n",
       "19  [0.0027924145, -0.002052237, -0.03421058, -0.0...    NaN   NaN   \n",
       "20  [-0.0010905978, 0.01231589, -0.033384707, -0.0...    NaN   NaN   \n",
       "21  [0.035445973, 0.02735742, -0.06592268, 0.04089...    NaN   NaN   \n",
       "22  [0.06187687, 0.051233497, -0.04808743, 0.04567...    NaN   NaN   \n",
       "23  [0.029146882, 0.045134187, -0.030990837, 0.030...    NaN   NaN   \n",
       "24  [-0.005796872, 0.027155058, -0.013468403, 0.01...    NaN   NaN   \n",
       "25  [0.03205173, -0.021878846, -0.03327563, 0.0542...    NaN   NaN   \n",
       "26  [0.018299174, 0.019921727, -0.030628204, 0.017...    NaN   NaN   \n",
       "27  [0.05653427, 0.05368885, -0.039951637, 0.03726...    NaN   NaN   \n",
       "28  [0.031359278, 0.005032076, -0.044264615, 0.017...    NaN   NaN   \n",
       "29  [-0.004642924, -0.0034772607, 0.007625134, -0....    NaN   NaN   \n",
       "\n",
       "                                         train_labels  \\\n",
       "0   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "1   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "2   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "3   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "4   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "5   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "6   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "7   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "8   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "9   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "10  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "11  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "12  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "13  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "14  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "15  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "16  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "17  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "18  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "19  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "20  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "21  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "22  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "23  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "24  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "25  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "26  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "27  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "28  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "29  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "\n",
       "                                          train_preds  \\\n",
       "0   [0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...   \n",
       "1   [1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, ...   \n",
       "2   [0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, ...   \n",
       "3   [1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, ...   \n",
       "4   [0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, ...   \n",
       "5   [1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, ...   \n",
       "6   [1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, ...   \n",
       "7   [0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, ...   \n",
       "8   [1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, ...   \n",
       "9   [0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, ...   \n",
       "10  [1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "11  [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, ...   \n",
       "12  [1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, ...   \n",
       "13  [0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "14  [1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, ...   \n",
       "15  [1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, ...   \n",
       "16  [1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, ...   \n",
       "17  [0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, ...   \n",
       "18  [1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, ...   \n",
       "19  [0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, ...   \n",
       "20  [1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, ...   \n",
       "21  [0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, ...   \n",
       "22  [0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, ...   \n",
       "23  [1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, ...   \n",
       "24  [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, ...   \n",
       "25  [1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, ...   \n",
       "26  [1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, ...   \n",
       "27  [1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, ...   \n",
       "28  [0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, ...   \n",
       "29  [0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, ...   \n",
       "\n",
       "                                          test_labels  \\\n",
       "0   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "1   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "2   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "3   [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "4   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "5   [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "6   [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "7   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "8   [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "9   [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "10  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "11  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "12  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "13  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "14  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "15  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "16  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "17  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "18  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "19  [0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "20  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "21  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "22  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "23  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "24  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "25  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "26  [0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, ...   \n",
       "27  [0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, ...   \n",
       "28  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "29  [1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, ...   \n",
       "\n",
       "                                           test_preds  \\\n",
       "0   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "1   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "2   [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "3   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "4   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "5   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "6   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "7   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "8   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, ...   \n",
       "9   [1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, ...   \n",
       "10  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "11  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "12  [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, ...   \n",
       "13  [1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "14  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "15  [1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, ...   \n",
       "16  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "17  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "18  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "19  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "20  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "21  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "22  [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "23  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, ...   \n",
       "24  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "25  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "26  [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "27  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, ...   \n",
       "28  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, ...   \n",
       "29  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, ...   \n",
       "\n",
       "                                                    w  \\\n",
       "0   [0.023063622, 0.069272585, 0.048519336, 0.0380...   \n",
       "1   [0.108309016, 0.00027426274, 0.02464876, 0.007...   \n",
       "2   [0.051672842, 0.009522428, 0.055882152, 0.0205...   \n",
       "3   [0.03156741, 0.02219344, 0.010648272, 0.007668...   \n",
       "4   [0.0017266019, 0.039406057, 0.047196496, 0.008...   \n",
       "5   [0.042616848, 0.0037744057, 0.012649039, 0.000...   \n",
       "6   [0.005307767, 0.0027488156, 0.0034871516, 0.01...   \n",
       "7   [0.050600514, 0.0061190575, 0.01297032, 0.0088...   \n",
       "8   [0.023131752, 0.004338759, 0.022537038, 0.0523...   \n",
       "9   [0.017749099, 0.014494464, 0.0029230614, 0.047...   \n",
       "10  [0.031264756, 0.00841499, 0.039814778, 0.05784...   \n",
       "11  [0.010666133, 0.0030575842, 0.004067133, 0.001...   \n",
       "12  [0.017663302, 0.015754057, 0.06232989, 0.05363...   \n",
       "13  [0.061478738, 0.06555518, 0.112809025, 0.09299...   \n",
       "14  [0.019046407, 0.014021384, 0.028657744, 0.0385...   \n",
       "15  [0.04214116, 0.06575665, 0.0540955, 0.05208239...   \n",
       "16  [0.028422246, 0.050325863, 0.07190567, 0.09295...   \n",
       "17  [0.02415741, 0.0051953755, 0.009138549, 0.0413...   \n",
       "18  [0.024552684, 0.010689903, 0.011926948, 0.0414...   \n",
       "19  [0.0027924145, 0.002052237, 0.03421058, 0.0057...   \n",
       "20  [0.0010905978, 0.01231589, 0.033384707, 0.0563...   \n",
       "21  [0.035445973, 0.02735742, 0.06592268, 0.040891...   \n",
       "22  [0.06187687, 0.051233497, 0.04808743, 0.045678...   \n",
       "23  [0.029146882, 0.045134187, 0.030990837, 0.0302...   \n",
       "24  [0.005796872, 0.027155058, 0.013468403, 0.0142...   \n",
       "25  [0.03205173, 0.021878846, 0.03327563, 0.054254...   \n",
       "26  [0.018299174, 0.019921727, 0.030628204, 0.0174...   \n",
       "27  [0.05653427, 0.05368885, 0.039951637, 0.037267...   \n",
       "28  [0.031359278, 0.005032076, 0.044264615, 0.0177...   \n",
       "29  [0.004642924, 0.0034772607, 0.007625134, 0.007...   \n",
       "\n",
       "                                                    n  \n",
       "0   [0.16870715, 0.50685394, 0.3549863, 0.27826822...  \n",
       "1   [0.7925137, 0.0019397533, 0.18030682, 0.054347...  \n",
       "2   [0.37806293, 0.06961574, 0.40886572, 0.1501554...  \n",
       "3   [0.23093593, 0.16233934, 0.07785441, 0.0560454...  \n",
       "4   [0.012567643, 0.28829738, 0.34530607, 0.063048...  \n",
       "5   [0.3117932, 0.02755301, 0.092495576, 0.0071186...  \n",
       "6   [0.0387738, 0.020047974, 0.025450949, 0.142872...  \n",
       "7   [0.3702159, 0.044710644, 0.094846636, 0.064457...  \n",
       "8   [0.16920571, 0.03168282, 0.16485372, 0.3831763...  \n",
       "9   [0.12981667, 0.10599998, 0.021323064, 0.347674...  \n",
       "10  [0.22872117, 0.061511762, 0.29128832, 0.423230...  \n",
       "11  [0.077985115, 0.022307472, 0.029695123, 0.0142...  \n",
       "12  [0.12918882, 0.115217395, 0.45604882, 0.392406...  \n",
       "13  [0.44982028, 0.47965077, 0.82544374, 0.6804615...  \n",
       "14  [0.13931008, 0.10253808, 0.20964366, 0.2823377...  \n",
       "15  [0.30831227, 0.4811251, 0.3957914, 0.38105994,...  \n",
       "16  [0.20792034, 0.36820605, 0.5261223, 0.6801431,...  \n",
       "17  [0.17671123, 0.037951346, 0.0668066, 0.3022499...  \n",
       "18  [0.17960377, 0.07815906, 0.087211475, 0.303369...  \n",
       "19  [0.02036702, 0.014950569, 0.25027806, 0.042004...  \n",
       "20  [0.007913509, 0.09005766, 0.2442345, 0.4120872...  \n",
       "21  [0.25931838, 0.20012818, 0.48234004, 0.2991661...  \n",
       "22  [0.45273373, 0.37484792, 0.3518257, 0.33420134...  \n",
       "23  [0.21322307, 0.33021453, 0.22671671, 0.2211413...  \n",
       "24  [0.042352963, 0.19864735, 0.0984915, 0.1041421...  \n",
       "25  [0.23448008, 0.16003722, 0.2434363, 0.39695638...  \n",
       "26  [0.13384198, 0.14571546, 0.22406305, 0.1278723...  \n",
       "27  [0.4136378, 0.39281565, 0.29228982, 0.2726478,...  \n",
       "28  [0.22941287, 0.03675636, 0.3238512, 0.12998265...  \n",
       "29  [0.03390863, 0.02537857, 0.055731773, 0.056263...  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gat = GetModelDf(\"gat\")\n",
    "gat_datasets = GetDatasetsByView(df_gat)\n",
    "df_gat#.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e52e0452",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>dataset</th>\n",
       "      <th>view</th>\n",
       "      <th>cv_no</th>\n",
       "      <th>weights</th>\n",
       "      <th>labels</th>\n",
       "      <th>preds</th>\n",
       "      <th>train_labels</th>\n",
       "      <th>train_preds</th>\n",
       "      <th>test_labels</th>\n",
       "      <th>test_preds</th>\n",
       "      <th>w</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[-2.4355013e-14, -7.9422874e-15, -7.03134e-15,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[2.4355013e-14, 7.9422874e-15, 7.03134e-15, 2....</td>\n",
       "      <td>[1.0068504e-12, 3.2833878e-13, 2.9067967e-13, ...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.00022351817, 4.2670813e-06, 3.4483182e-07, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.00022351817, 4.2670813e-06, 3.4483182e-07, ...</td>\n",
       "      <td>[0.009240371, 0.00017640363, 1.4255548e-05, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.008354922, 0.0090138875, 0.00655974, 0.0081...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.008354922, 0.0090138875, 0.00655974, 0.0081...</td>\n",
       "      <td>[0.34539732, 0.37263936, 0.27118346, 0.3379581...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[3.4208396e-12, 2.596752e-12, -7.1247565e-12, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, ...</td>\n",
       "      <td>[3.4208396e-12, 2.596752e-12, 7.1247565e-12, 3...</td>\n",
       "      <td>[1.414195e-10, 1.0735124e-10, 2.9454159e-10, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.0005361984, 1.1695011e-05, 2.8312315e-06, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.0005361984, 1.1695011e-05, 2.8312315e-06, 0...</td>\n",
       "      <td>[0.022166753, 0.00048347857, 0.00011704476, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>[2.4601245e-06, 6.863402e-07, 8.4290446e-07, 2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[2.4601245e-06, 6.863402e-07, 8.4290446e-07, 2...</td>\n",
       "      <td>[0.00010170298, 2.8373703e-05, 3.484616e-05, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[8.181603e-05, 3.8962575e-05, 1.4325404e-05, 2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[8.181603e-05, 3.8962575e-05, 1.4325404e-05, 2...</td>\n",
       "      <td>[0.0033823224, 0.0016107355, 0.0005922205, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.017023996, 0.013855415, 0.009946956, 0.0160...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, ...</td>\n",
       "      <td>[1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0.017023996, 0.013855415, 0.009946956, 0.0160...</td>\n",
       "      <td>[0.7037819, 0.5727909, 0.41121295, 0.6641522, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>[7.05444e-40, 5.20053e-40, 7.2889e-40, -8.5193...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, ...</td>\n",
       "      <td>[1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, ...</td>\n",
       "      <td>[1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[7.05444e-40, 5.20053e-40, 7.2889e-40, 8.51939...</td>\n",
       "      <td>[2.8818392e-38, 2.115419e-38, 2.9787628e-38, 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>[-1.0404896e-17, -1.9620575e-17, -1.2769403e-1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[1.0404896e-17, 1.9620575e-17, 1.2769403e-17, ...</td>\n",
       "      <td>[4.3014443e-16, 8.11126e-16, 5.2789454e-16, 4....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>[-5.97753e-40, -9.406e-41, 6.91294e-40, -4.478...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, ...</td>\n",
       "      <td>[5.97753e-40, 9.406e-41, 6.91294e-40, 4.4789e-...</td>\n",
       "      <td>[2.4366377e-38, 3.543377e-39, 2.8233412e-38, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>[1.668519e-18, 4.668642e-19, 1.2115933e-18, 1....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[1.668519e-18, 4.668642e-19, 1.2115933e-18, 1....</td>\n",
       "      <td>[6.8977546e-17, 1.9300438e-17, 5.008797e-17, 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.0075963885, 0.0050555468, 0.005596886, 0.00...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.0075963885, 0.0050555468, 0.005596886, 0.00...</td>\n",
       "      <td>[0.3140391, 0.20899925, 0.23137853, 0.2953196,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.0001340163, 8.552111e-05, 6.28521e-05, 0.00...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0.0001340163, 8.552111e-05, 6.28521e-05, 0.00...</td>\n",
       "      <td>[0.005540312, 0.0035354926, 0.0025983425, 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>[5.5897563e-06, -2.3478788e-06, 1.7229743e-06,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[5.5897563e-06, 2.3478788e-06, 1.7229743e-06, ...</td>\n",
       "      <td>[0.00023108377, 9.706267e-05, 7.122876e-05, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>[-8.4675754e-05, 0.00057628466, 0.0007121916, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, ...</td>\n",
       "      <td>[1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[8.4675754e-05, 0.00057628466, 0.0007121916, 0...</td>\n",
       "      <td>[0.003500545, 0.023823943, 0.029442417, 0.0059...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>[-0.0014725752, 0.00066329376, 0.0012488001, -...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, ...</td>\n",
       "      <td>[1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[0.0014725752, 0.00066329376, 0.0012488001, 0....</td>\n",
       "      <td>[0.06087712, 0.02742095, 0.051626123, 0.010079...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0013292617, -0.008167243, -0.008039634, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0.0013292617, 0.008167243, 0.008039634, 0.001...</td>\n",
       "      <td>[0.05495245, 0.33763856, 0.33236313, 0.0640013...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>[-1.5982949e-05, -3.7255732e-06, -7.847019e-06...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, ...</td>\n",
       "      <td>[1.5982949e-05, 3.7255732e-06, 7.847019e-06, 1...</td>\n",
       "      <td>[0.0006607444, 0.00015401735, 0.00032440035, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0019204092, 0.0025825961, 0.0017644401, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0.0019204092, 0.0025825961, 0.0017644401, 0.0...</td>\n",
       "      <td>[0.07939083, 0.10676603, 0.07294298, 0.0797757...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>[-0.00017957852, -6.103716e-05, -0.00014175565...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0.00017957852, 6.103716e-05, 0.00014175565, 0...</td>\n",
       "      <td>[0.0074238805, 0.0025233116, 0.005860261, 0.01...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[-5.651253e-05, 0.00057762186, 5.8827693e-05, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, ...</td>\n",
       "      <td>[1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[5.651253e-05, 0.00057762186, 5.8827693e-05, 0...</td>\n",
       "      <td>[0.0023362609, 0.023879224, 0.002431971, 0.010...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>[-5.655759e-08, -4.079454e-08, 1.4971452e-08, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, ...</td>\n",
       "      <td>[1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, ...</td>\n",
       "      <td>[1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, ...</td>\n",
       "      <td>[1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[5.655759e-08, 4.079454e-08, 1.4971452e-08, 5....</td>\n",
       "      <td>[2.3381235e-06, 1.68647e-06, 6.1892854e-07, 2....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>[7.958601e-14, -9.915393e-16, 3.1958437e-16, -...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, ...</td>\n",
       "      <td>[7.958601e-14, 9.915393e-16, 3.1958437e-16, 1....</td>\n",
       "      <td>[3.2901318e-12, 4.099081e-14, 1.3211804e-14, 4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>[6.6287266e-06, 2.0626212e-05, 1.928877e-06, 2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[6.6287266e-06, 2.0626212e-05, 1.928877e-06, 2...</td>\n",
       "      <td>[0.0002740354, 0.0008526996, 7.97409e-05, 0.00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>[-0.0001589426, 0.00074004167, 0.001365604, -0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0.0001589426, 0.00074004167, 0.001365604, 0.0...</td>\n",
       "      <td>[0.0065707793, 0.030593753, 0.056454863, 0.005...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0012638824, 0.0007535093, 0.0011331486, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
       "      <td>[0.0012638824, 0.0007535093, 0.0011331486, 0.0...</td>\n",
       "      <td>[0.052249633, 0.031150512, 0.04684502, 0.06300...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>[-2.5441565e-05, -4.879906e-06, -4.6271978e-07...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...</td>\n",
       "      <td>[2.5441565e-05, 4.879906e-06, 4.6271978e-07, 2...</td>\n",
       "      <td>[0.0010517691, 0.00020173816, 1.9129106e-05, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>[0.00040561936, 0.0003234212, 0.00010498321, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.00040561936, 0.0003234212, 0.00010498321, 0...</td>\n",
       "      <td>[0.01676854, 0.013370421, 0.004340067, 0.00850...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>sag</td>\n",
       "      <td>LH_ASDNC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.00038709503, 0.0005713174, 0.000559857, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, ...</td>\n",
       "      <td>[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
       "      <td>[0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...</td>\n",
       "      <td>[1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0.00038709503, 0.0005713174, 0.000559857, 0.0...</td>\n",
       "      <td>[0.016002733, 0.023618594, 0.023144813, 0.0263...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model dataset_type dataset view cv_no  \\\n",
       "0    sag     LH_ASDNC     NaN    2     1   \n",
       "1    sag     LH_ASDNC     NaN    2     4   \n",
       "2    sag     LH_ASDNC     NaN    1     4   \n",
       "3    sag     LH_ASDNC     NaN    0     0   \n",
       "4    sag     LH_ASDNC     NaN    3     4   \n",
       "5    sag     LH_ASDNC     NaN    3     1   \n",
       "6    sag     LH_ASDNC     NaN    1     1   \n",
       "7    sag     LH_ASDNC     NaN    1     3   \n",
       "8    sag     LH_ASDNC     NaN    2     3   \n",
       "9    sag     LH_ASDNC     NaN    2     0   \n",
       "10   sag     LH_ASDNC     NaN    3     0   \n",
       "11   sag     LH_ASDNC     NaN    3     3   \n",
       "12   sag     LH_ASDNC     NaN    5     4   \n",
       "13   sag     LH_ASDNC     NaN    5     0   \n",
       "14   sag     LH_ASDNC     NaN    5     1   \n",
       "15   sag     LH_ASDNC     NaN    4     3   \n",
       "16   sag     LH_ASDNC     NaN    5     3   \n",
       "17   sag     LH_ASDNC     NaN    0     1   \n",
       "18   sag     LH_ASDNC     NaN    4     0   \n",
       "19   sag     LH_ASDNC     NaN    2     2   \n",
       "20   sag     LH_ASDNC     NaN    4     2   \n",
       "21   sag     LH_ASDNC     NaN    1     0   \n",
       "22   sag     LH_ASDNC     NaN    0     3   \n",
       "23   sag     LH_ASDNC     NaN    5     2   \n",
       "24   sag     LH_ASDNC     NaN    4     4   \n",
       "25   sag     LH_ASDNC     NaN    3     2   \n",
       "26   sag     LH_ASDNC     NaN    0     2   \n",
       "27   sag     LH_ASDNC     NaN    1     2   \n",
       "28   sag     LH_ASDNC     NaN    0     4   \n",
       "29   sag     LH_ASDNC     NaN    4     1   \n",
       "\n",
       "                                              weights labels preds  \\\n",
       "0   [-2.4355013e-14, -7.9422874e-15, -7.03134e-15,...    NaN   NaN   \n",
       "1   [0.00022351817, 4.2670813e-06, 3.4483182e-07, ...    NaN   NaN   \n",
       "2   [0.008354922, 0.0090138875, 0.00655974, 0.0081...    NaN   NaN   \n",
       "3   [3.4208396e-12, 2.596752e-12, -7.1247565e-12, ...    NaN   NaN   \n",
       "4   [0.0005361984, 1.1695011e-05, 2.8312315e-06, 0...    NaN   NaN   \n",
       "5   [2.4601245e-06, 6.863402e-07, 8.4290446e-07, 2...    NaN   NaN   \n",
       "6   [8.181603e-05, 3.8962575e-05, 1.4325404e-05, 2...    NaN   NaN   \n",
       "7   [0.017023996, 0.013855415, 0.009946956, 0.0160...    NaN   NaN   \n",
       "8   [7.05444e-40, 5.20053e-40, 7.2889e-40, -8.5193...    NaN   NaN   \n",
       "9   [-1.0404896e-17, -1.9620575e-17, -1.2769403e-1...    NaN   NaN   \n",
       "10  [-5.97753e-40, -9.406e-41, 6.91294e-40, -4.478...    NaN   NaN   \n",
       "11  [1.668519e-18, 4.668642e-19, 1.2115933e-18, 1....    NaN   NaN   \n",
       "12  [0.0075963885, 0.0050555468, 0.005596886, 0.00...    NaN   NaN   \n",
       "13  [0.0001340163, 8.552111e-05, 6.28521e-05, 0.00...    NaN   NaN   \n",
       "14  [5.5897563e-06, -2.3478788e-06, 1.7229743e-06,...    NaN   NaN   \n",
       "15  [-8.4675754e-05, 0.00057628466, 0.0007121916, ...    NaN   NaN   \n",
       "16  [-0.0014725752, 0.00066329376, 0.0012488001, -...    NaN   NaN   \n",
       "17  [0.0013292617, -0.008167243, -0.008039634, 0.0...    NaN   NaN   \n",
       "18  [-1.5982949e-05, -3.7255732e-06, -7.847019e-06...    NaN   NaN   \n",
       "19  [0.0019204092, 0.0025825961, 0.0017644401, 0.0...    NaN   NaN   \n",
       "20  [-0.00017957852, -6.103716e-05, -0.00014175565...    NaN   NaN   \n",
       "21  [-5.651253e-05, 0.00057762186, 5.8827693e-05, ...    NaN   NaN   \n",
       "22  [-5.655759e-08, -4.079454e-08, 1.4971452e-08, ...    NaN   NaN   \n",
       "23  [7.958601e-14, -9.915393e-16, 3.1958437e-16, -...    NaN   NaN   \n",
       "24  [6.6287266e-06, 2.0626212e-05, 1.928877e-06, 2...    NaN   NaN   \n",
       "25  [-0.0001589426, 0.00074004167, 0.001365604, -0...    NaN   NaN   \n",
       "26  [0.0012638824, 0.0007535093, 0.0011331486, 0.0...    NaN   NaN   \n",
       "27  [-2.5441565e-05, -4.879906e-06, -4.6271978e-07...    NaN   NaN   \n",
       "28  [0.00040561936, 0.0003234212, 0.00010498321, 0...    NaN   NaN   \n",
       "29  [0.00038709503, 0.0005713174, 0.000559857, 0.0...    NaN   NaN   \n",
       "\n",
       "                                         train_labels  \\\n",
       "0   [1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, ...   \n",
       "1   [0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, ...   \n",
       "2   [0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, ...   \n",
       "3   [1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, ...   \n",
       "4   [0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, ...   \n",
       "5   [1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, ...   \n",
       "6   [1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, ...   \n",
       "7   [0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, ...   \n",
       "8   [0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, ...   \n",
       "9   [1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, ...   \n",
       "10  [1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, ...   \n",
       "11  [0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, ...   \n",
       "12  [0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, ...   \n",
       "13  [1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, ...   \n",
       "14  [1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, ...   \n",
       "15  [0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, ...   \n",
       "16  [0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, ...   \n",
       "17  [1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, ...   \n",
       "18  [1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, ...   \n",
       "19  [0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, ...   \n",
       "20  [0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, ...   \n",
       "21  [1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, ...   \n",
       "22  [0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, ...   \n",
       "23  [0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, ...   \n",
       "24  [0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, ...   \n",
       "25  [0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, ...   \n",
       "26  [0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, ...   \n",
       "27  [0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, ...   \n",
       "28  [0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, ...   \n",
       "29  [1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, ...   \n",
       "\n",
       "                                          train_preds  \\\n",
       "0   [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...   \n",
       "1   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "2   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "3   [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, ...   \n",
       "4   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "5   [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, ...   \n",
       "6   [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...   \n",
       "7   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "8   [1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, ...   \n",
       "9   [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, ...   \n",
       "10  [1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, ...   \n",
       "11  [1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, ...   \n",
       "12  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "13  [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...   \n",
       "14  [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...   \n",
       "15  [1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, ...   \n",
       "16  [1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, ...   \n",
       "17  [1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, ...   \n",
       "18  [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, ...   \n",
       "19  [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, ...   \n",
       "20  [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...   \n",
       "21  [1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, ...   \n",
       "22  [1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, ...   \n",
       "23  [0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, ...   \n",
       "24  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "25  [0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...   \n",
       "26  [1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, ...   \n",
       "27  [1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, ...   \n",
       "28  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "29  [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...   \n",
       "\n",
       "                                          test_labels  \\\n",
       "0   [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...   \n",
       "1   [0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, ...   \n",
       "2   [0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, ...   \n",
       "3   [1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, ...   \n",
       "4   [0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, ...   \n",
       "5   [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...   \n",
       "6   [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...   \n",
       "7   [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, ...   \n",
       "8   [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, ...   \n",
       "9   [1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, ...   \n",
       "10  [1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, ...   \n",
       "11  [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, ...   \n",
       "12  [0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, ...   \n",
       "13  [1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, ...   \n",
       "14  [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...   \n",
       "15  [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, ...   \n",
       "16  [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, ...   \n",
       "17  [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...   \n",
       "18  [1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, ...   \n",
       "19  [1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, ...   \n",
       "20  [1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, ...   \n",
       "21  [1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, ...   \n",
       "22  [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, ...   \n",
       "23  [1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, ...   \n",
       "24  [0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, ...   \n",
       "25  [1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, ...   \n",
       "26  [1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, ...   \n",
       "27  [1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, ...   \n",
       "28  [0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, ...   \n",
       "29  [0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, ...   \n",
       "\n",
       "                                           test_preds  \\\n",
       "0   [0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, ...   \n",
       "1   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "2   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "3   [0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, ...   \n",
       "4   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "5   [0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, ...   \n",
       "6   [1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, ...   \n",
       "7   [1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, ...   \n",
       "8   [1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, ...   \n",
       "9   [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...   \n",
       "10  [0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, ...   \n",
       "11  [1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, ...   \n",
       "12  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "13  [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...   \n",
       "14  [0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, ...   \n",
       "15  [1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, ...   \n",
       "16  [1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, ...   \n",
       "17  [0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, ...   \n",
       "18  [0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, ...   \n",
       "19  [0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "20  [0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "21  [0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, ...   \n",
       "22  [1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, ...   \n",
       "23  [0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, ...   \n",
       "24  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "25  [0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "26  [0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, ...   \n",
       "27  [0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, ...   \n",
       "28  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "29  [1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, ...   \n",
       "\n",
       "                                                    w  \\\n",
       "0   [2.4355013e-14, 7.9422874e-15, 7.03134e-15, 2....   \n",
       "1   [0.00022351817, 4.2670813e-06, 3.4483182e-07, ...   \n",
       "2   [0.008354922, 0.0090138875, 0.00655974, 0.0081...   \n",
       "3   [3.4208396e-12, 2.596752e-12, 7.1247565e-12, 3...   \n",
       "4   [0.0005361984, 1.1695011e-05, 2.8312315e-06, 0...   \n",
       "5   [2.4601245e-06, 6.863402e-07, 8.4290446e-07, 2...   \n",
       "6   [8.181603e-05, 3.8962575e-05, 1.4325404e-05, 2...   \n",
       "7   [0.017023996, 0.013855415, 0.009946956, 0.0160...   \n",
       "8   [7.05444e-40, 5.20053e-40, 7.2889e-40, 8.51939...   \n",
       "9   [1.0404896e-17, 1.9620575e-17, 1.2769403e-17, ...   \n",
       "10  [5.97753e-40, 9.406e-41, 6.91294e-40, 4.4789e-...   \n",
       "11  [1.668519e-18, 4.668642e-19, 1.2115933e-18, 1....   \n",
       "12  [0.0075963885, 0.0050555468, 0.005596886, 0.00...   \n",
       "13  [0.0001340163, 8.552111e-05, 6.28521e-05, 0.00...   \n",
       "14  [5.5897563e-06, 2.3478788e-06, 1.7229743e-06, ...   \n",
       "15  [8.4675754e-05, 0.00057628466, 0.0007121916, 0...   \n",
       "16  [0.0014725752, 0.00066329376, 0.0012488001, 0....   \n",
       "17  [0.0013292617, 0.008167243, 0.008039634, 0.001...   \n",
       "18  [1.5982949e-05, 3.7255732e-06, 7.847019e-06, 1...   \n",
       "19  [0.0019204092, 0.0025825961, 0.0017644401, 0.0...   \n",
       "20  [0.00017957852, 6.103716e-05, 0.00014175565, 0...   \n",
       "21  [5.651253e-05, 0.00057762186, 5.8827693e-05, 0...   \n",
       "22  [5.655759e-08, 4.079454e-08, 1.4971452e-08, 5....   \n",
       "23  [7.958601e-14, 9.915393e-16, 3.1958437e-16, 1....   \n",
       "24  [6.6287266e-06, 2.0626212e-05, 1.928877e-06, 2...   \n",
       "25  [0.0001589426, 0.00074004167, 0.001365604, 0.0...   \n",
       "26  [0.0012638824, 0.0007535093, 0.0011331486, 0.0...   \n",
       "27  [2.5441565e-05, 4.879906e-06, 4.6271978e-07, 2...   \n",
       "28  [0.00040561936, 0.0003234212, 0.00010498321, 0...   \n",
       "29  [0.00038709503, 0.0005713174, 0.000559857, 0.0...   \n",
       "\n",
       "                                                    n  \n",
       "0   [1.0068504e-12, 3.2833878e-13, 2.9067967e-13, ...  \n",
       "1   [0.009240371, 0.00017640363, 1.4255548e-05, 0....  \n",
       "2   [0.34539732, 0.37263936, 0.27118346, 0.3379581...  \n",
       "3   [1.414195e-10, 1.0735124e-10, 2.9454159e-10, 1...  \n",
       "4   [0.022166753, 0.00048347857, 0.00011704476, 0....  \n",
       "5   [0.00010170298, 2.8373703e-05, 3.484616e-05, 0...  \n",
       "6   [0.0033823224, 0.0016107355, 0.0005922205, 0.0...  \n",
       "7   [0.7037819, 0.5727909, 0.41121295, 0.6641522, ...  \n",
       "8   [2.8818392e-38, 2.115419e-38, 2.9787628e-38, 3...  \n",
       "9   [4.3014443e-16, 8.11126e-16, 5.2789454e-16, 4....  \n",
       "10  [2.4366377e-38, 3.543377e-39, 2.8233412e-38, 1...  \n",
       "11  [6.8977546e-17, 1.9300438e-17, 5.008797e-17, 6...  \n",
       "12  [0.3140391, 0.20899925, 0.23137853, 0.2953196,...  \n",
       "13  [0.005540312, 0.0035354926, 0.0025983425, 0.00...  \n",
       "14  [0.00023108377, 9.706267e-05, 7.122876e-05, 0....  \n",
       "15  [0.003500545, 0.023823943, 0.029442417, 0.0059...  \n",
       "16  [0.06087712, 0.02742095, 0.051626123, 0.010079...  \n",
       "17  [0.05495245, 0.33763856, 0.33236313, 0.0640013...  \n",
       "18  [0.0006607444, 0.00015401735, 0.00032440035, 0...  \n",
       "19  [0.07939083, 0.10676603, 0.07294298, 0.0797757...  \n",
       "20  [0.0074238805, 0.0025233116, 0.005860261, 0.01...  \n",
       "21  [0.0023362609, 0.023879224, 0.002431971, 0.010...  \n",
       "22  [2.3381235e-06, 1.68647e-06, 6.1892854e-07, 2....  \n",
       "23  [3.2901318e-12, 4.099081e-14, 1.3211804e-14, 4...  \n",
       "24  [0.0002740354, 0.0008526996, 7.97409e-05, 0.00...  \n",
       "25  [0.0065707793, 0.030593753, 0.056454863, 0.005...  \n",
       "26  [0.052249633, 0.031150512, 0.04684502, 0.06300...  \n",
       "27  [0.0010517691, 0.00020173816, 1.9129106e-05, 0...  \n",
       "28  [0.01676854, 0.013370421, 0.004340067, 0.00850...  \n",
       "29  [0.016002733, 0.023618594, 0.023144813, 0.0263...  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sag = GetModelDf(\"sag\")\n",
    "sag_datasets = GetDatasetsByView(df_sag)\n",
    "df_sag#.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9f60ce82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0. biomarker en küçük 8. biomarker demek degil! (bana bu lazim) : argsort argsort\n",
    "# 8. biomarker en küçük biomarker demek! (bu[argsort] lazim degil)\n",
    "llm = [0.16110869, 0.14068228, 0.1535641,  0.05990085, 0.05777565, 0.01962589,\n",
    " 0.05002575, 0.0685641,  0.01497542, 0.10926928, 0.12729558, 0.16272868,\n",
    " 0.04551447, 0.02275075, 0.10391028, 0.08341169, 0.03719392, 0.07100126,\n",
    " 0.06683969, 0.06206064, 0.13141218, 0.08685517, 0.126066,   0.02624511,\n",
    " 0.16088009, 0.0366286,  0.02318038, 0.02092858, 0.10593221, 0.08378284,\n",
    " 0.11462475, 0.03707968, 0.01594063, 0.02346183, 0.10462112]\n",
    "# print(np.argsort(llm)) : EN KUCUK BIOMARKER 8. BIOMARKER\n",
    "# burada bir indiste bulunan biomarkerları karsilastirirken 8. bio ve 6. bio karsilastiriliyor: sacma\n",
    "# [ 8 32  5 27 13 26 33 23 25 31 16 12  6  4  3 19 18  7 17 15 29 21 14 34\n",
    "# 28  9 30 22 10 20  1  2 24  0 11]\n",
    "# print(np.argsort(np.argsort(llm))) # 8. BIOMARKER EN KUCUK BIOMARKER\n",
    "# burada ise 8. biomarkerin kacinci kucuk biomarker oldugu karsilastiriliyor: mantikli\n",
    "# [33 30 31 14 13  2 12 17  0 25 28 34 11  4 22 19 10 18 16 15 29 21 27  7\n",
    "# 32  8  5  3 24 20 26  9  1  6 23]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "49c105ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def PrintScores(datasets):\n",
    "    for dset_no, df_dset in enumerate(datasets):\n",
    "        for i in range(5):\n",
    "            train_labels, train_preds = df_dset['train_labels'].iloc[i], df_dset['train_preds'].iloc[i]\n",
    "            test_labels, test_preds = df_dset['test_labels'].iloc[i], df_dset['test_preds'].iloc[i]\n",
    "\n",
    "            f1_train = metrics.accuracy_score(train_labels, train_preds)\n",
    "            f1_test = metrics.accuracy_score(test_labels, test_preds)\n",
    "            #print('cv', i, ' ', f1, f2, f3)\n",
    "            print(f\"view {dset_no} cv {i} train {np.round(f1_train, 2)} test {np.round(f1_test, 2)}\")\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9715b335",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "view 0 cv 0 train 0.61 test 0.63\n",
      "view 0 cv 1 train 0.57 test 0.55\n",
      "view 0 cv 2 train 0.52 test 0.62\n",
      "view 0 cv 3 train 0.53 test 0.53\n",
      "view 0 cv 4 train 0.55 test 0.45\n",
      "\n",
      "view 1 cv 0 train 0.78 test 0.68\n",
      "view 1 cv 1 train 0.56 test 0.65\n",
      "view 1 cv 2 train 0.68 test 0.62\n",
      "view 1 cv 3 train 0.49 test 0.58\n",
      "view 1 cv 4 train 0.53 test 0.45\n",
      "\n",
      "view 2 cv 0 train 0.52 test 0.53\n",
      "view 2 cv 1 train 0.58 test 0.58\n",
      "view 2 cv 2 train 0.52 test 0.58\n",
      "view 2 cv 3 train 0.56 test 0.47\n",
      "view 2 cv 4 train 0.52 test 0.53\n",
      "\n",
      "view 3 cv 0 train 0.46 test 0.5\n",
      "view 3 cv 1 train 0.48 test 0.52\n",
      "view 3 cv 2 train 0.49 test 0.55\n",
      "view 3 cv 3 train 0.48 test 0.53\n",
      "view 3 cv 4 train 0.5 test 0.48\n",
      "\n",
      "view 4 cv 0 train 0.61 test 0.67\n",
      "view 4 cv 1 train 0.77 test 0.67\n",
      "view 4 cv 2 train 0.58 test 0.53\n",
      "view 4 cv 3 train 0.56 test 0.6\n",
      "view 4 cv 4 train 0.7 test 0.62\n",
      "\n",
      "view 5 cv 0 train 0.55 test 0.58\n",
      "view 5 cv 1 train 0.45 test 0.6\n",
      "view 5 cv 2 train 0.71 test 0.55\n",
      "view 5 cv 3 train 0.78 test 0.57\n",
      "view 5 cv 4 train 0.49 test 0.55\n",
      "\n"
     ]
    }
   ],
   "source": [
    "PrintScores(gcn_datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "10f5cde9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "view 0 cv 0 train 0.48 test 0.5\n",
      "view 0 cv 1 train 0.89 test 0.53\n",
      "view 0 cv 2 train 0.96 test 0.62\n",
      "view 0 cv 3 train 0.88 test 0.47\n",
      "view 0 cv 4 train 0.95 test 0.67\n",
      "\n",
      "view 1 cv 0 train 0.48 test 0.55\n",
      "view 1 cv 1 train 0.96 test 0.55\n",
      "view 1 cv 2 train 0.98 test 0.62\n",
      "view 1 cv 3 train 0.98 test 0.5\n",
      "view 1 cv 4 train 1.0 test 0.42\n",
      "\n",
      "view 2 cv 0 train 0.54 test 0.52\n",
      "view 2 cv 1 train 0.95 test 0.43\n",
      "view 2 cv 2 train 0.73 test 0.65\n",
      "view 2 cv 3 train 0.87 test 0.58\n",
      "view 2 cv 4 train 0.94 test 0.53\n",
      "\n",
      "view 3 cv 0 train 0.58 test 0.55\n",
      "view 3 cv 1 train 1.0 test 0.7\n",
      "view 3 cv 2 train 0.97 test 0.48\n",
      "view 3 cv 3 train 0.95 test 0.5\n",
      "view 3 cv 4 train 1.0 test 0.75\n",
      "\n",
      "view 4 cv 0 train 0.68 test 0.67\n",
      "view 4 cv 1 train 1.0 test 0.47\n",
      "view 4 cv 2 train 0.99 test 0.5\n",
      "view 4 cv 3 train 0.98 test 0.52\n",
      "view 4 cv 4 train 1.0 test 0.6\n",
      "\n",
      "view 5 cv 0 train 0.57 test 0.55\n",
      "view 5 cv 1 train 1.0 test 0.57\n",
      "view 5 cv 2 train 0.97 test 0.48\n",
      "view 5 cv 3 train 1.0 test 0.57\n",
      "view 5 cv 4 train 0.99 test 0.58\n",
      "\n"
     ]
    }
   ],
   "source": [
    "PrintScores(diffpool_datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5c2b6c3c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "view 0 cv 0 train 0.52 test 0.5\n",
      "view 0 cv 1 train 0.41 test 0.5\n",
      "view 0 cv 2 train 0.49 test 0.5\n",
      "view 0 cv 3 train 0.49 test 0.5\n",
      "view 0 cv 4 train 0.51 test 0.5\n",
      "\n",
      "view 1 cv 0 train 0.49 test 0.5\n",
      "view 1 cv 1 train 0.49 test 0.5\n",
      "view 1 cv 2 train 0.49 test 0.5\n",
      "view 1 cv 3 train 0.52 test 0.5\n",
      "view 1 cv 4 train 0.43 test 0.5\n",
      "\n",
      "view 2 cv 0 train 0.49 test 0.5\n",
      "view 2 cv 1 train 0.49 test 0.5\n",
      "view 2 cv 2 train 0.54 test 0.5\n",
      "view 2 cv 3 train 0.5 test 0.5\n",
      "view 2 cv 4 train 0.5 test 0.5\n",
      "\n",
      "view 3 cv 0 train 0.52 test 0.5\n",
      "view 3 cv 1 train 0.53 test 0.5\n",
      "view 3 cv 2 train 0.48 test 0.5\n",
      "view 3 cv 3 train 0.46 test 0.5\n",
      "view 3 cv 4 train 0.49 test 0.55\n",
      "\n",
      "view 4 cv 0 train 0.51 test 0.52\n",
      "view 4 cv 1 train 0.54 test 0.5\n",
      "view 4 cv 2 train 0.5 test 0.5\n",
      "view 4 cv 3 train 0.52 test 0.52\n",
      "view 4 cv 4 train 0.47 test 0.5\n",
      "\n",
      "view 5 cv 0 train 0.56 test 0.5\n",
      "view 5 cv 1 train 0.48 test 0.5\n",
      "view 5 cv 2 train 0.51 test 0.58\n",
      "view 5 cv 3 train 0.49 test 0.5\n",
      "view 5 cv 4 train 0.5 test 0.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "PrintScores(gunet_datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "82c45ef8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "view 0 cv 0 train 0.53 test 0.57\n",
      "view 0 cv 1 train 0.53 test 0.5\n",
      "view 0 cv 2 train 0.45 test 0.53\n",
      "view 0 cv 3 train 0.48 test 0.5\n",
      "view 0 cv 4 train 0.43 test 0.52\n",
      "\n",
      "view 1 cv 0 train 0.46 test 0.52\n",
      "view 1 cv 1 train 0.5 test 0.53\n",
      "view 1 cv 2 train 0.45 test 0.52\n",
      "view 1 cv 3 train 0.5 test 0.58\n",
      "view 1 cv 4 train 0.53 test 0.68\n",
      "\n",
      "view 2 cv 0 train 0.5 test 0.52\n",
      "view 2 cv 1 train 0.53 test 0.5\n",
      "view 2 cv 2 train 0.52 test 0.52\n",
      "view 2 cv 3 train 0.51 test 0.58\n",
      "view 2 cv 4 train 0.53 test 0.5\n",
      "\n",
      "view 3 cv 0 train 0.45 test 0.5\n",
      "view 3 cv 1 train 0.48 test 0.5\n",
      "view 3 cv 2 train 0.52 test 0.52\n",
      "view 3 cv 3 train 0.49 test 0.52\n",
      "view 3 cv 4 train 0.48 test 0.53\n",
      "\n",
      "view 4 cv 0 train 0.51 test 0.53\n",
      "view 4 cv 1 train 0.42 test 0.5\n",
      "view 4 cv 2 train 0.52 test 0.55\n",
      "view 4 cv 3 train 0.55 test 0.53\n",
      "view 4 cv 4 train 0.5 test 0.52\n",
      "\n",
      "view 5 cv 0 train 0.49 test 0.6\n",
      "view 5 cv 1 train 0.55 test 0.5\n",
      "view 5 cv 2 train 0.49 test 0.53\n",
      "view 5 cv 3 train 0.53 test 0.5\n",
      "view 5 cv 4 train 0.54 test 0.52\n",
      "\n"
     ]
    }
   ],
   "source": [
    "PrintScores(gat_datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "59f09cbf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "view 0 cv 0 train 0.65 test 0.47\n",
      "view 0 cv 1 train 0.76 test 0.62\n",
      "view 0 cv 2 train 0.68 test 0.58\n",
      "view 0 cv 3 train 0.66 test 0.5\n",
      "view 0 cv 4 train 0.5 test 0.5\n",
      "\n",
      "view 1 cv 0 train 0.7 test 0.48\n",
      "view 1 cv 1 train 0.66 test 0.53\n",
      "view 1 cv 2 train 0.66 test 0.55\n",
      "view 1 cv 3 train 0.47 test 0.62\n",
      "view 1 cv 4 train 0.5 test 0.5\n",
      "\n",
      "view 2 cv 0 train 0.63 test 0.52\n",
      "view 2 cv 1 train 0.63 test 0.53\n",
      "view 2 cv 2 train 0.64 test 0.6\n",
      "view 2 cv 3 train 0.69 test 0.5\n",
      "view 2 cv 4 train 0.5 test 0.5\n",
      "\n",
      "view 3 cv 0 train 0.66 test 0.43\n",
      "view 3 cv 1 train 0.66 test 0.53\n",
      "view 3 cv 2 train 0.68 test 0.6\n",
      "view 3 cv 3 train 0.71 test 0.55\n",
      "view 3 cv 4 train 0.5 test 0.5\n",
      "\n",
      "view 4 cv 0 train 0.62 test 0.48\n",
      "view 4 cv 1 train 0.67 test 0.52\n",
      "view 4 cv 2 train 0.68 test 0.55\n",
      "view 4 cv 3 train 0.67 test 0.45\n",
      "view 4 cv 4 train 0.5 test 0.5\n",
      "\n",
      "view 5 cv 0 train 0.61 test 0.5\n",
      "view 5 cv 1 train 0.7 test 0.52\n",
      "view 5 cv 2 train 0.7 test 0.53\n",
      "view 5 cv 3 train 0.67 test 0.5\n",
      "view 5 cv 4 train 0.5 test 0.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "PrintScores(sag_datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7989cd37",
   "metadata": {},
   "outputs": [],
   "source": [
    "def CalculateReprScore(df_datasets):\n",
    "    def Calculate_mIv(current_df):\n",
    "        m_M_v = np.asarray(list(current_df['n'].map(lambda x: x).to_numpy())).T\n",
    "        m_I_v = np.array([LA.norm(nth_bios) for nth_bios in m_M_v])\n",
    "        return m_I_v\n",
    "    \n",
    "    def CalculateMd(m_I_v_s):\n",
    "        m_d = None\n",
    "        d_list = []\n",
    "        for i in range(len(m_I_v_s)):\n",
    "            for k in range(i+1, len(m_I_v_s)):\n",
    "                d_ik = LA.norm(m_I_v_s[i] - m_I_v_s[k]) ** 2\n",
    "                d_list.append(d_ik)\n",
    "\n",
    "        m_d = np.array(d_list)\n",
    "        return m_d\n",
    "\n",
    "    def FindTopIndices_with_L2AcrossViewsFolds(df_datasets):\n",
    "        all_datasets = np.asarray(list(df_datasets[0]['n'].map(lambda x: x).to_numpy())).T\n",
    "        for i, df_dataset in enumerate(df_datasets):\n",
    "            if i > 0:\n",
    "                all_datasets = np.concatenate((all_datasets, np.asarray(list(df_dataset['n'].map(lambda x: x).to_numpy())).T), axis=1)\n",
    "\n",
    "        bios_scores = np.array([LA.norm(nth_bios) for nth_bios in all_datasets])\n",
    "        #print(bios_scores)\n",
    "        bios_order = np.argsort(bios_scores)\n",
    "        #print(bios_order)\n",
    "        top_bio_indices = bios_order[-Ks:]\n",
    "        #print(bios_scores[top_bio_indices])\n",
    "        return top_bio_indices\n",
    "    \n",
    "    def FindTopIndices_with_Averaging(m_I_v_s):\n",
    "        sorted_lst = np.asarray(np.argsort(np.argsort(m_I_v_s))) # sorted_lst.shape: (6, 35)\n",
    "        sorted_bios = np.argsort(np.average(sorted_lst, axis=0))\n",
    "        top_bio_indices = sorted_bios[-Ks:]\n",
    "        return top_bio_indices\n",
    "    \n",
    "    \n",
    "    \n",
    "    m_I_v_s = []\n",
    "    for df_dset in df_datasets:\n",
    "        m_I_v_s.append(Calculate_mIv(df_dset))\n",
    "    \n",
    "    Ks_bios = FindTopIndices_with_Averaging(m_I_v_s)\n",
    "    #Ks_bios = FindTopIndices_with_L2AcrossViewsFolds(df_datasets)\n",
    "    print('Top bio indices: ', (list(reversed(Ks_bios))))\n",
    "    \n",
    "    m_I_v_s_Ks_bios = [] # highest Ks biomarkers of m_I_v_s\n",
    "    for mIv in m_I_v_s:\n",
    "        m_I_v_s_Ks_bios.append(mIv[Ks_bios])    \n",
    "    \n",
    "\n",
    "    m_d = CalculateMd(m_I_v_s_Ks_bios) # pass m_I_v_s\n",
    "    repr_score = -(LA.norm(m_d - np.array([0] * len(m_d)))) # 1 - cikartildi !!\n",
    "    return abs(repr_score), m_I_v_s_Ks_bios, list(reversed(Ks_bios))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "4d4dca52",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ks = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "4449937a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top bio indices:  [0, 11, 24, 2, 1, 22, 20, 10, 9, 14, 30, 34, 28, 15, 21]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6900580746820978"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcn_repr_score, gcn_mIvs, gcn_bio_indices = CalculateReprScore(gcn_datasets)\n",
    "gcn_repr_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "59cf5f70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top bio indices:  [27, 9, 23, 13, 4, 19, 34, 31, 33, 11, 20, 7, 1, 24, 29]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2.5189928270684545"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diffpool_repr_score, diffpool_mIvs, diffpool_bio_indices = CalculateReprScore(diffpool_datasets)\n",
    "diffpool_repr_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "65bd88f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top bio indices:  [25, 12, 27, 14, 7, 17, 34, 31, 22, 23, 19, 2, 9, 5, 3]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.8683306168365306"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gat_repr_score, gat_mIvs, gat_bio_indices = CalculateReprScore(gat_datasets)\n",
    "gat_repr_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "6ac7c24d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top bio indices:  [5, 30, 14, 18, 11, 4, 24, 9, 33, 27, 0, 19, 16, 34, 8]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10.748367865138226"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gunet_repr_score, gunet_mIvs, gunet_bio_indices = CalculateReprScore(gunet_datasets)\n",
    "gunet_repr_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "6f7f7e43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top bio indices:  [6, 29, 9, 23, 30, 12, 28, 24, 27, 17, 13, 32, 22, 1, 34]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "14.2942905596082"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sag_repr_score, sag_mIvs, sag_bio_indices = CalculateReprScore(sag_datasets)\n",
    "sag_repr_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "9c408ccb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PART:  0 INDICES:  15 8\n",
      "[21, 15, 28, 34, 30, 14, 9]\n",
      "34 occurred 3 times\n",
      "PART:  1 INDICES:  8 1\n",
      "[10, 20, 22, 1, 2, 24, 11]\n",
      "9 occurred 3 times\n",
      "24 occurred 3 times\n"
     ]
    }
   ],
   "source": [
    "part_size = 7\n",
    "for i in range(2):\n",
    "    start, end = i*part_size, (i+1)*part_size\n",
    "    print('PART: ', i, 'INDICES: ', 15-start, 15-end)\n",
    "    print(gcn_bio_indices[::-1][start:end])\n",
    "    first_8_bios = np.concatenate((gcn_bio_indices[::-1][start:end], diffpool_bio_indices[::-1][start:end], gat_bio_indices[::-1][start:end], sag_bio_indices[::-1][start:end], gunet_bio_indices[::-1][start:end])).tolist()\n",
    "    for bio in np.unique(first_8_bios):\n",
    "        if first_8_bios.count(bio) >= 3:\n",
    "            print(f\"{bio} occurred {first_8_bios.count(bio)} times\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "5fa6e817",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PART:  0 INDICES:  0 8\n",
      "[0, 11, 24, 2, 1, 22, 20, 10]\n",
      "9 occurred 3 times\n",
      "24 occurred 3 times\n"
     ]
    }
   ],
   "source": [
    "part_size = 8\n",
    "for i in range(Ks // part_size):\n",
    "    start, end = i*part_size, (i+1)*part_size\n",
    "    print('PART: ', i, 'INDICES: ', start, end)\n",
    "    print(gcn_bio_indices[start:end])\n",
    "    first_8_bios = np.concatenate((gcn_bio_indices[start:end], diffpool_bio_indices[start:end], gat_bio_indices[start:end], sag_bio_indices[start:end], gunet_bio_indices[start:end])).tolist()\n",
    "    for bio in np.unique(first_8_bios):\n",
    "        if first_8_bios.count(bio) >= 3:\n",
    "            print(f\"{bio} occurred {first_8_bios.count(bio)} times\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "042c59cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def GetHigherLevelReprMatrix():\n",
    "    for i in range(6):\n",
    "        print('GCN: ', gcn_bio_indices)\n",
    "        print('DIFFPOOL: ', diffpool_bio_indices)\n",
    "        print('GAT: ', gat_bio_indices)\n",
    "        print('GUNET: ', gunet_bio_indices)\n",
    "        print('SAG: ', sag_bio_indices)\n",
    "            \n",
    "        gnns_bios = [gcn_bio_indices, diffpool_bio_indices, gat_bio_indices, gunet_bio_indices, sag_bio_indices]\n",
    "        #gnns_bios = [gcn_top_bios, diffpool_top_bios]\n",
    "    \n",
    "        repr_matrix = np.zeros((len(gnns_bios), len(gnns_bios)))\n",
    "    \n",
    "        print()\n",
    "    \n",
    "        for j in range(len(gnns_bios)):\n",
    "            gnn1_bios = gnns_bios[j]\n",
    "            for k in range(len(gnns_bios)):\n",
    "                gnn2_bios = gnns_bios[k]\n",
    "                repr_matrix[j,k] = sum([1 if (gnn1_bios[b] in gnn2_bios) else 0 for b in range(Ks)])\n",
    "                \n",
    "        #print(((repr_matrix / Ks).sum(axis=0)-1)/(5-1)) # 5-1 : GNN mimari sayisi-1[-1 var çünkü her satırda skor olarak 1 var]. Skorları 0-1 arasina normalize etmek icin.\n",
    "        #print(repr_matrix / Ks) \n",
    "        #print()\n",
    "        #print()\n",
    "        \n",
    "        return repr_matrix / Ks # all views have the same matrix and indices. doesn't matter which one is returned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "a7b17240",
   "metadata": {},
   "outputs": [],
   "source": [
    "def PrintHigherLevelReprScores(final_matrix):\n",
    "    repr_scores = (np.sum(final_matrix, axis=0) - 1) / 4\n",
    "    model_orders = ['diffpool', 'gat', 'gcn', 'gunet', 'sag']\n",
    "    model_sort_with_repr_scores = np.array(model_orders)[list(reversed(np.argsort(repr_scores)))]\n",
    "    repr_score_dict = {model_sort_with_repr_scores[i]: np.round(np.sort(repr_scores)[::-1][i], 3) for i in range(len(model_orders))}\n",
    "    print('REPR SCORE : ', repr_score_dict)   \n",
    "    print('REPR MATRIX: '), print(final_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "f915d826",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN:  [0, 11, 24, 2, 1, 22, 20, 10, 9, 14, 30, 34, 28, 15, 21]\n",
      "DIFFPOOL:  [27, 9, 23, 13, 4, 19, 34, 31, 33, 11, 20, 7, 1, 24, 29]\n",
      "GAT:  [25, 12, 27, 14, 7, 17, 34, 31, 22, 23, 19, 2, 9, 5, 3]\n",
      "GUNET:  [5, 30, 14, 18, 11, 4, 24, 9, 33, 27, 0, 19, 16, 34, 8]\n",
      "SAG:  [6, 29, 9, 23, 30, 12, 28, 24, 27, 17, 13, 32, 22, 1, 34]\n",
      "\n",
      "REPR SCORE :  {'gat': 0.483, 'sag': 0.45, 'gunet': 0.433, 'gcn': 0.417, 'diffpool': 0.417}\n",
      "REPR MATRIX: \n",
      "[[1.         0.4        0.33333333 0.46666667 0.46666667]\n",
      " [0.4        1.         0.46666667 0.53333333 0.53333333]\n",
      " [0.33333333 0.46666667 1.         0.4        0.46666667]\n",
      " [0.46666667 0.53333333 0.4        1.         0.33333333]\n",
      " [0.46666667 0.53333333 0.46666667 0.33333333 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "PrintHigherLevelReprScores(GetHigherLevelReprMatrix())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "824b19ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "def GetIQCVector(dataset, model_bio_indices):\n",
    "    sorted_bios = np.asarray(list(dataset['w'].map(lambda x: np.argsort(np.argsort(x))).to_numpy())).T\n",
    "    \n",
    "    iqc_vect = []\n",
    "    for bio in sorted_bios:\n",
    "        #print(bio)\n",
    "        npc = np.sort(bio)\n",
    "        #print(npc)\n",
    "        Q1 = np.median(npc[:len(npc)//2])\n",
    "        Q3 = np.median(npc[len(npc)//2:])\n",
    "        IQR = Q3 - Q1\n",
    "        #print(IQR)\n",
    "        #print()\n",
    "        \n",
    "        iqc_vect.append(IQR)\n",
    "    \n",
    "    #return np.sort(np.array(iqc_vect))    \n",
    "    return np.array(iqc_vect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0058ed37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN TOP BIOMARKER INDICES:  [0, 11, 24, 2, 1]\n",
      "IQC AVR:  1.6285714285714286\n"
     ]
    }
   ],
   "source": [
    "iqc_gcn = GetIQCVector(df_gcn, gcn_bio_indices)\n",
    "print('GCN TOP BIOMARKER INDICES: ', gcn_bio_indices)\n",
    "print('IQC AVR: ', np.average(iqc_gcn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b568615a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DIFFPOOL TOP BIOMARKER INDICES:  [27, 9, 23, 13, 4]\n",
      "IQC AVR:  5.457142857142857\n"
     ]
    }
   ],
   "source": [
    "iqc_diffpool = GetIQCVector(df_diffpool, diffpool_bio_indices)\n",
    "print('DIFFPOOL TOP BIOMARKER INDICES: ', diffpool_bio_indices)\n",
    "print('IQC AVR: ', np.average(iqc_diffpool))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7fd2c823",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAT TOP BIOMARKER INDICES:  [25, 27, 14, 12, 22]\n",
      "IQC AVR:  16.057142857142857\n"
     ]
    }
   ],
   "source": [
    "iqc_gat = GetIQCVector(df_gat, gat_bio_indices)\n",
    "print('GAT TOP BIOMARKER INDICES: ', gat_bio_indices)\n",
    "print('IQC AVR: ', np.average(iqc_gat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bba28995",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SAG TOP BIOMARKER INDICES:  [29, 6, 27, 30, 17]\n",
      "IQC AVR:  16.0\n"
     ]
    }
   ],
   "source": [
    "iqc_sag = GetIQCVector(df_sag, sag_bio_indices)\n",
    "print('SAG TOP BIOMARKER INDICES: ', sag_bio_indices)\n",
    "print('IQC AVR: ', np.average(iqc_sag))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dbcac84c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GUNET TOP BIOMARKER INDICES:  [33, 30, 5, 27, 9]\n",
      "IQC AVR:  16.6\n"
     ]
    }
   ],
   "source": [
    "iqc_gunet = GetIQCVector(df_gunet, gunet_bio_indices)\n",
    "print('GUNET TOP BIOMARKER INDICES: ', gunet_bio_indices)\n",
    "print('IQC AVR: ', np.average(iqc_gunet))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "474fa880",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.15798319327731\n",
      "1.356302521008403\n",
      "16.035294117647055\n",
      "16.70084033613445\n",
      "15.8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "biomarker_list = np.array(list(range(1, len(iqc_gcn)+1)))\n",
    "\n",
    "def FitLine(iqc_list, label):\n",
    "    b, a = np.polyfit(biomarker_list, iqc_list, deg=1)\n",
    "    print(a)\n",
    "    plt.plot(biomarker_list, a + b * biomarker_list) # , label=label\n",
    "\n",
    "plt.scatter(biomarker_list, iqc_diffpool, label = \"DiffPool\", marker =\"v\", alpha=1)\n",
    "plt.scatter(biomarker_list, iqc_gcn, label = \"GCN\", alpha=0.7)\n",
    "plt.scatter(biomarker_list, iqc_sag, label = \"SAG\", marker =\"s\", alpha=1)\n",
    "plt.scatter(biomarker_list, iqc_gunet, label = \"g-U-Nets\", marker =\"*\", alpha=0.8)\n",
    "plt.scatter(biomarker_list, iqc_gat, label = \"GAT\", marker =\"^\", alpha=0.6)\n",
    "\n",
    "\n",
    "FitLine(iqc_diffpool, \"diffpool\")\n",
    "FitLine(iqc_gcn, \"gcn\")\n",
    "FitLine(iqc_sag, \"sag\")\n",
    "FitLine(iqc_gunet, \"gunet\")\n",
    "FitLine(iqc_gat, \"gat\")\n",
    "\n",
    "plt.xlabel('Biomarkers')\n",
    "plt.ylabel('IQR Scores')\n",
    "plt.title('Comparison')\n",
    "plt.legend()\n",
    "#plt.show()\n",
    "plt.savefig('iqr.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5689d02e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "our_method_scores = np.array([gcn_repr_score, diffpool_repr_score, gunet_repr_score, gat_repr_score, sag_repr_score])\n",
    "avg_iqr_scores = np.array([np.average(iqc_gcn), np.average(iqc_diffpool), np.average(iqc_gunet), np.average(iqc_gat), np.average(iqc_sag)])\n",
    "\n",
    "sorted_indices = np.argsort(our_method_scores)\n",
    "our_method_scores = our_method_scores[sorted_indices] # kucukten buyuge siraliyoruz linear bir dogru gozuksun diye\n",
    "avg_iqr_scores = avg_iqr_scores[sorted_indices] # modellerin ayni siralamasini buraya da tasimak zorundayiz. aksi halde map olmaz pairler\n",
    "\n",
    "plt.xlabel('Reproducibility Scores')\n",
    "plt.ylabel('Average of IQR Scores')\n",
    "plt.title('Internal Consistency')\n",
    "plt.legend()\n",
    "\n",
    "plt.plot(our_method_scores, avg_iqr_scores) \n",
    "plt.savefig('int_cons.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2d19ca30",
   "metadata": {},
   "outputs": [],
   "source": [
    "def GetAssignedBiomarkerScores(df_datasets):\n",
    "    all_datasets = np.asarray(list(df_datasets[0]['n'].map(lambda x: x).to_numpy())).T\n",
    "    for i, df_dataset in enumerate(df_datasets):\n",
    "        if i > 0:\n",
    "            all_datasets = np.concatenate((all_datasets, np.asarray(list(df_dataset['n'].map(lambda x: x).to_numpy())).T), axis=1)\n",
    "\n",
    "    bios_scores = np.array([LA.norm(nth_bios) for nth_bios in all_datasets])\n",
    "    return bios_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "df9575dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotBiomarkerImportanceComparison(bio_scores, y_title):\n",
    "    print(y_title)\n",
    "    \n",
    "    stdev = np.std(bio_scores)\n",
    "    mean = np.average(bio_scores)    \n",
    "    print(f\"mean:{mean:.2f}, stdev:{stdev:.2f}\")\n",
    "    \n",
    "    important_indices = np.where((bio_scores > mean + stdev))\n",
    "    unimportant_indices = np.where((bio_scores <= mean + stdev))\n",
    "\n",
    "    important_bios = (bio_scores > mean + stdev).astype(float)\n",
    "    unimportant_bios = (bio_scores <= mean + stdev).astype(float)\n",
    "\n",
    "    important_bios[important_indices] = bio_scores[important_indices]\n",
    "    unimportant_bios[unimportant_indices] = bio_scores[unimportant_indices]\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.stem(np.array(list(range(35)))+1, important_bios, 'g') \n",
    "    ax.stem(np.array(list(range(35)))+1, unimportant_bios, 'r')\n",
    "    plt.xlabel('Biomarkers')\n",
    "    plt.ylabel(y_title)\n",
    "    plt.title('Biomarker Importance Comparison')\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "48b6a106",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.603862   3.9179237  4.2638907  1.7186604  1.7869787  0.58513576\n",
      " 1.4034675  1.7619854  0.62092495 3.193383   3.7032678  4.5760665\n",
      " 1.2898357  0.736661   3.067084   2.5329714  1.0357927  1.9052196\n",
      " 1.9327415  1.8616227  3.6942778  2.5746174  3.7360358  0.8413342\n",
      " 4.56118    0.91135037 0.565278   0.77207905 3.0232272  2.5456328\n",
      " 3.1191065  1.0600488  0.47222242 0.61723757 3.0426044 ]\n",
      "L2 Score of Biomarkers\n",
      "mean:2.23, stdev:1.32\n"
     ]
    },
    {
     "data": {
      "image/png": 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9eRkZGeXefqY4ffo0oqOjddM3b95Ebm4unnrqKQBAamoqrl27huTkZEREROiWy8zMrPRju7q6YvDgwRg8eDCKioqgUqkwc+ZMTJ482arve7IPHKNCNnHv3j1s27YNzs7Oel9sJSkUCsTGxuL777/X646+dOkS1q5di86dO8PDw8Ms9SgUCkiSpHfa5blz5yp8ldSYmBisX78eW7ZswQsvvKD761OlUkGhUGD69OkGf+0JIXDt2rUKPwdLKj71tdiCBQsAaK+PA0D35TZv3jy95ebOnQtAO9aiPFxdXY2GD4VCYbC9FixYYHCabHnFxsbC3d0ds2bNMjjNuvhx2rZtiwYNGuCjjz4yGqYfdvrrgAED0KJFC8ycORP79u0zmF9QUIC33noLAPD444+jbt26+Oyzz/QOO27evBnp6enl3n6mWLZsGe7du6ebXrJkCe7fv697TYt7KUpu96KiIixevLhSj1t6H3d2dkbTpk0hhMC9e/es+r4n+8AeFbKKzZs36wbfXb58GWvXrsXp06cxadKkB37ovPfee9i+fTs6d+6MESNGoFq1ali6dCkKCwsxZ84cs9XXs2dPzJ07F3FxcYiPj8fly5exaNEihIaG4tixYxW6z379+mHFihUYMmQIPDw8sHTpUjRo0ADvvfceJk+ejHPnzqFfv35wd3dHZmYmNm7ciOHDh+ONN94w2/Myl8zMTPTp0wdxcXHYt28f1qxZg/j4eISFhQEAwsLCkJCQgGXLlukOGRw4cACrVq1Cv3799P5yf5C2bdtix44dmDt3Lvz9/RESEoIOHTqgV69eWL16NTw9PdG0aVPs27cPO3bsQK1atSr0fDw8PPDJJ5/gpZdeQrt27RAfHw9vb2/8+uuvuH37NlatWgUnJyd88cUX6NGjB5o1a4ahQ4eiXr16yMnJwa5du+Dh4YEffvihzMeoXr06kpOTERMTg4iICAwaNAhPPvkkqlevjt9++w1r166Ft7c3Zs6cierVq+ODDz7A0KFDERkZiWeffRaXLl3C/PnzERwcjHHjxlXoeT5IUVERunbtikGDBiEjIwOLFy9G586d0adPHwBAp06d4O3tjYSEBIwePRqSJGH16tWVPpwSGxsLX19fPPnkk/Dx8UF6ejoWLlyInj176gYfW+t9T3bC+icakSMxdnpyjRo1RKtWrcSSJUv0TikUwvD0ZCGEOHLkiOjevbtwc3MTNWvWFNHR0brra5R+nIMHD+q1GztlVwjtKZ6urq56bcuXLxcNGzYUSqVSNG7cWKxYsUK3fukaExMTDZ5r6euoFFu8eLHBtSc2bNggOnfuLFxdXYWrq6to3LixSExMFBkZGbplIiMjH3oa58Oea1n3ERQUJHr27GnQXvq5Fd/nyZMnxYABA4S7u7vw9vYWI0eONDit9969e2L69OkiJCREVK9eXQQGBorJkyfrncb6oMcWQns6bUREhHBxcREAdKcqX79+XQwdOlTUrl1buLm5ie7du4tTp06JoKAgvdOZy9oPdu3aJQCIXbt26bX/5z//EZ06dRIuLi7Cw8NDtG/fXqxbt05vmaNHjwqVSiVq1aollEqlCAoKEoMGDRI7d+40+hxKu379upg6dapo0aKFqFmzpqhRo4Zo3ry5mDx5ssjNzdVb9uuvvxatW7cWSqVSPPLII+K5554T58+f11vG2L4rRPlf6+JttHv3bjF8+HDh7e0t3NzcxHPPPSeuXbumt+7evXvFE088IVxcXIS/v7+YMGGC7jT2ktvyQftq6dOTly5dKiIiInTbs0GDBuLNN98UeXl5eutV5n1f1utN9kkSgqONiMi4adOmYfr06bhy5Qpq165t63LIDFauXImhQ4fi4MGDemfiEckVx6gQERGRbDGoEBERkWwxqBAREZFscYwKERERyRZ7VIiIiEi2GFSIiIhItuz6gm8ajQYXLlyAu7t7uX9jhIiIiGxLCIGCggL4+/vDyenBfSZ2HVQuXLjA33wgIiKyU9nZ2QgICHjgMnYdVIovt5ydnc3ffiAiIrIT+fn5CAwM1H2PP4hdB5Xiwz0eHh4MKkRERHamPMM2OJiWiIiIZItBhYiIiGSLQYWIiIhki0GFiIiIZItBhYiIiGSLQYWIiIhki0GFiIiIZItBhYiIiGSLQYWIiIhky66vTGspao0ae7L2ILcgF37ufgivHw6Fk8LWZRERETkcBpVSktOTMWbLGJzPP69rC/AIwPy4+VA1UdmwMiIiIsfDQz8lJKcnY8A3A/RCCgDk5OdgwDcDkJyebKPKyB6pNWqknkvFuuPrkHouFWqN2tYlERHZHfao/E2tUWPMljEQEAbzBAQkSBi7ZSz6NurLw0D0UOyZI3vDQ94kV+xR+duerD0GPSklCQhk52djT9YeK1ZF9og9c2RvktOTETw/GNGrohGfHI/oVdEInh/MfZVkgUHlb7kFuWZdjhzTw3rmAGDslrE8DESywWBNcseg8jc/dz+zLkeOiT1zZE8YrMkeMKj8Lbx+OAI8AiBBMjpfgoRAj0CE1w+3cmVkT9gzR/aEwZrsAYPK3xROCsyPmw8ABmGleHpe3DwOLqMHYs8c2RMGa7IHDColqJqokDQoCf7u/nrtAR4BSBqUxLM16KHYM0f2hMGa7AGDSimqJiqcHHFSN50Sn4LMMZkMKVQu7Jkje8JgTfaAQcWIkl8iEUER/FIhk7BnjuwFgzXZAwYVIgtgzxzZCwZrkjtemZbIQtgzR/ZC1USFmJAYeH7gCUAbrGMbxHKfJVlgjwoRETFYk2wxqBAREZFsMagQERGRbDGoEBERkWwxqBAREZFsMagQERGRbDGoEBERkWwxqBAREZFsMagQERGRbDGoEBERkWwxqBAREZFs8bd+bEitUWNP1h7kFuTCz90P4fXDedlqIiKiEhhUbCQ5PRljtozB+fzzurYAjwDMj5vPXyslIiL6Gw/92EByejIGfDNAL6QAQE5+DgZ8MwDJ6ck2qoyIiEheGFSsTK1RY8yWMRAQBvOK28ZuGQu1Rm3t0oiIiGSHQcXK9mTtMehJKUlAIDs/G3uy9lixKiIiInliULGy3IJcsy5HRERUlTGoWJmfu59ZlyMiIqrKGFSsLLx+OAI8AiBBMjpfgoRAj0CE1w+3cmVERETyw6BiZQonBebHzQcAg7BSPD0vbh6vp0JERAQGFZtQNVEhaVAS/N399doDPAKQNCiJ11EhIiL6Gy/4ZiOqJirEhMTA8wNPAEBKfApiG8SyJ4WIiKgE9qjYUMlQEhEUwZBCRERUCoMKERERyRaDChEREckWgwoRERHJFoMKERERyRaDChEREckWgwoRERHJFoMKERERyRaDChEREcmWbILK7NmzIUkSxo4da+tSiIiISCZkEVQOHjyIpUuXomXLlrYuhYiIiGTE5kHl5s2beO655/D555/D29vb1uUQERGRjNg8qCQmJqJnz56IiYl56LKFhYXIz8/XuxEREVHVZdNfT16/fj2OHDmCgwcPlmv5WbNmYfr06RauioiIiOTCZj0q2dnZGDNmDL766ivUqFGjXOtMnjwZeXl5ult2draFqyw/tUaN1HOpWHd8HVLPpUKtUdu6JCIiIrtnsx6Vw4cP4/Lly2jTpo2uTa1W46effsLChQtRWFgIhUKht45SqYRSqbR2qQ+VnJ6MMVvG4Hz+eV1bgEcA5sfNh6qJyoaVERER2TebBZWuXbvi+PHjem1Dhw5F48aNMXHiRIOQIlfJ6ckY8M0ACAi99pz8HAz4ZgCSBiUxrBAREVWQzYKKu7s7mjdvrtfm6uqKWrVqGbTLlVqjxpgtYwxCCgAICEiQMHbLWPRt1BcKJ/sIXkRERHJi87N+7NmerD16h3tKExDIzs/Gnqw9VqyKiIio6rDpWT+lpaam2roEk+QW5Jp1OSIiItLHHpVK8HP3M+tyREREpI9BpRLC64cjwCMAEiSj8yVICPQIRHj9cCtXRkREVDUwqFSCwkmB+XHzAcAgrBRPz4ubx4G0REREFcSgUkmqJiokDUqCv7u/XnuARwBPTSYiIqokWQ2mtVeqJirEhMTA8wNPAEBKfApiG8SyJ4WIiKiS2KNiJiVDSURQBEMKERGRGTCoEBERkWzx0A8RkUypNWrsydqD3IJc+Ln7Ibx+OHtryeEwqBARyRB/7JRIi4d+iIhkpvjHTkv/REfxj50mpyfbqDIi62OPCtFDsPudSrPkPsEfOyXSx6BC9ADsfqfSLL1PmPJjp1HBUZV+PCK546EfojKw+51Ks8Y+wR87JdLHoEJkxMO63wFg7JaxUGvU1i6NbMRa+wR/7JRIH4MKkRGmdL+TY7DWPsEfOyXSx6BCZAS736k0a+0T/LFTIn0MKkRGsPudSrPmPsEfOyX6B4MKkRHsfqfSrL1PqJqocHLESd10SnwKMsdkMqSQw2FQITKC3e9Umi32Cf7YKRGDClGZ2P1OpXGfILI+XvCN6AFUTVSICYmB5weeALTd77ENYvmXrQPjPkFkXexRIXoIdr9TadwniKyHQYWIiIhki0GFiIiIZItBhYiIiGSLQYWIiIhki0GFiIiIZItBhYiIiGSLQYWIiIhki0GFiIiIZItBhYiIiGSLQYWIiIhki0GFiIiIZItBhYiIiGSLQYWIiIhki0GFiIiIZItBhYiIiGSLQYWIiIhkq9JBRa1WIy0tDdevXzdHPUREREQ6JgeVsWPHYvny5QC0ISUyMhJt2rRBYGAgUlNTzV0fEREROTCTg0pSUhLCwsIAAD/88AMyMzNx6tQpjBs3Dm+99ZbZCyQiIiLHZXJQuXr1Knx9fQEAKSkpGDhwIB577DH861//wvHjx81eIBERETkuk4OKj48PTp48CbVajS1btqBbt24AgNu3b0OhUJi9QCIiInJc1UxdYejQoRg0aBD8/PwgSRJiYmIAAPv370fjxo3NXiARERE5LpODyrRp09CiRQtkZWVh4MCBUCqVAACFQoFJkyaZvUAiIiJyXCYFlXv37iEuLg6fffYZnn76ab15CQkJZi2MiIiIyKQxKtWrV8exY8csVQsRERGRHpMH0z7//PO666gQ2R21GkhNBdat0/6rVtu6IiIiegCTx6jcv38f//73v7Fjxw60bdsWrq6uevPnzp1rtuKIzCo5GRgzBjh//p+2gABg/nxApbJdXUREVCaTg8qJEyfQpk0bAMDvv/+uN0+SJPNURWRuycnAgAGAEPrtOTna9qQkhhUiIhkyOajs2rXLEnUQWY5are1JKR1SAG2bJAFjxwJ9+wK8FhARkaxU+EcJ//jjD2zduhV37twBAAhjXwJEcrBnj/7hntKEALKztcsREZGsmBxUrl27hq5du+Kxxx7DU089hdzcXADAsGHD8Prrr5u9QKJK+3sfNdtyRESlcaC+xZgcVMaNG4fq1asjKysLNWvW1LUPHjwYW7ZsMWtxRGbh52fe5YiISkpOBoKDgehoID5e+29wsLadKs3koLJt2zZ88MEHCAgI0Gtv2LAh/vzzT7MVRmQ24eHas3vKGuwtSUBgoHY5IiJTFA/UL314uXigPsNKpZkcVG7duqXXk1Lsr7/+0l1On0hWFArtKciAYVgpnp43jwNpicg0DxuoD2gH6vMwUKWYHFTCw8Px5Zdf6qYlSYJGo8GcOXMQHR1t1uKIzEal0p6C7O+v3x4Q4JinJvN4OlHlcaC+VZh8evKcOXPQtWtXHDp0CEVFRZgwYQJ+++03/PXXX9i7d68laiQyD5UKiIkBPD210ykpQGys4/Wk8MJ3RObBgfpWYXKPSvPmzfH777+jc+fO6Nu3L27dugWVSoWjR4+iQYMGlqiRyHxKhpKICMcMKTyeTmQeVX2gvkx6Xit0wbfo6Gi89dZbBvMWLVqExMREsxRGRGbGC9+Ro1GrtYddcnO1YSE83Lz7dvFA/Zwc4+8rSdLOt8eB+jLqeTW5R0WlUuHw4cMG7fPnz8fkyZPNUhQRWQCPp5MjscYpw1V1oL7Mel5NDioffvghevTogVOnTunaPv74Y0ydOhX//e9/zVocEZkRj6eTo7DmF21VG6gvwzOZTA4qL730Et544w3ExMTg3Llz+OCDD/Duu+8iJSUF4SZ2by1ZsgQtW7aEh4cHPDw80LFjR2zevNnUkoioPKr68XQiwDZftCoVcPLkP9MpKUBmpv2FFECWPa8mj1EBgAkTJuDatWt4/PHHoVarsXXrVjzxxBMm309AQABmz56Nhg0bQgiBVatWoW/fvjh69CiaNWtWkdKIqCxV+Xg6UTFTvmijosz3uFVloL4Me17LFVQ+/fRTg7Z69eqhZs2aiIiIwIEDB3DgwAEAwOjRo8v94L1799abnjlzJpYsWYJffvmFQYXI3IqPpw8YoA0lJcOKPR9PJypJhl+0dkWGPa/lCiqffPKJ0XaFQoG9e/fqrp8iSZJJQaUktVqNb7/9Frdu3ULHjh2NLlNYWIjCwkLddH5+foUei8hhFR9PHz1a27NSLCBAG1LssauaqCQZftHaFRn2vJYrqGRmZlqsgOPHj6Njx464e/cu3NzcsHHjRjRt2tTosrNmzcL06dMtVguRQ+CF76gqk+EXrV2RYc+rSYNp7927hwYNGiA9Pd1sBTRq1AhpaWnYv38/XnvtNSQkJOBkyUFJJUyePBl5eXm6W3Z2ttnqIHIoVeV4OlFpVfWUYWuS2ZlMJgWV6tWr4+7du2YtwNnZGaGhoWjbti1mzZqFsLAwzC/eyUpRKpW6M4SKb0RERHpk9kVrl2R0JpPJpycnJibigw8+wP379y1RDzQajd44FCIiIpPJ6IvWbsmk59Xk05MPHjyInTt3Ytu2bWjRogVcXV315iebcCGdyZMno0ePHqhfvz4KCgqwdu1apKamYuvWraaWRUREpE8mX7RUOSYHFS8vLzz99NNmefDLly9jyJAhyM3NhaenJ1q2bImtW7eiW7duZrl/IiIism8mB5UVK1aY7cGXL19utvsiIiKiqsfkMSpERERE1lKhS+gnJSXhm2++QVZWFoqKivTmHTlyxCyFEREREZnco/Lpp59i6NCh8PHxwdGjR9G+fXvUqlULZ8+eRY8ePSxRIxERETkok4PK4sWLsWzZMixYsADOzs6YMGECtm/fjtGjRyMvL88SNRIREZGDMjmoZGVloVOnTgAAFxcXFBQUAABeeOEFrFu3zrzVERERkUMzOaj4+vrir7/+AgDUr18fv/zyCwDt7wEJY7+rQERERFRBJgeVLl264D//+Q8AYOjQoRg3bhy6deuGwYMHo3///mYvkIiIiByXyWf9LFu2DBqNBoD2cvq1atXC//73P/Tp0wevvPKK2QskIiIix2VyUHFycoKT0z8dMc888wyeeeYZsxZFREREBFTwOip3797FsWPHcPnyZV3vSrE+ffqYpTAiIiIik4PKli1bMGTIEFy9etVgniRJUKvVZimMiIiIyOTBtKNGjcLAgQORm5sLjUajd2NIISIiInMyOahcunQJ48ePh4+PjyXqISIiItIxOagMGDAAqampFiiFiIiISJ/JY1QWLlyIgQMHYs+ePWjRogWqV6+uN3/06NFmK46IiIgcm8lBZd26ddi2bRtq1KiB1NRUSJKkmydJEoMKERERmY3JQeWtt97C9OnTMWnSJL3rqRARERGZm8lJo6ioCIMHD2ZIISIiIoszOW0kJCTg66+/tkQtRERERHpMPvSjVqsxZ84cbN26FS1btjQYTDt37lyzFUdEVGWo1cCePUBuLuDnB4SHAwqFrasikj2Tg8rx48fRunVrAMCJEyf05pUcWEtERH9LTgbGjAHOn/+nLSAAmD8fUKlsVxeRHTA5qOzatcsSdRARVU3JycCAAYAQ+u05Odr2pCSGFTlhz5fsVGpE7Pnz53G+5F8IRFS1qdVAaiqwbp32X/5sxoOp1dqelNIhBfinbexYbke5SE4GgoOB6GggPl77b3Cwtp1sxuSgotFo8O6778LT0xNBQUEICgqCl5cXZsyYYfBLykRUhfBD3HR79ugf7ilNCCA7W7sc2VZxz1fp16u454v7uc2YHFTeeustLFy4ELNnz8bRo0dx9OhRvP/++1iwYAHefvttS9RIRLbGD/GKyc0173JkGez5kjWTx6isWrUKX3zxBfr06aNra9myJerVq4cRI0Zg5syZZi2QiGzsYR/ikqT9EO/bl8fyS/PzM+9yZBmm9HxFRVmtLNIyuUflr7/+QuPGjQ3aGzdujL/++sssRRGRjPDwRcWFh2vP7inrjEhJAgIDtcuR7bDnS9ZMDiphYWFYuHChQfvChQsRFhZmlqKISEb4IV5xCoX2FGTAMKwUT8+bx54oW2PPl6yZfOhnzpw56NmzJ3bs2IGOHTsCAPbt24fs7GykpKSYvUAisjF+iFeOSqU9BXn0aO2YnmIBAdqQwlOTba+45ysnx/ghTknSzmfPl02Y3KMSGRmJ33//Hf3798eNGzdw48YNqFQqZGRkIJwvIlHVw8MXladSASdP/jOdkgJkZjKkyAV7vmTN5B4VAPD39+egWSJHUfwhPmCA9kO75F+c/BAvv5LbJyKC20tu2PMlW+UKKseOHUPz5s3h5OSEY8eOPXDZli1bmqUwIpIRfoiTI1CpgJgYwNNTO52SAsTGMlTaWLmCSqtWrXDx4kXUrVsXrVq1giRJEEaO40mSBDXPMyeqmvghTo6APV+yU66gkpmZiTp16uj+T0QOih/iRGRl5QoqQUFBRv9PREREZEkmD6a9du0aatWqBQDIzs7G559/jjt37qBPnz4864eIiIjMqtynJx8/fhzBwcGoW7cuGjdujLS0NLRr1w6ffPIJli1bhujoaHz33XcWLJWIiIgcTbmDyoQJE9CiRQv89NNPiIqKQq9evdCzZ0/k5eXh+vXreOWVVzB79mxL1kpEREQOptyHfg4ePIgff/wRLVu2RFhYGJYtW4YRI0bAyUmbdUaNGoUnnnjCYoUSERGR4yl3j8pff/0FX19fAICbmxtcXV3h7e2tm+/t7Y2CggLzV0hEREQOy6RL6EulLi1cepqIiIjInEw66+fFF1+EUqkEANy9exevvvoqXF1dAQCFhYXmr46IiIgcWrmDSkJCgt70888/b7DMkCFDKl8RERER0d/KHVRWrFhhyTqIiIiIDJg0RoWIiIjImhhUiIiISLYYVIiIiEi2GFSIiIhItsoVVNq0aYPr168DAN59913cvn3bokURERERAeUMKunp6bh16xYAYPr06bh586ZFiyIiIiICynl6cqtWrTB06FB07twZQgh89NFHcHNzM7rs1KlTzVogEREROa5yBZWVK1finXfewaZNmyBJEjZv3oxq1QxXlSSJQYWIiIjMplxBpVGjRli/fj0AwMnJCTt37kTdunUtWhgRERGRSb/1AwAajcYSdRAREREZMDmoAMCZM2cwb948pKenAwCaNm2KMWPGoEGDBmYtjoiIiBybyddR2bp1K5o2bYoDBw6gZcuWaNmyJfbv349mzZph+/btlqiRiIiIHJTJPSqTJk3CuHHjMHv2bIP2iRMnolu3bmYrjoiIiBybyT0q6enpGDZsmEH7v/71L5w8edIsRREREREBFQgqderUQVpamkF7WloazwQiIiIiszL50M/LL7+M4cOH4+zZs+jUqRMAYO/evfjggw8wfvx4sxdIREREjsvkoPL222/D3d0dH3/8MSZPngwA8Pf3x7Rp0zB69GizF0hERESOy+SgIkkSxo0bh3HjxqGgoAAA4O7ubvbCiIiIiEweo1KSu7t7pULKrFmz0K5dO7i7u6Nu3bro168fMjIyKlMSERERVSGVCiqVtXv3biQmJuKXX37B9u3bce/ePcTGxup+qZmIiIgcW4WuTGsuW7Zs0ZteuXIl6tati8OHDyMiIsJGVREREZFc2DSolJaXlwcAeOSRR4zOLywsRGFhoW46Pz/fKnURERGRbVTq0M/du3fNVQc0Gg3Gjh2LJ598Es2bNze6zKxZs+Dp6am7BQYGmu3xicjOqdVAaiqwbp32X7Xa1hURkRmYHFQ0Gg1mzJiBevXqwc3NDWfPngWgPW15+fLlFS4kMTERJ06cwPr168tcZvLkycjLy9PdsrOzK/x4RFSFJCcDwcFAdDQQH6/9NzhY205Eds3koPLee+9h5cqVmDNnDpydnXXtzZs3xxdffFGhIkaOHIlNmzZh165dCAgIKHM5pVIJDw8PvRsRVUGm9I4kJwMDBgDnz+u35+Ro2xlWiOyayUHlyy+/xLJly/Dcc89BoVDo2sPCwnDq1CmT7ksIgZEjR2Ljxo348ccfERISYmo5RFTVmNI7olYDY8YAQhjOK24bO5aHgYjsmMlBJScnB6GhoQbtGo0G9+7dM+m+EhMTsWbNGqxduxbu7u64ePEiLl68iDt37phaFhFVBab2juzZY7hsSUIA2dna5YjILpkcVJo2bYo9Rt70SUlJaN26tUn3tWTJEuTl5SEqKgp+fn6629dff21qWURk7yrSO5KbW777ftByHIRLJGsmn548depUJCQkICcnBxqNBsnJycjIyMCXX36JTZs2mXRfwtgHEhE5JlN6R6KitG1+fuW777KWS07WhqOSjxsQAMyfD6hU5btvIrIok3tU+vbtix9++AE7duyAq6srpk6divT0dPzwww/o1q2bJWokIkdQkd6R8HBtsJAk48tKEhAYqF2uNA7CJbILJgWV+/fv491330VISAi2b9+Oy5cv4/bt2/j5558RGxtrqRqJyBFUpHdEodD2fgCGYaV4et487XIlcRAukd0wKahUq1YNc+bMwf379y1VDxE5qor2jqhUQFIS4O+v3x4QoG03dgiHg3CJ7IbJh366du2K3bt3W6IWInJkFe0dAbRh5OTJf6ZTUoDMzLLHmZhjEC4RWYXJg2l79OiBSZMm4fjx42jbti1cXV315vfp08dsxZEZqdXavw5zc7Vd5+Hhxj/wiWypuHdk9GjtWJFiAQHakPKgAa4l9+eIiAfv35UdhEtEVmNyUBkxYgQAYO7cuQbzJEmCmsd05ccezmxgkKJiKhUQEwN4emqnU1KA2Fjz7g/Fh5lycoyPU5Ek7Xxjg3CJyKoq9Fs/Zd0YUmTIHs5s4O+0UGmm9I5U9P4repiJiKyqUr+eTDJnD2c22EOQoqqpIoNwicjqKhRUdu/ejd69eyM0NBShoaHo06eP0avVkgWYchVNuZ/ZYA9Biqo2UwfhEpHVmRxU1qxZg5iYGNSsWROjR4/G6NGj4eLigq5du2Lt2rWWqJGKmXqIRO5nNsg9SJFjsPRhJiKqFJMH086cORNz5szBuHHjdG2jR4/G3LlzMWPGDMTHx5u1QPpb8SGS0r0PxYdIjHVVy/3MBrkHKSIisjmTe1TOnj2L3r17G7T36dMHmZmZZimKSqnoIZLKXF7cGuQepIiIyOZMDiqBgYHYuXOnQfuOHTsQGBholqKolIoeIpH7mQ1yD1JERGRzJh/6ef311zF69GikpaWhU6dOAIC9e/di5cqVmF/8pUjmVZlDJJW5gJalFQepAQO0oaRkj5EcghQREdmcyUHltddeg6+vLz7++GN88803AIAmTZrg66+/Rt++fc1eIKHyh0iscQGtipJzkCIiIpszOagAQP/+/dG/f39z10JlMcdVNOV8ZoOcgxQREdmUyWNUDh48iP379xu079+/H4cOHTJLUVSK3MeamIOcgxQREdmMyUElMTER2dnZBu05OTlITEw0S1FkBK+iSUREDsjkQz8nT55EmzZtDNpbt26NkyWv8Ejmx0MkRETkYEzuUVEqlbh06ZJBe25uLqpVq9CQFzIFD5EQEZEDMTmoxMbGYvLkycjLy9O13bhxA1OmTEG3bt3MWhwRERE5NpO7QD766CNEREQgKCgIrVu3BgCkpaXBx8cHq1evNnuBRERE5LhMDir16tXDsWPH8NVXX+HXX3+Fi4sLhg4dimeffRbVq1e3RI1ERETkoCo0qMTV1RXDhw83dy1EREREeso9RuX333/HgQMH9Np27tyJ6OhotG/fHu+//77ZiyMiIiLHVu6gMnHiRGzatEk3nZmZid69e8PZ2RkdO3bErFmzMG/ePEvUSERERA6q3Id+Dh06hAkTJuimv/rqKzz22GPYunUrAKBly5ZYsGABxo4da/YiiYiIyDGVu0fl6tWrCAgI0E3v2rULvXv31k1HRUXh3LlzZi2OiIiIHFu5g8ojjzyC3NxcAIBGo8GhQ4fwxBNP6OYXFRVBGPvBPCIiIqIKKndQiYqKwowZM5CdnY158+ZBo9EgKipKN//kyZMIDg62QIlERETkqMo9RmXmzJno1q0bgoKCoFAo8Omnn8LV1VU3f/Xq1ejSpYtFiiQiIiLHVO6gEhwcjPT0dPz222+oU6cO/Ev9iu/06dP1xrCQg1KrgT17gNxcwM8PCA/n7xEREVGFmXTBt2rVqiEsLMzovLLayYEkJwNjxgDnz//TFhAAzJ+v/eVnIiIiE5n8o4RERiUnAwMG6IcUAMjJ0bYnJ9umLiIismsMKlR5arW2J8XYWV/FbWPHapcjIiIyAYMKVd6ePYY9KSUJAWRna5cjIiIyAYMKVd7f19cx23JERER/MzmoaDSaMtuzsrIqXRDZIT8/8y5HRET0t3IHlfz8fAwaNAiurq7w8fHB1KlToS4x5uDKlSsICQmxSJEkc+Hh2rN7JMn4fEkCAgO1yxEREZmg3EHl7bffxq+//orVq1dj5syZ+PLLL9G3b18UFRXpluEl9B2UQqE9BRkwDCvF0/Pm8XoqRERksnIHle+++w5Lly7FgAED8NJLL+HQoUO4cuUKevfujcLCQgCAVNZf1FT1qVRAUhJQ6kKACAjQtvM6KuWjVgOpqcC6ddp/eaYUETm4cgeVK1euICgoSDddu3Zt7NixAwUFBXjqqadw+/ZtixRIdkSlAk6e/Gc6JQXIzGRIKa/kZCA4GIiOBuLjtf8GB/MaNETk0ModVOrXr4/09HS9Nnd3d2zbtg137txB//79zV4c2aGSh3ciIni4p7x4wTwiIqPKHVRiY2OxYsUKg3Y3Nzds3boVNWrUMGthRA6DF8wjIipTuX/rZ/r06bhw4YLRee7u7ti+fTuOHDlitsKIHIYpF8yLirJaWUREclDuHhVvb280a9aszPl5eXn46quvzFIUkUPhBfOIiMpktivTXrt2DcuXLzfX3RE5Dl4wj4ioTLyEPpGt8YJ5RERlYlAhsjVeMI+IqEwMKkRywAvmEREZVe6zflQP+aC8ceNGZWshcmwqFRATA3h6aqdTUoDYWPakEJFDK3dQ8Sz+8HzA/CFDhlS6ICKHxgvmERHpKXdQMXaxNyIiIiJL4hgVIiIiki0GFSIiIpItBhUiIiKSLQYVIiIikq1yD6YlIiIiM1KrtT82mpur/YmM8HCe6WcEgwoRWRY/jIkMJScDY8bo/3J6QID2KtW8wKMeHvohIstJTgaCg4HoaCA+XvtvcLC2nchRJScDAwbohxQAyMnRtvP9oYdBhYgsgx/GRIbUam1PihCG84rbxo7VLkcAGFSIyBL4YUxk3J49huG9JCGA7GztcgTAxkHlp59+Qu/eveHv7w9JkvDdd9/ZshwiMhd+GBMZl5tr3uUcgE2Dyq1btxAWFoZFixbZsgwiMjd+GBMZ5+dn3uUcgE3P+unRowd69OhhyxKIyBL4YUxkXHi49uyenBzjh0YlSTs/PNz6tcmUXY1RKSwsRH5+vt6NiGSo+MNYkozPlyQgMJAfxuR4FArtKciA4fujeHrePJ7CX4JdBZVZs2bB09NTdwsMDLR1SURkDD+MicqmUgFJSYC/v357QIC2nddR0WNXQWXy5MnIy8vT3bKzs21dEhGVhR/GVZ9aDaSmAuvWaf/lWVzlp1IBJ0/+M52SAmRm8n1hhF1dmVapVEKpVNq6DCIqL5UKiIkBPD210ykpQGwse1KqAl5ZtfJKvg8iIvi+KINd9agQkR3ih3HVw4v5kRXZNKjcvHkTaWlpSEtLAwBkZmYiLS0NWVlZtiyLiIjKwov5kZXZNKgcOnQIrVu3RuvWrQEA48ePR+vWrTF16lRblkVERGXhxfzIymw6RiUqKgrCWConIiJ54sX8yMrsajAtERGVg1qt7dHIzdVeVC883Hxjg3gxP7IyDqYlIqpKkpOB4GAgOhqIj9f+GxxsvgGuvJgfWRmDChFRVWGNs3F4MT+yMgYVIqKqwJpn4/BifmRFDCpERFWBtc/G4ZVVyUo4mJaIqCqwxdk41ryYnyUHCJOsMagQEVUFVflsHF6u36Hx0A+RveMPwxFQdc/G4eX6HR6DCpE9s/SpqGQ/quLZOLxcP4FBhch+8S9NKq2qnY3Dy/UTGFSI7BP/0qSyVKWzcXi5fgKDCpF94l+a9CDWPBvHkqryAGEqNwYVInvEvzTJEVTVAcJkEgYVInvEvzTJEVTFAcJkMgYVInvEvzTJUVS1AcJkMgYVInvEvzTJkVSlAcJkMgYVsl+OfqEz/qVJjqSqDBC2BTv/rOQl9Mk+8ZLaWioVEBMDeHpqp1NSgNhYfogTkVYV+KxkjwqVTa4pnBc608e/NInImCryWcmgQsbJ9dLsvNAZEdHDVaHPSgYVMiTnFM4LnRHZP7n21lYlVeizkkGF9Mk9hfNCZ0T2Ta69tVVNFfqsZFAhfXJP4bzQGZH9knNvbVVThT4rGVRIn9xTOC90RmSf5N5bW9VUoc9KBhXSJ/cUzgudEdknuffWVjVV6LOSQYX02UMK54XOiOyP3Htrq6Iq8lnJoEL67CWF85LaRPZF7r219sLUM6aqwGclgwoZspcUzgudEdkPe+itlbuKnjFl55+VDCpkXBVI4UQkI/bSWytXDnzGFIMKlc3OUzgRyYy99NbKjYOfMcWgQkRE1sPeWtM5+BlTDCpERGRd7K01jYOfMcWgQkREJGcOfsYUgwoREZGcOfgZUwwqREREcubgZ0wxqBAREcmdA58xVc3WBRAREVE5qFRATAzg6amdTkkBYmOrbE9KMfaoEBER2QsHPGOKQYWIiIhki0GFiIiIZItBhYiIiGSLQYWIiIhki0GFiIiIZItBhYiIiGSLQYWIiIhki0GFiIiIZItBhYiIiGSLQYWIiIhki0GFiIiIZItBhYiIiGSLQYWIiIhki0GFiIiIZItBhYiIiGSLQYWIiIhki0GFiIiIZItBhYiIiGSLQYWIiIhki0GFiIiIZItBhYiIiGSLQYWIiIhki0GFiIiIZItBxRi1GpGZwDPHAafdPwFqtWzWUWv+WeanP3/Smzb3etZaR/b1VeS1reB6st4OMq/Pms/Jau/dCu57sn5OFVxPzuvIvT6rvjcsQcjAwoULRVBQkFAqlaJ9+/Zi//795VovLy9PABB5eXnmK2bDBqGuV08I4J9bQIAQGzbYfJ0NJzeIhrP8dcvXnAIRMDdAbDj5gMep4HrWWkf29VXkta3gerLeDjKvz5rPyWrv3Qrue7J+ThVcT87ryL0+q743TGDK97fNg8r69euFs7Oz+Pe//y1+++038fLLLwsvLy9x6dKlh65r9qCyYYMQkiQ0Jd+sgBCSpL0Ze9NaaZ0NJzcIaZokak6B3o4jTZOENE0qc+epyHrWWkf29VXkta3gerLeDjKvz5rPyWrv3Qrue7J+ThVcT87ryL0+q743TGTK97ckhBC26cvR6tChA9q1a4eFCxcCADQaDQIDAzFq1ChMmjTpgevm5+fD09MTeXl58PDwqFwhajUQHAycPw8BQEiS3mwhSYC/P8Thw4BCoVtHatMGuHABksEdmm8dtUaNtsva4sLNC6hZJJD1iXbZ+uOA284SJEjwc/PH4eGHoHBS/POUKrCetdaRfX0VeW2t+PrydbL+c7Lae7eC+56sn5PM9wk+p4c/J/eJArdrABIkBHgEIHNMpt56pjLl+9umQaWoqAg1a9ZEUlIS+vXrp2tPSEjAjRs38P333+stX1hYiMLCQt10fn4+AgMDzRNUUlOB6GgAgEaSkPFYo8rdHxERURXRrWcGcjz+iQu7EnYhKjiqwvdnSlCx6WDaq1evQq1Ww8fHR6/dx8cHFy9eNFh+1qxZ8PT01N0CAwPNV0xurvnui4iIqAq57azf55ZbYL3vzGpWeyQzmDx5MsaPH6+bLu5RMQs/P91/JSHQ6PcMo4tpNm4EOnfWTvz8M5z693/oXVd2nZ+z90L19cPXSR68EZ0Dn9RNV2Q9a60j+/oq8tpWcD1ZbweZ12fN52S1924F9z1ZP6cKrifndeRen7mf03Vnjd60n7ufwTKWYleHfkqzyBiVnBzt0KHSJAkICAAyM/XGm1hjHbVGjeD5wcjJz4GA4TplHTOsyHrWWkf29VXkta3gerLeDjKvz5rPyWrv3Qrue7J+ThVcT87ryL0+q743KsBuDv04Ozujbdu22Llzp65No9Fg586d6Nixo3WLUSiA+fO1/y81kFY3PW+e/peSldZROCkwP067jlRqyFvx9Ly4eQY7TUXWs9Y6sq+vIq9tBdeT9XaQeX3WfE5We+9WcN+T9XOq4HpyXkfu9Vn1vWFhNr/g2/jx4/H5559j1apVSE9Px2uvvYZbt25h6NCh1i9GpQKSkoB69fTbAwK07SqVzdZRNVEhaVAS6nnorxPgEYCkQUlQNTHyOBVcz1rryL6+iry2FVxP1ttB5vVZ8zlZ7b1bwX1P1s+pguvJeR2512fV94YF2fz0ZABYuHAhPvzwQ1y8eBGtWrXCp59+ig4dOjx0PbMe+ilJrQb27NEOsPXzA8LDDf9yttE6ao0ae7L2ILcgF37ufgivH16uZFuR9ay1juzrq8hrW8H1ZL0dZF6fNZ+T1d67Fdz3ZP2cKrienNeRe31WfW+Uk92cnlxZFgsqREREZDF2M0aFiIiI6EEYVIiIiEi2GFSIiIhIthhUiIiISLYYVIiIiEi2GFSIiIhIthhUiIiISLYYVIiIiEi2GFSIiIhItqrZuoDKKL6obn5+vo0rISIiovIq/t4uz8Xx7TqoFBQUAAACAwNtXAkRERGZqqCgAJ6eng9cxq5/60ej0eDChQtwd3eHVPonyv+Wn5+PwMBAZGdnO/zvAXFbaHE7aHE7/IPbQovbQYvb4R+W2hZCCBQUFMDf3x9OTg8ehWLXPSpOTk4ICAgo17IeHh4Ov8MV47bQ4nbQ4nb4B7eFFreDFrfDPyyxLR7Wk1KMg2mJiIhIthhUiIiISLaqfFBRKpV45513oFQqbV2KzXFbaHE7aHE7/IPbQovbQYvb4R9y2BZ2PZiWiIiIqrYq36NCRERE9otBhYiIiGSLQYWIiIhki0GFiIiIZKvKB5VFixYhODgYNWrUQIcOHXDgwAFbl2RV06ZNgyRJerfGjRvbuiyr+Omnn9C7d2/4+/tDkiR89913evOFEJg6dSr8/Pzg4uKCmJgYnD592jbFWtDDtsOLL75osI/ExcXZplgLmjVrFtq1awd3d3fUrVsX/fr1Q0ZGht4yd+/eRWJiImrVqgU3Nzc8/fTTuHTpko0qtozybIeoqCiDfeLVV1+1UcWWs2TJErRs2VJ3MbOOHTti8+bNuvmOsD8AD98Ott4fqnRQ+frrrzF+/Hi88847OHLkCMLCwtC9e3dcvnzZ1qVZVbNmzZCbm6u7/fzzz7YuySpu3bqFsLAwLFq0yOj8OXPm4NNPP8Vnn32G/fv3w9XVFd27d8fdu3etXKllPWw7AEBcXJzePrJu3TorVmgdu3fvRmJiIn755Rds374d9+7dQ2xsLG7duqVbZty4cfjhhx/w7bffYvfu3bhw4QJUKpUNqza/8mwHAHj55Zf19ok5c+bYqGLLCQgIwOzZs3H48GEcOnQIXbp0Qd++ffHbb78BcIz9AXj4dgBsvD+IKqx9+/YiMTFRN61Wq4W/v7+YNWuWDauyrnfeeUeEhYXZugybAyA2btyom9ZoNMLX11d8+OGHurYbN24IpVIp1q1bZ4MKraP0dhBCiISEBNG3b1+b1GNLly9fFgDE7t27hRDa17969eri22+/1S2Tnp4uAIh9+/bZqkyLK70dhBAiMjJSjBkzxnZF2ZC3t7f44osvHHZ/KFa8HYSw/f5QZXtUioqKcPjwYcTExOjanJycEBMTg3379tmwMus7ffo0/P398eijj+K5555DVlaWrUuyuczMTFy8eFFv//D09ESHDh0cbv8AgNTUVNStWxeNGjXCa6+9hmvXrtm6JIvLy8sDADzyyCMAgMOHD+PevXt6+0Tjxo1Rv379Kr1PlN4Oxb766ivUrl0bzZs3x+TJk3H79m1blGc1arUa69evx61bt9CxY0eH3R9Kb4dittwf7PpHCR/k6tWrUKvV8PHx0Wv38fHBqVOnbFSV9XXo0AErV65Eo0aNkJubi+nTpyM8PBwnTpyAu7u7rcuzmYsXLwKA0f2jeJ6jiIuLg0qlQkhICM6cOYMpU6agR48e2LdvHxQKha3LswiNRoOxY8fiySefRPPmzQFo9wlnZ2d4eXnpLVuV9wlj2wEA4uPjERQUBH9/fxw7dgwTJ05ERkYGkpOTbVitZRw/fhwdO3bE3bt34ebmho0bN6Jp06ZIS0tzqP2hrO0A2H5/qLJBhbR69Oih+3/Lli3RoUMHBAUF4ZtvvsGwYcNsWBnJxTPPPKP7f4sWLdCyZUs0aNAAqamp6Nq1qw0rs5zExEScOHHCYcZrlaWs7TB8+HDd/1u0aAE/Pz907doVZ86cQYMGDaxdpkU1atQIaWlpyMvLQ1JSEhISErB7925bl2V1ZW2Hpk2b2nx/qLKHfmrXrg2FQmEwQvvSpUvw9fW1UVW25+Xlhcceewx//PGHrUuxqeJ9gPuHoUcffRS1a9eusvvIyJEjsWnTJuzatQsBAQG6dl9fXxQVFeHGjRt6y1fVfaKs7WBMhw4dAKBK7hPOzs4IDQ1F27ZtMWvWLISFhWH+/PkOtz+UtR2Msfb+UGWDirOzM9q2bYudO3fq2jQaDXbu3Kl33M3R3Lx5E2fOnIGfn5+tS7GpkJAQ+Pr66u0f+fn52L9/v0PvHwBw/vx5XLt2rcrtI0IIjBw5Ehs3bsSPP/6IkJAQvflt27ZF9erV9faJjIwMZGVlVal94mHbwZi0tDQAqHL7hDEajQaFhYUOsz+UpXg7GGP1/cFmw3itYP369UKpVIqVK1eKkydPiuHDhwsvLy9x8eJFW5dmNa+//rpITU0VmZmZYu/evSImJkbUrl1bXL582dalWVxBQYE4evSoOHr0qAAg5s6dK44ePSr+/PNPIYQQs2fPFl5eXuL7778Xx44dE3379hUhISHizp07Nq7cvB60HQoKCsQbb7wh9u3bJzIzM8WOHTtEmzZtRMOGDcXdu3dtXbpZvfbaa8LT01OkpqaK3Nxc3e327du6ZV599VVRv3598eOPP4pDhw6Jjh07io4dO9qwavN72Hb4448/xLvvvisOHTokMjMzxffffy8effRRERERYePKzW/SpEli9+7dIjMzUxw7dkxMmjRJSJIktm3bJoRwjP1BiAdvBznsD1U6qAghxIIFC0T9+vWFs7OzaN++vfjll19sXZJVDR48WPj5+QlnZ2dRr149MXjwYPHHH3/Yuiyr2LVrlwBgcEtISBBCaE9Rfvvtt4WPj49QKpWia9euIiMjw7ZFW8CDtsPt27dFbGysqFOnjqhevboICgoSL7/8cpUM88a2AQCxYsUK3TJ37twRI0aMEN7e3qJmzZqif//+Ijc313ZFW8DDtkNWVpaIiIgQjzzyiFAqlSI0NFS8+eabIi8vz7aFW8C//vUvERQUJJydnUWdOnVE165ddSFFCMfYH4R48HaQw/4gCSGEdfpuiIiIiExTZceoEBERkf1jUCEiIiLZYlAhIiIi2WJQISIiItliUCEiIiLZYlAhIiIi2WJQISIiItliUCGihzp37hwkSdJdOluOgoODMW/ePFuXQURmxqBCRHjxxRchSZLuVqtWLcTFxeHYsWMAgMDAQOTm5qJ58+Y2rpSIHA2DChEBAOLi4pCbm4vc3Fzs3LkT1apVQ69evQAACoUCvr6+qFatmo2rNFRUVGRX90tEpmFQISIAgFKphK+vL3x9fdGqVStMmjQJ2dnZuHLlitFDP7t370b79u2hVCrh5+eHSZMm4f79+7r5UVFRGDVqFMaOHQtvb2/4+Pjg888/x61btzB06FC4u7sjNDQUmzdv1q2jVqsxbNgwhISEwMXFBY0aNTL4qfkXX3wR/fr1w8yZM+Hv749GjRoZfT5ffPEFvLy8dL9+e+LECfTo0QNubm7w8fHBCy+8gKtXr+rVO3LkSIwdOxa1a9dG9+7dIYTAtGnTUL9+fSiVSvj7+2P06NHm2NxEVE4MKkRk4ObNm1izZg1CQ0NRq1Ytg/k5OTl46qmn0K5dO/z6669YsmQJli9fjvfee09vuVWrVqF27do4cOAARo0ahddeew0DBw5Ep06dcOTIEcTGxuKFF17A7du3AWh/Wj4gIADffvstTp48ialTp2LKlCn45ptv9O53586dyMjIwPbt27Fp0yaD+ubMmYNJkyZh27Zt6Nq1K27cuIEuXbqgdevWOHToELZs2YJLly5h0KBBBvU6Oztj7969+Oyzz7BhwwZ88sknWLp0KU6fPo3vvvsOLVq0qOzmJSJTWO3nD4lIthISEoRCoRCurq7C1dVVABB+fn7i8OHDQgghMjMzBQBx9OhRIYQQU6ZMEY0aNRIajUZ3H4sWLRJubm5CrVYLIYSIjIwUnTt31s2/f/++cHV1FS+88IKuLTc3VwAQ+/btK7O2xMRE8fTTT+vV6uPjIwoLC/WWCwoKEp988omYMGGC8PPzEydOnNDNmzFjhoiNjdVbPjs7WwDQ/WJ2ZGSkaN26td4yH3/8sXjsscdEUVFR2RuPiCyKPSpEBACIjo5GWloa0tLScODAAXTv3h09evTAn3/+abBseno6OnbsCEmSdG1PPvkkbt68ifPnz+vaWrZsqfu/QqFArVq19HokfHx8AACXL1/WtS1atAht27ZFnTp14ObmhmXLliErK0vv8Vu0aAFnZ2eDuj7++GN8/vnn+Pnnn9GsWTNd+6+//opdu3bBzc1Nd2vcuDEA4MyZM7rl2rZtq3d/AwcOxJ07d/Doo4/i5ZdfxsaNG/UObxGR5TGoEBEAwNXVFaGhoQgNDUW7du3wxRdf4NatW/j8888rfJ/Vq1fXm5YkSa+tOOhoNBoAwPr16/HGG29g2LBh2LZtG9LS0jB06FCDga2urq5GHy88PBxqtdrgUNHNmzfRu3dvXRArvp0+fRoRERFl3m9gYCAyMjKwePFiuLi4YMSIEYiIiMC9e/dM3BJEVFHyG8JPRLIgSRKcnJxw584dg3lNmjTBhg0bIITQhY29e/fC3d0dAQEBFX7MvXv3olOnThgxYoSurWSPx8O0b98eI0eORFxcHKpVq4Y33ngDANCmTRts2LABwcHBJp+55OLigt69e6N3795ITExE48aNcfz4cbRp08ak+yGiimGPChEBAAoLC3Hx4kVcvHgR6enpGDVqlK4norQRI0YgOzsbo0aNwqlTp/D999/jnXfewfjx4+HkVPGPlYYNG+LQoUPYunUrfv/9d7z99ts4ePCgSffRqVMnpKSkYPr06boLwCUmJuKvv/7Cs88+i4MHD+LMmTPYunUrhg4dCrVaXeZ9rVy5EsuXL8eJEydw9uxZrFmzBi4uLggKCqrwcyQi07BHhYgAAFu2bIGfnx8AwN3dHY0bN8a3336LqKgonDt3Tm/ZevXqISUlBW+++SbCwsLwyCOPYNiwYfi///u/StXwyiuv4OjRoxg8eDAkScKzzz6LESNG6J3CXB6dO3fGf//7Xzz11FNQKBQYNWoU9u7di4kTJyI2NhaFhYUICgpCXFzcA4OVl5cXZs+ejfHjx0OtVqNFixb44YcfjJ4JRUSWIQkhhK2LICIiIjKGh36IiIhIthhUiIiISLYYVIiIiEi2GFSIiIhIthhUiIiISLYYVIiIiEi2GFSIiIhIthhUiIiISLYYVIiIiEi2GFSIiIhIthhUiIiISLYYVIiIiEi2/h+WRMYxMyRmagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l2_scores = GetAssignedBiomarkerScores(gcn_datasets)\n",
    "print(l2_scores)\n",
    "PlotBiomarkerImportanceComparison(l2_scores, 'L2 Score of Biomarkers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "08d11bf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def GetAllDatasetsColumn(df_datasets, column_name):\n",
    "    all_datasets = np.asarray(list(df_datasets[0][column_name].map(lambda x: x).to_numpy())).T\n",
    "    for i, df_dataset in enumerate(df_datasets):\n",
    "        if i > 0:\n",
    "            all_datasets = np.concatenate((all_datasets, np.asarray(list(df_dataset[column_name].map(lambda x: x).to_numpy())).T), axis=1)\n",
    "    return all_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2d60deda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.16090967, 0.1368943 , 0.1488853 , 0.06006382, 0.06166879,\n",
       "       0.01890076, 0.04828353, 0.06138883, 0.02015654, 0.11157092,\n",
       "       0.12941793, 0.15981285, 0.04517274, 0.02450461, 0.10713183,\n",
       "       0.08741034, 0.03528562, 0.06631208, 0.06619897, 0.06498836,\n",
       "       0.12883404, 0.08994766, 0.13066718, 0.0294139 , 0.15922724,\n",
       "       0.03112722, 0.01948522, 0.02596077, 0.10517212, 0.08887105,\n",
       "       0.10877221, 0.03690085, 0.01587145, 0.02143489, 0.10634245],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_values = GetAllDatasetsColumn(gcn_datasets, 'w')\n",
    "w_values_avr = np.average(w_values, axis=1)\n",
    "w_values_avr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "dc411c2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of Weights (Their Abs Taken)\n",
      "mean:0.08, stdev:0.05\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotBiomarkerImportanceComparison(w_values_avr, 'Mean of Weights (Their Abs Taken)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5db0f3c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bank of the Superior Temporal Sulcus',\n",
       "       'Lateral orbital frontal cortex', 'Precuneus cortex',\n",
       "       'Caudal Middle Frontal Gyrus', 'Caudal Anterior-cingulate Cortex',\n",
       "       'Posterior-cingulate cortex', 'Lateral occipital cortex',\n",
       "       'Pericalcarine cortex', 'Isthmus-cingulate Cortex',\n",
       "       'Supramarginal gyrus'], dtype='<U36')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = [\n",
    "        \"Bank of the Superior Temporal Sulcus\",#1\n",
    "        \"Caudal Anterior-cingulate Cortex\",#2\n",
    "        \"Caudal Middle Frontal Gyrus\",#3\n",
    "        \"Unmeasured Corpus Callosum\",#4\n",
    "        \"Cunesus Cortex\",#5\n",
    "        \"Entorhinal Cortex\",#6\n",
    "        \"Fusiform Gyrus\",#7\n",
    "        \"Inferior Parietal Cortex\",#8\n",
    "        \"Inferior Temporal Gyrus\",#9\n",
    "        \"Isthmus-cingulate Cortex\",#10\n",
    "        \"Lateral occipital cortex\",#11\n",
    "        \"Lateral orbital frontal cortex\",#12\n",
    "        \"Lingual gyrus\",#13\n",
    "        \"Medial orbital frontal cortex\",#14\n",
    "        \"Middle temporal gyrus\",#15\n",
    "        \"Parahippocampal gyrus\",#16\n",
    "        \"Paracentral lobule\",#17\n",
    "        \"Pars opercularis\",#18\n",
    "        \"Pars orbitalis\",#19\n",
    "        \"Pars triangularis\",#20\n",
    "        \"Pericalcarine cortex\",#21\n",
    "        \"Postcentral gyrus\",#22\n",
    "        \"Posterior-cingulate cortex\",#23\n",
    "        \"Precentral gyrus\",#24\n",
    "        \"Precuneus cortex\",#25\n",
    "        \"Rostral anterior cingulate cortex\",#26\n",
    "        \"Rostral middle frontal gyrus\",#£7\n",
    "        \"Superior frontal gyrus\",#28\n",
    "        \"Superior parietal cortex\",#29\n",
    "        \"Superior temporal gyrus\",#30\n",
    "        \"Supramarginal gyrus\",#31\n",
    "        \"Frontal pole\",#32\n",
    "        \"Temporal pole\",#33\n",
    "        \"Transverse temporal cortex\",#34\n",
    "        \"Insula cortex\"#35\n",
    "]\n",
    "\n",
    "np.array(labels)[list(reversed((np.argsort(l2_scores))[-10:].tolist()))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "45180283",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import cohen_kappa_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b590ab4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'diffpool'\n",
    "cv = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c545dd10",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_kappa_score_for_cv(kappa_matrix, kappa_files, master_no):\n",
    "    kappa_scores = []\n",
    "\n",
    "    with open(f\"{model_name}/kappa/{kappa_files[master_no]}\",'rb') as f:\n",
    "        master_preds = pickle.load(f)['preds']    \n",
    "        \n",
    "    for k in range(len(kappa_files)):\n",
    "        with open(f\"{model_name}/kappa/{kappa_files[k]}\",'rb') as f:\n",
    "            k_preds = pickle.load(f)['preds']   \n",
    "        \n",
    "        print(master_preds)\n",
    "        print(k_preds)\n",
    "        print()\n",
    "        \n",
    "        kappa_matrix[master_no, k] = cohen_kappa_score(k_preds, master_preds)\n",
    "\n",
    "    print('*******************************')\n",
    "\n",
    "def group_by_test_view(kappa_files):\n",
    "    kappa_test_view_0_files = []\n",
    "    kappa_test_view_1_files = []\n",
    "    kappa_test_view_2_files = []\n",
    "    kappa_test_view_3_files = []\n",
    "    kappa_test_view_4_files = []\n",
    "    kappa_test_view_5_files = []\n",
    "    \n",
    "    for k_file in kappa_files:\n",
    "        if 'test_with_view_0' in k_file:\n",
    "            kappa_test_view_0_files.append(k_file)\n",
    "        if 'test_with_view_1' in k_file:\n",
    "            kappa_test_view_1_files.append(k_file)\n",
    "        if 'test_with_view_2' in k_file:\n",
    "            kappa_test_view_2_files.append(k_file)\n",
    "        if 'test_with_view_3' in k_file:\n",
    "            kappa_test_view_3_files.append(k_file)\n",
    "        if 'test_with_view_4' in k_file:\n",
    "            kappa_test_view_4_files.append(k_file)\n",
    "        if 'test_with_view_5' in k_file:\n",
    "            kappa_test_view_5_files.append(k_file)\n",
    "            \n",
    "    return [kappa_test_view_0_files, kappa_test_view_1_files, kappa_test_view_2_files, kappa_test_view_3_files, kappa_test_view_4_files, kappa_test_view_5_files]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4e5c9c02",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'diffpool/kappa'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [83], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m kappa_files \u001b[38;5;241m=\u001b[39m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlistdir\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mmodel_name\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/kappa\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m kappa_cv_0_files \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m      3\u001b[0m kappa_cv_1_files \u001b[38;5;241m=\u001b[39m []\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'diffpool/kappa'"
     ]
    }
   ],
   "source": [
    "kappa_files = os.listdir(f\"{model_name}/kappa\")\n",
    "kappa_cv_0_files = []\n",
    "kappa_cv_1_files = []\n",
    "kappa_cv_2_files = []\n",
    "kappa_cv_3_files = []\n",
    "kappa_cv_4_files = []\n",
    "\n",
    "for k_file in kappa_files:\n",
    "    if 'CV_0' in k_file:\n",
    "        kappa_cv_0_files.append(k_file)\n",
    "    if 'CV_1' in k_file:\n",
    "        kappa_cv_1_files.append(k_file)\n",
    "    if 'CV_2' in k_file:\n",
    "        kappa_cv_2_files.append(k_file)\n",
    "    if 'CV_3' in k_file:\n",
    "        kappa_cv_3_files.append(k_file)\n",
    "    if 'CV_4' in k_file:\n",
    "        kappa_cv_4_files.append(k_file)\n",
    "        \n",
    "kappa_cv_0_files = sorted(kappa_cv_0_files)\n",
    "kappa_cv_1_files = sorted(kappa_cv_1_files)\n",
    "kappa_cv_2_files = sorted(kappa_cv_2_files)\n",
    "kappa_cv_3_files = sorted(kappa_cv_3_files)\n",
    "kappa_cv_4_files = sorted(kappa_cv_4_files)\n",
    "\n",
    "group_files_cv_0 = group_by_test_view(kappa_cv_0_files)\n",
    "group_files_cv_1 = group_by_test_view(kappa_cv_1_files)\n",
    "group_files_cv_2 = group_by_test_view(kappa_cv_2_files)\n",
    "group_files_cv_3 = group_by_test_view(kappa_cv_3_files)\n",
    "group_files_cv_4 = group_by_test_view(kappa_cv_4_files)\n",
    "\n",
    "group_files = None\n",
    "\n",
    "if cv == 0:\n",
    "    group_files = group_files_cv_0\n",
    "if cv == 1:\n",
    "    group_files = group_files_cv_1\n",
    "if cv == 2:\n",
    "    group_files = group_files_cv_2\n",
    "if cv == 3:\n",
    "    group_files = group_files_cv_3\n",
    "if cv == 4:\n",
    "    group_files = group_files_cv_4\n",
    "\n",
    "group_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7084c8a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "kappa_matrix = np.zeros((len(group_files[0]), len(group_files[0])))\n",
    "                        \n",
    "for i, g_files in enumerate(group_files):\n",
    "    calculate_kappa_score_for_cv(kappa_matrix, g_files, i)\n",
    "    \n",
    "kappa_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffa0f545",
   "metadata": {},
   "outputs": [],
   "source": [
    "kappa_matrix_flatten = kappa_matrix.flatten()\n",
    "nan_and_1_indices = ~(np.isnan(kappa_matrix_flatten) | (kappa_matrix_flatten == 1))\n",
    "kappa_matrix_flatten[nan_and_1_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1a789ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.average(kappa_matrix_flatten[nan_and_1_indices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b921ab9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "581f37ed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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