ore-psych-ml-ibs-base0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c9d7319e",
   "metadata": {},
   "source": [
    "`ore-psych-ml-ibs-base0.ipynb` notebook (ver 2022-12-16) for the main ML analysis procedures accompanying the manuscript\n",
    "\n",
    "### A psychological symptom based machine learning model for clinical evaluation of irritable bowel syndrome\n",
    " \n",
    "by Noman Haleem, ..., Arvid Lundervold submitted to Open Research Europe."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d303ad",
   "metadata": {},
   "source": [
    "Uses the `pq_df_pt_anon` pickle data file and runs under the following conda environment, denoted `oreibs0`:\n",
    "```\n",
    "conda create --name oreibs0 -c conda-forge scikit-learn python=3.8\n",
    "conda activate oreibs0\n",
    "conda install ipython\n",
    "conda install jupyter\n",
    "conda install pandas\n",
    "conda install matplotlib\n",
    "conda install statsmodels\n",
    "conda install -c conda-forge  pingouin\n",
    "conda install -c plotly plotly=5.10.0\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9ac5a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.graph_objects as go\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.express as px\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels\n",
    "import pingouin as pg\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import itertools\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import *\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.feature_selection import SequentialFeatureSelector\n",
    "from sklearn.model_selection import PredefinedSplit\n",
    "#from pandas_profiling import ProfileReport\n",
    "from sklearn.inspection import permutation_importance\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import accuracy_score, balanced_accuracy_score\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fddfd16",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_questionnaire_combinations(questionnaires):\n",
    "    \n",
    "    '''This function takes a dictionary as input where keys are individual\n",
    "    questionnaires and values and are variables within that questionnaire.\n",
    "    \n",
    "    It generates and return a dictionary of all possible combinations of \n",
    "    input questionnaires. The keys in the new dictionary are questionnaire\n",
    "    names separated by _ and values are combinations of all variables in those\n",
    "    questionnaires.\n",
    "    '''\n",
    "\n",
    "    quest_names = questionnaires.keys()\n",
    "\n",
    "    questionnaire_combinations = dict({})\n",
    "\n",
    "    for l in range(0,len(questionnaires)+1):\n",
    "\n",
    "        for subset in itertools.combinations(quest_names,l):\n",
    "            if len(subset)>0:\n",
    "\n",
    "                temp_features = [questionnaires[x] for x in subset]\n",
    "                temp_features1 = []\n",
    "                \n",
    "                for x in temp_features:\n",
    "                    for y in x:\n",
    "                        temp_features1.append(y)\n",
    "\n",
    "                questionnaire_combinations['_'.join(x for x in list(subset))] = temp_features1\n",
    "\n",
    "    return questionnaire_combinations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9f818198",
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_cross_validator(train_set, folds):\n",
    "\n",
    "    ''' CCV returns an iterable tuple of train test indices folds from given dataset based on participant group stratification\n",
    "        train_set is input dataset\n",
    "        folds represent the number of required folds\n",
    "    '''\n",
    "\n",
    "    iterable_indices = []\n",
    "\n",
    "    SSS = StratifiedShuffleSplit(n_splits=folds, test_size=0.25,random_state=42)\n",
    "\n",
    "    for train_folds,test_folds in SSS.split(train_set, train_set['stratification']):\n",
    "\n",
    "        iterable_indices.append((train_folds,test_folds))\n",
    "\n",
    "    return iterable_indices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "506ab795",
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_combination_finder(questionnaire_combinations,train_set, CCV, model, model_name):\n",
    "\n",
    "    ''' Feature combination finder takes the following inputs:\n",
    "        questionnaire_combinations = A dictionary containing various combinations of questionnaires as keys and values\n",
    "        train_set = training dataset\n",
    "        CCV = A list of iterable train, test indices produced from participant group stratification rule \n",
    "        model = A classifier object that will be used for both feature selection and training process\n",
    "        model_name = name of the classifier object to be recorded in results\n",
    "        The function uses SequentialFeatureSelection technique to select n feature combinations from input questionnaire_combinations, which provide\n",
    "        best performance on CCV train / test folds. \n",
    "    '''\n",
    "      \n",
    "    results = pd.DataFrame(columns=['Model','Questionnaire(s)','Selected Features'])\n",
    "    counter = 0\n",
    "\n",
    "    for k in questionnaire_combinations.keys():\n",
    "\n",
    "        if 'Age' in train_set.columns and 'Gender_q' in train_set.columns:\n",
    "            features = questionnaire_combinations[k]+['Age','Gender_q']\n",
    "            print('Age and Gender_q are added to feature combination ',k)\n",
    "        \n",
    "        else:\n",
    "            features = questionnaire_combinations[k]\n",
    "\n",
    "\n",
    "        for num_features in range(1,len(features)+1):\n",
    "            \n",
    "            print('Checking for  {} features in {}'.format(num_features,k))\n",
    "            \n",
    "            if num_features < len(features):\n",
    "\n",
    "                X_train = train_set[features].to_numpy()\n",
    "                y_train = train_set['label'].to_numpy()\n",
    "\n",
    "                # FS = Feature selection pipeline\n",
    "\n",
    "                FS = Pipeline([\n",
    "                    ('scaler', StandardScaler()),\n",
    "                    ('SFS', SequentialFeatureSelector(estimator=model,\n",
    "                                                            direction = 'backward',\n",
    "                                                            n_jobs=-1,\n",
    "                                                            cv=CCV,\n",
    "                                                            scoring = 'balanced_accuracy', \n",
    "                                                            n_features_to_select=num_features)\n",
    "                    )\n",
    "                ])                                                           \n",
    "\n",
    "                FS.fit(X_train,y_train)\n",
    "\n",
    "                selected_features = [a for a,b in zip(features,FS['SFS'].get_support()) if b]\n",
    "\n",
    "            else:\n",
    "\n",
    "                selected_features = features\n",
    "\n",
    "            results.loc[counter] = [model_name,k,selected_features]\n",
    "            counter += 1\n",
    "\n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "684f23e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rank_features(trained_model, X_train,y_train, features, method='permutation'):\n",
    "\n",
    "    ''' A simple supplementary function which takes trained model, training data and features used in training process as inputs \n",
    "        to rank features using either permutation testing or feature importance method. The latter is allowed only if the\n",
    "        underlying model support the method.\n",
    "    '''\n",
    "\n",
    "    if method == 'feature_importance':\n",
    "        \n",
    "        importance_df = pd.DataFrame({'importance':trained_model.feature_importances_,\n",
    "                                     'features':features,\n",
    "                                     'std':np.std(\n",
    "                                         [a.feature_importances_ for a in trained_model.estimators_],axis=0)})\n",
    "    \n",
    "        title = 'Feature importances'\n",
    "        \n",
    "    else:\n",
    "\n",
    "        result = permutation_importance(trained_model,X_train,y_train,n_repeats=10,\n",
    "                                   random_state=42)\n",
    "\n",
    "        importance_df = pd.DataFrame({'importance':result.importances_mean,\n",
    "                                     'features':features,\n",
    "                                     'std':result.importances_std})\n",
    "\n",
    "        title = 'Permutation importances'\n",
    "\n",
    "    importance_df = importance_df.sort_values('importance',ascending=False)\n",
    "\n",
    "    x = importance_df['features'][:30]\n",
    "    y = importance_df['importance'][:30]\n",
    "\n",
    "    fig = go.Figure()\n",
    "\n",
    "    fig.add_trace(go.Bar(x=x,\n",
    "                    y=y,\n",
    "                #  error_y=error_y,\n",
    "                ))\n",
    "\n",
    "    fig.update_layout(title = title,\n",
    "                        xaxis_tickangle=-45)\n",
    "\n",
    "    return fig, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cf4e0fee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def annotated_confusion_matrix(data, labels=['HC','IBS']):\n",
    "\n",
    "    ''' A  function to produce interactive confusion matrix using Plotly\n",
    "    '''\n",
    "    data = data.astype(np.float)\n",
    "    data = np.flipud(data)\n",
    "    data = data.astype(np.int)\n",
    "\n",
    "    x = [labels[0],labels[1]]\n",
    "    y = [labels[1],labels[0]]\n",
    "\n",
    "\n",
    "    fig = ff.create_annotated_heatmap(data, \n",
    "                                        x=x, \n",
    "                                        y=y, \n",
    "                                        annotation_text=data,\n",
    "                                        colorscale = 'blues',\n",
    "                                        )\n",
    "    \n",
    "    for i in range(len(fig.layout.annotations)):\n",
    "        fig.layout.annotations[i].font.size = 18\n",
    "\n",
    "\n",
    "    fig.update_layout(height = 500,\n",
    "                        width = 500\n",
    "                        )\n",
    "    \n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aba0ff6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adapted from https://github.com/DTrimarchi10/confusion_matrix/blob/master/cf_matrix.py\n",
    "def make_confusion_matrix(cf,\n",
    "                          group_names=None,\n",
    "                          categories='auto',\n",
    "                          count=True,\n",
    "                          percent=True,\n",
    "                          cbar=True,\n",
    "                          xyticks=True,\n",
    "                          xyplotlabels=True,\n",
    "                          sum_stats=True,\n",
    "                          figsize=None,\n",
    "                          cmap='Blues',\n",
    "                          title=None):\n",
    "    '''\n",
    "    This function will make a pretty plot of an sklearn Confusion Matrix cm using a Seaborn heatmap visualization.\n",
    "    Arguments\n",
    "    ---------\n",
    "    cf:            confusion matrix to be passed in\n",
    "    group_names:   List of strings that represent the labels row by row to be shown in each square.\n",
    "    categories:    List of strings containing the categories to be displayed on the x,y axis. Default is 'auto'\n",
    "    count:         If True, show the raw number in the confusion matrix. Default is True.\n",
    "    normalize:     If True, show the proportions for each category. Default is True.\n",
    "    cbar:          If True, show the color bar. The cbar values are based off the values in the confusion matrix.\n",
    "                   Default is True.\n",
    "    xyticks:       If True, show x and y ticks. Default is True.\n",
    "    xyplotlabels:  If True, show 'True Label' and 'Predicted Label' on the figure. Default is True.\n",
    "    sum_stats:     If True, display summary statistics below the figure. Default is True.\n",
    "    figsize:       Tuple representing the figure size. Default will be the matplotlib rcParams value.\n",
    "    cmap:          Colormap of the values displayed from matplotlib.pyplot.cm. Default is 'Blues'\n",
    "                   See http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "                   \n",
    "    title:         Title for the heatmap. Default is None.\n",
    "    '''\n",
    "\n",
    "\n",
    "    # CODE TO GENERATE TEXT INSIDE EACH SQUARE\n",
    "    blanks = ['' for i in range(cf.size)]\n",
    "\n",
    "    if group_names and len(group_names)==cf.size:\n",
    "        group_labels = [\"{}\\n\".format(value) for value in group_names]\n",
    "    else:\n",
    "        group_labels = blanks\n",
    "\n",
    "    if count:\n",
    "        group_counts = [\"{0:0.0f}\\n\".format(value) for value in cf.flatten()]\n",
    "    else:\n",
    "        group_counts = blanks\n",
    "\n",
    "    if percent:\n",
    "        group_percentages = [\"{0:.2%}\".format(value) for value in cf.flatten()/np.sum(cf)]\n",
    "    else:\n",
    "        group_percentages = blanks\n",
    "\n",
    "    box_labels = [f\"{v1}{v2}{v3}\".strip() for v1, v2, v3 in zip(group_labels,group_counts,group_percentages)]\n",
    "    box_labels = np.asarray(box_labels).reshape(cf.shape[0],cf.shape[1])\n",
    "\n",
    "\n",
    "    # CODE TO GENERATE SUMMARY STATISTICS & TEXT FOR SUMMARY STATS\n",
    "    if sum_stats:\n",
    "        #Accuracy is sum of diagonal divided by total observations\n",
    "        accuracy  = np.trace(cf) / float(np.sum(cf))\n",
    "\n",
    "        #if it is a binary confusion matrix, show some more stats\n",
    "        if len(cf)==2:\n",
    "            #Metrics for Binary Confusion Matrices\n",
    "            precision = cf[1,1] / sum(cf[:,1])\n",
    "            recall    = cf[1,1] / sum(cf[1,:])\n",
    "            f1_score  = 2*precision*recall / (precision + recall)\n",
    "            stats_text = \"\\n\\nAccuracy={:0.3f}\\nPrecision={:0.3f}\\nRecall={:0.3f}\\nF1 Score={:0.3f}\".format(\n",
    "                accuracy,precision,recall,f1_score)\n",
    "        else:\n",
    "            stats_text = \"\\n\\nAccuracy={:0.3f}\".format(accuracy)\n",
    "    else:\n",
    "        stats_text = \"\"\n",
    "\n",
    "\n",
    "    # SET FIGURE PARAMETERS ACCORDING TO OTHER ARGUMENTS\n",
    "    if figsize==None:\n",
    "        #Get default figure size if not set\n",
    "        figsize = plt.rcParams.get('figure.figsize')\n",
    "\n",
    "    if xyticks==False:\n",
    "        #Do not show categories if xyticks is False\n",
    "        categories=False\n",
    "\n",
    "\n",
    "    # MAKE THE HEATMAP VISUALIZATION\n",
    "    plt.figure(figsize=figsize)\n",
    "    sns.heatmap(cf,annot=box_labels,fmt=\"\",cmap=cmap,cbar=cbar,xticklabels=categories,yticklabels=categories)\n",
    "\n",
    "    if xyplotlabels:\n",
    "        plt.ylabel('True label')\n",
    "        plt.xlabel('Predicted label' + stats_text)\n",
    "    else:\n",
    "        plt.xlabel(stats_text)\n",
    "    \n",
    "    if title:\n",
    "        plt.title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "11354ad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading anonymized patient questionnaire dataframe from (pickle file)\n",
    "#df = pd.read_pickle('data/pq_df_pt_anon') \n",
    "#  or the .csv-file\n",
    "dff = pd.read_csv('data/pq_df_pt_anon.csv')\n",
    "dff.index = dff['pt_anon_ids']\n",
    "df = dff.drop(['pt_anon_ids'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d713838",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub_category</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>BIS_Q1_BL</th>\n",
       "      <th>BIS_Q2_BL</th>\n",
       "      <th>BIS_Q3_BL</th>\n",
       "      <th>BIS_Q4_BL</th>\n",
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       "      <th>HADS_q11_BL</th>\n",
       "      <th>HADS_q12_BL</th>\n",
       "      <th>HADS_q13_BL</th>\n",
       "      <th>HADS_q14_BL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pt_anon_ids</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BGA_046</th>\n",
       "      <td>IBS</td>\n",
       "      <td>Diarrhea</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGA_048</th>\n",
       "      <td>IBS</td>\n",
       "      <td>Diarrhea</td>\n",
       "      <td>F</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGA_051</th>\n",
       "      <td>IBS</td>\n",
       "      <td>Diarrhea</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>BGA_058</th>\n",
       "      <td>IBS</td>\n",
       "      <td>Diarrhea</td>\n",
       "      <td>F</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGA_068</th>\n",
       "      <td>IBS</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>M</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Category Sub_category Gender   Age  BIS_Q1_BL  BIS_Q2_BL  \\\n",
       "pt_anon_ids                                                            \n",
       "BGA_046          IBS     Diarrhea      M  38.0        0.0        0.0   \n",
       "BGA_048          IBS     Diarrhea      F  34.0        7.0        4.0   \n",
       "BGA_051          IBS     Diarrhea      M  38.0        6.0        1.0   \n",
       "BGA_058          IBS     Diarrhea      F  57.0        1.0        3.0   \n",
       "BGA_068          IBS        Mixed      M  31.0        2.0        1.0   \n",
       "\n",
       "             BIS_Q3_BL  BIS_Q4_BL  BIS_Q5_BL  BIS_Q6_BL  ...  HADS_q5_BL  \\\n",
       "pt_anon_ids                                              ...               \n",
       "BGA_046            3.0        5.0        5.0        3.0  ...         0.0   \n",
       "BGA_048            0.0        5.0        5.0        4.0  ...         NaN   \n",
       "BGA_051            1.0        2.0        0.0        2.0  ...         0.0   \n",
       "BGA_058            4.0        4.0        4.0        4.0  ...         0.0   \n",
       "BGA_068            0.0        7.0        5.0        3.0  ...         0.0   \n",
       "\n",
       "             HADS_q6_BL  HADS_q7_BL  HADS_q8_BL  HADS_q9_BL  HADS_q10_BL  \\\n",
       "pt_anon_ids                                                                \n",
       "BGA_046             0.0         0.0         1.0         0.0          0.0   \n",
       "BGA_048             NaN         NaN         NaN         NaN          NaN   \n",
       "BGA_051             0.0         0.0         1.0         1.0          0.0   \n",
       "BGA_058             1.0         1.0         2.0         1.0          1.0   \n",
       "BGA_068             1.0         0.0         0.0         1.0          1.0   \n",
       "\n",
       "             HADS_q11_BL  HADS_q12_BL  HADS_q13_BL  HADS_q14_BL  \n",
       "pt_anon_ids                                                      \n",
       "BGA_046              1.0          0.0          0.0          3.0  \n",
       "BGA_048              NaN          NaN          NaN          NaN  \n",
       "BGA_051              0.0          0.0          0.0          2.0  \n",
       "BGA_058              1.0          1.0          1.0          0.0  \n",
       "BGA_068              1.0          1.0          0.0          0.0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "45a2b5ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Sub_category</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>BIS_Q1_BL</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HC</th>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBS</th>\n",
       "      <td>60</td>\n",
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       "      <td>60</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "      <td>...</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Sub_category  Gender  Age  BIS_Q1_BL  BIS_Q2_BL  BIS_Q3_BL  \\\n",
       "Category                                                               \n",
       "HC                  41      41   41         39         39         39   \n",
       "IBS                 60      60   60         57         57         57   \n",
       "\n",
       "          BIS_Q4_BL  BIS_Q5_BL  BIS_Q6_BL  FSS_Q1_BL  ...  HADS_q5_BL  \\\n",
       "Category                                              ...               \n",
       "HC               39         39         39         39  ...          36   \n",
       "IBS              57         57         57         58  ...          52   \n",
       "\n",
       "          HADS_q6_BL  HADS_q7_BL  HADS_q8_BL  HADS_q9_BL  HADS_q10_BL  \\\n",
       "Category                                                                \n",
       "HC                36          36          36          36           36   \n",
       "IBS               52          52          52          52           52   \n",
       "\n",
       "          HADS_q11_BL  HADS_q12_BL  HADS_q13_BL  HADS_q14_BL  \n",
       "Category                                                      \n",
       "HC                 36           36           36           36  \n",
       "IBS                52           52           52           52  \n",
       "\n",
       "[2 rows x 36 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Category').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4fdc5b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning quantitative representation for gender i.e. 0 for males and 1 for females\n",
    "df['Gender_q'] = df['Gender'].apply(lambda x: 0 if x=='M' else 1)\n",
    "\n",
    "# Assiging label based participant category i.e. 0 for healthy controls and 1 for IBS patients\n",
    "df['label'] = df['Category'].apply(lambda x: 0 if x=='HC' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1bd2b2a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>pt_anon_ids</th>\n",
       "      <th>BGA_046</th>\n",
       "      <th>BGA_048</th>\n",
       "      <th>BGA_051</th>\n",
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       "      <th>Gender</th>\n",
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       "      <td>F</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>38.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>...</td>\n",
       "      <td>39.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>47.0</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIS_Q1_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIS_Q2_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIS_Q3_BL</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIS_Q4_BL</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIS_Q5_BL</th>\n",
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       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIS_Q6_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q1_BL</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q10_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q11_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q12_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q13_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q2_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q3_BL</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q4_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q5_BL</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q6_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q7_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q8_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q9_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q1_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q2_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q3_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q4_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q5_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q6_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q7_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q8_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q9_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q10_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q11_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q12_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q13_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q14_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_q</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "pt_anon_ids    BGA_046   BGA_048   BGA_051   BGA_058 BGA_068   BGA_071  \\\n",
       "Category           IBS       IBS       IBS       IBS     IBS       IBS   \n",
       "Sub_category  Diarrhea  Diarrhea  Diarrhea  Diarrhea   Mixed  Diarrhea   \n",
       "Gender               M         F         M         F       M         M   \n",
       "Age               38.0      34.0      38.0      57.0    31.0      35.0   \n",
       "BIS_Q1_BL          0.0       7.0       6.0       1.0     2.0       2.0   \n",
       "BIS_Q2_BL          0.0       4.0       1.0       3.0     1.0       5.0   \n",
       "BIS_Q3_BL          3.0       0.0       1.0       4.0     0.0       3.0   \n",
       "BIS_Q4_BL          5.0       5.0       2.0       4.0     7.0       3.0   \n",
       "BIS_Q5_BL          5.0       5.0       0.0       4.0     5.0       3.0   \n",
       "BIS_Q6_BL          3.0       4.0       2.0       4.0     3.0       3.0   \n",
       "FSS_Q1_BL          5.0       4.0       4.0       4.0     4.0       4.0   \n",
       "FSS_Q10_BL         3.0       4.0       3.0       5.0     5.0       4.0   \n",
       "FSS_Q11_BL         3.0       3.0       3.0       4.0     4.0       4.0   \n",
       "FSS_Q12_BL         3.0       5.0       5.0       5.0     5.0       4.0   \n",
       "FSS_Q13_BL         1.0       2.0       2.0       3.0     3.0       1.0   \n",
       "FSS_Q2_BL          3.0       4.0       4.0       4.0     4.0       4.0   \n",
       "FSS_Q3_BL          4.0       3.0       4.0       4.0     5.0       4.0   \n",
       "FSS_Q4_BL          3.0       3.0       5.0       4.0     4.0       3.0   \n",
       "FSS_Q5_BL          4.0       3.0       4.0       5.0     4.0       4.0   \n",
       "FSS_Q6_BL          1.0       3.0       3.0       4.0     1.0       4.0   \n",
       "FSS_Q7_BL          3.0       3.0       3.0       4.0     3.0       4.0   \n",
       "FSS_Q8_BL          3.0       4.0       3.0       4.0     4.0       4.0   \n",
       "FSS_Q9_BL          3.0       2.0       3.0       4.0     4.0       4.0   \n",
       "HADS_q1_BL         0.0       NaN       1.0       2.0     1.0       2.0   \n",
       "HADS_q2_BL         0.0       NaN       0.0       1.0     1.0       1.0   \n",
       "HADS_q3_BL         0.0       NaN       0.0       1.0     0.0       1.0   \n",
       "HADS_q4_BL         0.0       NaN       0.0       1.0     0.0       1.0   \n",
       "HADS_q5_BL         0.0       NaN       0.0       0.0     0.0       0.0   \n",
       "HADS_q6_BL         0.0       NaN       0.0       1.0     1.0       1.0   \n",
       "HADS_q7_BL         0.0       NaN       0.0       1.0     0.0       1.0   \n",
       "HADS_q8_BL         1.0       NaN       1.0       2.0     0.0       2.0   \n",
       "HADS_q9_BL         0.0       NaN       1.0       1.0     1.0       1.0   \n",
       "HADS_q10_BL        0.0       NaN       0.0       1.0     1.0       0.0   \n",
       "HADS_q11_BL        1.0       NaN       0.0       1.0     1.0       2.0   \n",
       "HADS_q12_BL        0.0       NaN       0.0       1.0     1.0       1.0   \n",
       "HADS_q13_BL        0.0       NaN       0.0       1.0     0.0       1.0   \n",
       "HADS_q14_BL        3.0       NaN       2.0       0.0     0.0       0.0   \n",
       "Gender_q             0         1         0         1       0         0   \n",
       "label                1         1         1         1       1         1   \n",
       "\n",
       "pt_anon_ids    BGA_078   BGA_083   BGA_086 BGA_095  ... BGA_135 BGA_137  \\\n",
       "Category           IBS       IBS       IBS     IBS  ...     IBS      HC   \n",
       "Sub_category  Diarrhea  Diarrhea  Diarrhea   Mixed  ...   Mixed      HC   \n",
       "Gender               F         F         F       M  ...       F       M   \n",
       "Age               24.0      30.0      22.0    28.0  ...    39.0    29.0   \n",
       "BIS_Q1_BL          1.0       4.0       4.0     3.0  ...     NaN     0.0   \n",
       "BIS_Q2_BL          0.0       2.0       3.0     0.0  ...     NaN     0.0   \n",
       "BIS_Q3_BL          0.0       3.0       2.0     0.0  ...     NaN     0.0   \n",
       "BIS_Q4_BL          4.0       2.0       2.0     6.0  ...     NaN     3.0   \n",
       "BIS_Q5_BL          2.0       2.0       2.0     3.0  ...     NaN     2.0   \n",
       "BIS_Q6_BL          1.0       4.0       3.0     3.0  ...     NaN     1.0   \n",
       "FSS_Q1_BL          4.0       3.0       3.0     5.0  ...     NaN     4.0   \n",
       "FSS_Q10_BL         3.0       2.0       2.0     4.0  ...     NaN     3.0   \n",
       "FSS_Q11_BL         3.0       3.0       3.0     4.0  ...     NaN     3.0   \n",
       "FSS_Q12_BL         3.0       1.0       2.0     5.0  ...     1.0     3.0   \n",
       "FSS_Q13_BL         1.0       5.0       1.0     3.0  ...     1.0     1.0   \n",
       "FSS_Q2_BL          4.0       3.0       3.0     4.0  ...     NaN     4.0   \n",
       "FSS_Q3_BL          4.0       3.0       3.0     4.0  ...     NaN     4.0   \n",
       "FSS_Q4_BL          4.0       3.0       3.0     4.0  ...     NaN     4.0   \n",
       "FSS_Q5_BL          4.0       3.0       3.0     5.0  ...     NaN     4.0   \n",
       "FSS_Q6_BL          3.0       4.0       1.0     1.0  ...     NaN     1.0   \n",
       "FSS_Q7_BL          3.0       4.0       2.0     3.0  ...     NaN     3.0   \n",
       "FSS_Q8_BL          3.0       4.0       4.0     3.0  ...     NaN     4.0   \n",
       "FSS_Q9_BL          3.0       2.0       2.0     3.0  ...     NaN     3.0   \n",
       "HADS_q1_BL         2.0       2.0       1.0     2.0  ...     1.0     0.0   \n",
       "HADS_q2_BL         1.0       0.0       0.0     1.0  ...     1.0     1.0   \n",
       "HADS_q3_BL         1.0       0.0       0.0     2.0  ...     1.0     1.0   \n",
       "HADS_q4_BL         0.0       0.0       0.0     0.0  ...     0.0     0.0   \n",
       "HADS_q5_BL         2.0       0.0       0.0     2.0  ...     1.0     0.0   \n",
       "HADS_q6_BL         0.0       0.0       0.0     1.0  ...     0.0     0.0   \n",
       "HADS_q7_BL         1.0       1.0       2.0     2.0  ...     0.0     0.0   \n",
       "HADS_q8_BL         0.0       2.0       1.0     1.0  ...     1.0     1.0   \n",
       "HADS_q9_BL         1.0       1.0       2.0     2.0  ...     1.0     1.0   \n",
       "HADS_q10_BL        0.0       1.0       1.0     0.0  ...     1.0     1.0   \n",
       "HADS_q11_BL        1.0       1.0       2.0     1.0  ...     2.0     1.0   \n",
       "HADS_q12_BL        0.0       1.0       0.0     2.0  ...     1.0     0.0   \n",
       "HADS_q13_BL        1.0       1.0       1.0     2.0  ...     0.0     0.0   \n",
       "HADS_q14_BL        0.0       0.0       2.0     0.0  ...     0.0     0.0   \n",
       "Gender_q             1         1         1       0  ...       1       0   \n",
       "label                1         1         1       1  ...       1       0   \n",
       "\n",
       "pt_anon_ids  BGA_138 BGA_139 BGA_141 BGA_082 BGA_124 BGA_126 BGA_127 BGA_186  \n",
       "Category         IBS     IBS     IBS      HC      HC      HC      HC     NaN  \n",
       "Sub_category   Mixed   Mixed   Mixed      HC      HC      HC      HC     NaN  \n",
       "Gender             F       F       F       F       F       M       M     NaN  \n",
       "Age             41.0    47.0    29.0    46.0    23.0    47.0    31.0     NaN  \n",
       "BIS_Q1_BL        NaN     0.0     1.0     0.0     2.0     0.0     NaN     NaN  \n",
       "BIS_Q2_BL        NaN     1.0     1.0     0.0     2.0     2.0     NaN     NaN  \n",
       "BIS_Q3_BL        NaN     1.0     0.0     0.0     3.0     1.0     NaN     NaN  \n",
       "BIS_Q4_BL        NaN     7.0     6.0     1.0     5.0     1.0     NaN     NaN  \n",
       "BIS_Q5_BL        NaN     7.0     3.0     0.0     3.0     0.0     NaN     NaN  \n",
       "BIS_Q6_BL        NaN     7.0     5.0     0.0     4.0     0.0     NaN     NaN  \n",
       "FSS_Q1_BL        NaN     4.0     4.0     3.0     3.0     3.0     NaN     NaN  \n",
       "FSS_Q10_BL       NaN     3.0     3.0     3.0     3.0     3.0     NaN     NaN  \n",
       "FSS_Q11_BL       NaN     3.0     4.0     3.0     3.0     3.0     NaN     NaN  \n",
       "FSS_Q12_BL       1.0     4.0     4.0     2.0     3.0     1.0     NaN     NaN  \n",
       "FSS_Q13_BL       1.0     4.0     2.0     1.0     1.0     1.0     NaN     NaN  \n",
       "FSS_Q2_BL        NaN     4.0     3.0     3.0     3.0     3.0     NaN     NaN  \n",
       "FSS_Q3_BL        NaN     4.0     4.0     3.0     4.0     3.0     NaN     NaN  \n",
       "FSS_Q4_BL        NaN     4.0     3.0     3.0     4.0     3.0     NaN     NaN  \n",
       "FSS_Q5_BL        NaN     5.0     3.0     3.0     4.0     3.0     NaN     NaN  \n",
       "FSS_Q6_BL        NaN     3.0     1.0     1.0     3.0     3.0     NaN     NaN  \n",
       "FSS_Q7_BL        NaN     3.0     3.0     3.0     3.0     3.0     NaN     NaN  \n",
       "FSS_Q8_BL        NaN     4.0     3.0     3.0     3.0     3.0     NaN     NaN  \n",
       "FSS_Q9_BL        NaN     3.0     3.0     3.0     3.0     3.0     NaN     NaN  \n",
       "HADS_q1_BL       3.0     1.0     2.0     0.0     2.0     1.0     NaN     NaN  \n",
       "HADS_q2_BL       1.0     1.0     0.0     0.0     2.0     0.0     NaN     NaN  \n",
       "HADS_q3_BL       3.0     2.0     2.0     0.0     2.0     0.0     NaN     NaN  \n",
       "HADS_q4_BL       1.0     1.0     0.0     0.0     1.0     0.0     NaN     NaN  \n",
       "HADS_q5_BL       3.0     0.0     2.0     0.0     2.0     0.0     NaN     NaN  \n",
       "HADS_q6_BL       1.0     0.0     0.0     0.0     1.0     0.0     NaN     NaN  \n",
       "HADS_q7_BL       0.0     0.0     0.0     0.0     2.0     0.0     NaN     NaN  \n",
       "HADS_q8_BL       2.0     3.0     1.0     0.0     1.0     0.0     NaN     NaN  \n",
       "HADS_q9_BL       3.0     1.0     1.0     0.0     2.0     0.0     NaN     NaN  \n",
       "HADS_q10_BL      1.0     1.0     1.0     0.0     1.0     0.0     NaN     NaN  \n",
       "HADS_q11_BL      1.0     0.0     1.0     0.0     1.0     0.0     NaN     NaN  \n",
       "HADS_q12_BL      1.0     0.0     0.0     0.0     2.0     0.0     NaN     NaN  \n",
       "HADS_q13_BL      1.0     1.0     1.0     0.0     2.0     0.0     NaN     NaN  \n",
       "HADS_q14_BL      0.0     0.0     2.0     0.0     1.0     0.0     NaN     NaN  \n",
       "Gender_q           1       1       1       1       1       0       0       1  \n",
       "label              1       1       1       0       0       0       0       1  \n",
       "\n",
       "[39 rows x 103 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1f2e5191",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Category', 'Sub_category', 'Gender', 'Age', 'BIS_Q1_BL', 'BIS_Q2_BL',\n",
       "       'BIS_Q3_BL', 'BIS_Q4_BL', 'BIS_Q5_BL', 'BIS_Q6_BL', 'FSS_Q1_BL',\n",
       "       'FSS_Q10_BL', 'FSS_Q11_BL', 'FSS_Q12_BL', 'FSS_Q13_BL', 'FSS_Q2_BL',\n",
       "       'FSS_Q3_BL', 'FSS_Q4_BL', 'FSS_Q5_BL', 'FSS_Q6_BL', 'FSS_Q7_BL',\n",
       "       'FSS_Q8_BL', 'FSS_Q9_BL', 'HADS_q1_BL', 'HADS_q2_BL', 'HADS_q3_BL',\n",
       "       'HADS_q4_BL', 'HADS_q5_BL', 'HADS_q6_BL', 'HADS_q7_BL', 'HADS_q8_BL',\n",
       "       'HADS_q9_BL', 'HADS_q10_BL', 'HADS_q11_BL', 'HADS_q12_BL',\n",
       "       'HADS_q13_BL', 'HADS_q14_BL', 'Gender_q', 'label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9e70d29d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We ignore this step - requires installation of pandas-profiling\n",
    "\n",
    "#Report = ProfileReport(df, title= 'Patient questionnaire exploration', explorative=True)\n",
    "#Report.to_notebook_iframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "28ec1734",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len(df): 103; total number of items: 33\n"
     ]
    }
   ],
   "source": [
    "questionnaires = ({\n",
    "                    'Anx':['HADS_q{}_BL'.format(x) for x in range(1,14,2)],\n",
    "                    'Dep':['HADS_q{}_BL'.format(x) for x in range(2,15,2)],\n",
    "                    'Bis':[x for x in df.columns if 'BIS' in x],\n",
    "                    'Fss':[x for x in df.columns if 'FSS' in x],\n",
    "                })\n",
    "\n",
    "BFH_questionnairs = questionnaires['Bis'] + questionnaires['Fss'] + questionnaires['Anx'] + questionnaires['Dep']\n",
    "nitems = len(BFH_questionnairs)\n",
    "ndf = len(df)\n",
    "print(f'len(df): {ndf}; total number of items: {nitems}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "54cc502f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining variable type i.e. categorial or numerical\n",
    "\n",
    "for col in questionnaires['Bis']:\n",
    "    df[col] = df[col].astype('float64')\n",
    "    \n",
    "for col in questionnaires['Fss']+questionnaires['Anx']+questionnaires['Dep']+['Gender_q','label']:\n",
    "    df[col] = df[col].astype('category')   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22a62a04",
   "metadata": {},
   "source": [
    "**Find  fraction of 50% of length of shortest Questionnaire and total number of items:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "82ead6a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Questionnarie with least number of items: min=6, p=0.09\n"
     ]
    }
   ],
   "source": [
    "x = min(len(y) for y in questionnaires.values()) # Questionnaire with minimum items \n",
    "p = np.round((0.5*x) / np.sum([len(y) for y in questionnaires.values()]), 2) \n",
    "print(f'Questionnarie with least number of items: min={x}, p={p}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c35725c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df['Category'].iloc[BGA_048]: IBS\n",
      "df['Category'].iloc[BGA_047]: IBS\n",
      "df['Category'].iloc[BGA_062]: IBS\n",
      "df['Category'].iloc[BGA_112]: IBS\n",
      "df['Category'].iloc[BGA_050]: IBS\n",
      "df['Category'].iloc[BGA_053]: HC\n",
      "df['Category'].iloc[BGA_158]: IBS\n",
      "df['Category'].iloc[BGA_165]: HC\n",
      "df['Category'].iloc[BGA_173]: nan\n",
      "df['Category'].iloc[BGA_056]: IBS\n",
      "df['Category'].iloc[BGA_081]: HC\n",
      "df['Category'].iloc[BGA_092]: HC\n",
      "df['Category'].iloc[BGA_110]: HC\n",
      "df['Category'].iloc[BGA_114]: IBS\n",
      "df['Category'].iloc[BGA_116]: IBS\n",
      "df['Category'].iloc[BGA_135]: IBS\n",
      "df['Category'].iloc[BGA_138]: IBS\n",
      "df['Category'].iloc[BGA_127]: HC\n",
      "df['Category'].iloc[BGA_186]: nan\n",
      "\n",
      "19 participants were dropped (at missing percentage p=9.0%) - 84 subjects remain.\n"
     ]
    }
   ],
   "source": [
    "to_drop = []        # Dropping those participants who miss more than p % of their data\n",
    "\n",
    "for i in df.index:\n",
    "    \n",
    "    missing_percent = df.loc[i].isna().sum() / df.shape[1]\n",
    "\n",
    "    if (missing_percent > p) or (pd.isna(df['Category'].loc[i])):\n",
    "        print(f\"df['Category'].iloc[{i}]:\", df['Category'].loc[i])\n",
    "        to_drop.append(i)\n",
    "\n",
    "df_mostly_complete = df.drop(to_drop)\n",
    "\n",
    "print(f'\\n{len(to_drop)} participants were dropped (at missing percentage p={p*100}%) - {len(df_mostly_complete)} subjects remain.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cdbf91a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub_category</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>BIS_Q1_BL</th>\n",
       "      <th>BIS_Q2_BL</th>\n",
       "      <th>BIS_Q3_BL</th>\n",
       "      <th>BIS_Q4_BL</th>\n",
       "      <th>BIS_Q5_BL</th>\n",
       "      <th>BIS_Q6_BL</th>\n",
       "      <th>...</th>\n",
       "      <th>HADS_q7_BL</th>\n",
       "      <th>HADS_q8_BL</th>\n",
       "      <th>HADS_q9_BL</th>\n",
       "      <th>HADS_q10_BL</th>\n",
       "      <th>HADS_q11_BL</th>\n",
       "      <th>HADS_q12_BL</th>\n",
       "      <th>HADS_q13_BL</th>\n",
       "      <th>HADS_q14_BL</th>\n",
       "      <th>Gender_q</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pt_anon_ids</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BGA_046</th>\n",
       "      <td>IBS</td>\n",
       "      <td>Diarrhea</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGA_051</th>\n",
       "      <td>IBS</td>\n",
       "      <td>Diarrhea</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGA_058</th>\n",
       "      <td>IBS</td>\n",
       "      <td>Diarrhea</td>\n",
       "      <td>F</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGA_068</th>\n",
       "      <td>IBS</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>M</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGA_071</th>\n",
       "      <td>IBS</td>\n",
       "      <td>Diarrhea</td>\n",
       "      <td>M</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGA_139</th>\n",
       "      <td>IBS</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>F</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGA_141</th>\n",
       "      <td>IBS</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>F</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGA_082</th>\n",
       "      <td>HC</td>\n",
       "      <td>HC</td>\n",
       "      <td>F</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGA_124</th>\n",
       "      <td>HC</td>\n",
       "      <td>HC</td>\n",
       "      <td>F</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BGA_126</th>\n",
       "      <td>HC</td>\n",
       "      <td>HC</td>\n",
       "      <td>M</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Category Sub_category Gender   Age  BIS_Q1_BL  BIS_Q2_BL  \\\n",
       "pt_anon_ids                                                            \n",
       "BGA_046          IBS     Diarrhea      M  38.0        0.0        0.0   \n",
       "BGA_051          IBS     Diarrhea      M  38.0        6.0        1.0   \n",
       "BGA_058          IBS     Diarrhea      F  57.0        1.0        3.0   \n",
       "BGA_068          IBS        Mixed      M  31.0        2.0        1.0   \n",
       "BGA_071          IBS     Diarrhea      M  35.0        2.0        5.0   \n",
       "...              ...          ...    ...   ...        ...        ...   \n",
       "BGA_139          IBS        Mixed      F  47.0        0.0        1.0   \n",
       "BGA_141          IBS        Mixed      F  29.0        1.0        1.0   \n",
       "BGA_082           HC           HC      F  46.0        0.0        0.0   \n",
       "BGA_124           HC           HC      F  23.0        2.0        2.0   \n",
       "BGA_126           HC           HC      M  47.0        0.0        2.0   \n",
       "\n",
       "             BIS_Q3_BL  BIS_Q4_BL  BIS_Q5_BL  BIS_Q6_BL  ... HADS_q7_BL  \\\n",
       "pt_anon_ids                                              ...              \n",
       "BGA_046            3.0        5.0        5.0        3.0  ...        0.0   \n",
       "BGA_051            1.0        2.0        0.0        2.0  ...        0.0   \n",
       "BGA_058            4.0        4.0        4.0        4.0  ...        1.0   \n",
       "BGA_068            0.0        7.0        5.0        3.0  ...        0.0   \n",
       "BGA_071            3.0        3.0        3.0        3.0  ...        1.0   \n",
       "...                ...        ...        ...        ...  ...        ...   \n",
       "BGA_139            1.0        7.0        7.0        7.0  ...        0.0   \n",
       "BGA_141            0.0        6.0        3.0        5.0  ...        0.0   \n",
       "BGA_082            0.0        1.0        0.0        0.0  ...        0.0   \n",
       "BGA_124            3.0        5.0        3.0        4.0  ...        2.0   \n",
       "BGA_126            1.0        1.0        0.0        0.0  ...        0.0   \n",
       "\n",
       "            HADS_q8_BL HADS_q9_BL HADS_q10_BL HADS_q11_BL HADS_q12_BL  \\\n",
       "pt_anon_ids                                                             \n",
       "BGA_046            1.0        0.0         0.0         1.0         0.0   \n",
       "BGA_051            1.0        1.0         0.0         0.0         0.0   \n",
       "BGA_058            2.0        1.0         1.0         1.0         1.0   \n",
       "BGA_068            0.0        1.0         1.0         1.0         1.0   \n",
       "BGA_071            2.0        1.0         0.0         2.0         1.0   \n",
       "...                ...        ...         ...         ...         ...   \n",
       "BGA_139            3.0        1.0         1.0         0.0         0.0   \n",
       "BGA_141            1.0        1.0         1.0         1.0         0.0   \n",
       "BGA_082            0.0        0.0         0.0         0.0         0.0   \n",
       "BGA_124            1.0        2.0         1.0         1.0         2.0   \n",
       "BGA_126            0.0        0.0         0.0         0.0         0.0   \n",
       "\n",
       "            HADS_q13_BL HADS_q14_BL Gender_q label  \n",
       "pt_anon_ids                                         \n",
       "BGA_046             0.0         3.0        0     1  \n",
       "BGA_051             0.0         2.0        0     1  \n",
       "BGA_058             1.0         0.0        1     1  \n",
       "BGA_068             0.0         0.0        0     1  \n",
       "BGA_071             1.0         0.0        0     1  \n",
       "...                 ...         ...      ...   ...  \n",
       "BGA_139             1.0         0.0        1     1  \n",
       "BGA_141             1.0         2.0        1     1  \n",
       "BGA_082             0.0         0.0        1     0  \n",
       "BGA_124             2.0         1.0        1     0  \n",
       "BGA_126             0.0         0.0        0     0  \n",
       "\n",
       "[84 rows x 39 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mostly_complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e83c35fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 84 entries, BGA_046 to BGA_126\n",
      "Data columns (total 39 columns):\n",
      " #   Column        Non-Null Count  Dtype   \n",
      "---  ------        --------------  -----   \n",
      " 0   Category      84 non-null     object  \n",
      " 1   Sub_category  84 non-null     object  \n",
      " 2   Gender        84 non-null     object  \n",
      " 3   Age           84 non-null     float64 \n",
      " 4   BIS_Q1_BL     84 non-null     float64 \n",
      " 5   BIS_Q2_BL     84 non-null     float64 \n",
      " 6   BIS_Q3_BL     84 non-null     float64 \n",
      " 7   BIS_Q4_BL     84 non-null     float64 \n",
      " 8   BIS_Q5_BL     84 non-null     float64 \n",
      " 9   BIS_Q6_BL     84 non-null     float64 \n",
      " 10  FSS_Q1_BL     84 non-null     category\n",
      " 11  FSS_Q10_BL    84 non-null     category\n",
      " 12  FSS_Q11_BL    82 non-null     category\n",
      " 13  FSS_Q12_BL    84 non-null     category\n",
      " 14  FSS_Q13_BL    84 non-null     category\n",
      " 15  FSS_Q2_BL     84 non-null     category\n",
      " 16  FSS_Q3_BL     84 non-null     category\n",
      " 17  FSS_Q4_BL     84 non-null     category\n",
      " 18  FSS_Q5_BL     84 non-null     category\n",
      " 19  FSS_Q6_BL     84 non-null     category\n",
      " 20  FSS_Q7_BL     83 non-null     category\n",
      " 21  FSS_Q8_BL     84 non-null     category\n",
      " 22  FSS_Q9_BL     84 non-null     category\n",
      " 23  HADS_q1_BL    84 non-null     category\n",
      " 24  HADS_q2_BL    84 non-null     category\n",
      " 25  HADS_q3_BL    84 non-null     category\n",
      " 26  HADS_q4_BL    84 non-null     category\n",
      " 27  HADS_q5_BL    84 non-null     category\n",
      " 28  HADS_q6_BL    84 non-null     category\n",
      " 29  HADS_q7_BL    84 non-null     category\n",
      " 30  HADS_q8_BL    84 non-null     category\n",
      " 31  HADS_q9_BL    84 non-null     category\n",
      " 32  HADS_q10_BL   84 non-null     category\n",
      " 33  HADS_q11_BL   84 non-null     category\n",
      " 34  HADS_q12_BL   84 non-null     category\n",
      " 35  HADS_q13_BL   84 non-null     category\n",
      " 36  HADS_q14_BL   84 non-null     category\n",
      " 37  Gender_q      84 non-null     category\n",
      " 38  label         84 non-null     category\n",
      "dtypes: category(29), float64(7), object(3)\n",
      "memory usage: 15.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_mostly_complete.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "524252d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(84, 39)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mostly_complete.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "91e8d642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category        0\n",
       "Sub_category    0\n",
       "Gender          0\n",
       "Age             0\n",
       "BIS_Q1_BL       0\n",
       "BIS_Q2_BL       0\n",
       "BIS_Q3_BL       0\n",
       "BIS_Q4_BL       0\n",
       "BIS_Q5_BL       0\n",
       "BIS_Q6_BL       0\n",
       "FSS_Q1_BL       0\n",
       "FSS_Q10_BL      0\n",
       "FSS_Q11_BL      2\n",
       "FSS_Q12_BL      0\n",
       "FSS_Q13_BL      0\n",
       "FSS_Q2_BL       0\n",
       "FSS_Q3_BL       0\n",
       "FSS_Q4_BL       0\n",
       "FSS_Q5_BL       0\n",
       "FSS_Q6_BL       0\n",
       "FSS_Q7_BL       1\n",
       "FSS_Q8_BL       0\n",
       "FSS_Q9_BL       0\n",
       "HADS_q1_BL      0\n",
       "HADS_q2_BL      0\n",
       "HADS_q3_BL      0\n",
       "HADS_q4_BL      0\n",
       "HADS_q5_BL      0\n",
       "HADS_q6_BL      0\n",
       "HADS_q7_BL      0\n",
       "HADS_q8_BL      0\n",
       "HADS_q9_BL      0\n",
       "HADS_q10_BL     0\n",
       "HADS_q11_BL     0\n",
       "HADS_q12_BL     0\n",
       "HADS_q13_BL     0\n",
       "HADS_q14_BL     0\n",
       "Gender_q        0\n",
       "label           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking completeness\n",
    "df_mostly_complete.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d996667",
   "metadata": {},
   "source": [
    "**Data Imputation using most frequent method**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6298d277",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing data points in HC_df are 2\n",
      "Missing data points in IBS_df are 1\n"
     ]
    }
   ],
   "source": [
    "# Seperating HC and IBS dataframes to impute missing values with the most frequent values from each category\n",
    "\n",
    "HC_df = df_mostly_complete[df_mostly_complete['Category'] == 'HC'] \n",
    "IBS_df = df_mostly_complete[df_mostly_complete['Category'] == 'IBS']\n",
    "\n",
    "print('Missing data points in HC_df are', HC_df.isna().sum().sum())\n",
    "print('Missing data points in IBS_df are', IBS_df.isna().sum().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3a082504",
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(strategy='most_frequent')\n",
    "\n",
    "HC_df_imputed = HC_df.iloc[:,3:]\n",
    "HC_df_imputed = pd.DataFrame(imputer.fit_transform(HC_df_imputed),\n",
    "                                 index=HC_df_imputed.index, \n",
    "                                 columns=HC_df_imputed.columns)\n",
    "HC_df_imputed = pd.concat([HC_df.iloc[:,0:3], HC_df_imputed], axis=1)\n",
    "\n",
    "IBS_df_imputed = IBS_df.iloc[:,3:]\n",
    "IBS_df_imputed = pd.DataFrame(imputer.fit_transform(IBS_df_imputed),\n",
    "                                 index=IBS_df_imputed.index, \n",
    "                                 columns=IBS_df_imputed.columns)\n",
    "IBS_df_imputed = pd.concat([IBS_df.iloc[:,0:3], IBS_df_imputed], axis=1)\n",
    "\n",
    "complete_df = pd.concat([HC_df_imputed,IBS_df_imputed])\n",
    "\n",
    "df = complete_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d13eb21",
   "metadata": {},
   "source": [
    "**Stratifying participants based on disease sub category and gender to achieve suitable representation in training, validation and testing data (as opposed to stratification by age)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "efa539d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning labels for stratification based on IBS subcategory \n",
    "\n",
    "for i in df.index:\n",
    "    df.loc[i, 'stratification'] = '{}_{}'.format(df.loc[i,'Sub_category'], df.loc[i,'Gender'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d8f2a882",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Total sample size: 84 \n",
      " Samples for training: 50 \n",
      " Samples for validation 17 \n",
      " Samples for testing (i.e. unseen) 17\n"
     ]
    }
   ],
   "source": [
    "SSS_for_test_data = StratifiedShuffleSplit(n_splits=1, test_size=0.2,random_state=42) # Stratified sample split for test datasets\n",
    "SSS_for_valid_train_data = StratifiedShuffleSplit(n_splits=1, test_size=0.25,random_state=42) # Stratified sample split for train and validation datasets\n",
    "\n",
    "for train_valid_index, test_index in SSS_for_test_data.split(df, df['stratification']):\n",
    "    train_valid_set = df.iloc[train_valid_index]\n",
    "    test_set = df.iloc[test_index]                  # Unseen / kept out dataset\n",
    "    \n",
    "for train_index, valid_index in SSS_for_valid_train_data.split(train_valid_set, train_valid_set['stratification']):\n",
    "    train_set = train_valid_set.iloc[train_index]    # Training dataset\n",
    "    valid_set = train_valid_set.iloc[valid_index]    # To be used for validation\n",
    "\n",
    "print(' Total sample size: {} \\n Samples for training: {} \\n Samples for validation {} \\n Samples for testing (i.e. unseen) {}'\\\n",
    "    .format(df.shape[0],train_set.shape[0],valid_set.shape[0],test_set.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c63fc2e",
   "metadata": {},
   "source": [
    "### Save anonymized train, validation and test datasets to .csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8d7c8ac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 38)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = train_set.drop(columns= ['label', 'Gender_q'])\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9a0b3bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, 38)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_valid = valid_set.drop(columns= ['label', 'Gender_q'])\n",
    "df_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6f94a5b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, 38)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = test_set.drop(columns= ['label', 'Gender_q'])\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "58b8bee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.to_csv('data/training.csv')\n",
    "df_valid.to_csv('data/validation.csv')\n",
    "df_test.to_csv('data/testing.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b166cb2",
   "metadata": {},
   "source": [
    "### Key characteristics of the participants included in the study (Table 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "26df4ecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>pt_anon_ids</th>\n",
       "      <th>BGA_149</th>\n",
       "      <th>BGA_151</th>\n",
       "      <th>BGA_060</th>\n",
       "      <th>BGA_085</th>\n",
       "      <th>BGA_046</th>\n",
       "      <th>BGA_152</th>\n",
       "      <th>BGA_059</th>\n",
       "      <th>BGA_122</th>\n",
       "      <th>BGA_139</th>\n",
       "      <th>BGA_071</th>\n",
       "      <th>...</th>\n",
       "      <th>BGA_161</th>\n",
       "      <th>BGA_142</th>\n",
       "      <th>BGA_130</th>\n",
       "      <th>BGA_147</th>\n",
       "      <th>BGA_074</th>\n",
       "      <th>BGA_076</th>\n",
       "      <th>BGA_115</th>\n",
       "      <th>BGA_094</th>\n",
       "      <th>BGA_088</th>\n",
       "      <th>BGA_065</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <td>IBS</td>\n",
       "      <td>IBS</td>\n",
       "      <td>IBS</td>\n",
       "      <td>IBS</td>\n",
       "      <td>IBS</td>\n",
       "      <td>IBS</td>\n",
       "      <td>IBS</td>\n",
       "      <td>IBS</td>\n",
       "      <td>IBS</td>\n",
       "      <td>IBS</td>\n",
       "      <td>...</td>\n",
       "      <td>IBS</td>\n",
       "      <td>IBS</td>\n",
       "      <td>HC</td>\n",
       "      <td>IBS</td>\n",
       "      <td>IBS</td>\n",
       "      <td>HC</td>\n",
       "      <td>IBS</td>\n",
       "      <td>IBS</td>\n",
       "      <td>HC</td>\n",
       "      <td>HC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub_category</th>\n",
       "      <td>Mixed</td>\n",
       "      <td>Diarrhea</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>Diarrhea</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>Constipation</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>Diarrhea</td>\n",
       "      <td>...</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>Diarrhea</td>\n",
       "      <td>HC</td>\n",
       "      <td>Diarrhea</td>\n",
       "      <td>Constipation</td>\n",
       "      <td>HC</td>\n",
       "      <td>Diarrhea</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>HC</td>\n",
       "      <td>HC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>38.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIS_Q1_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIS_Q2_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIS_Q3_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIS_Q4_BL</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIS_Q5_BL</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIS_Q6_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q1_BL</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q10_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q11_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q12_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q13_BL</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q2_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q3_BL</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q4_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q5_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q6_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q7_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q8_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q9_BL</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q1_BL</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q2_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q3_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q4_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q5_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q6_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q7_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q8_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q9_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q10_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q11_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q12_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q13_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HADS_q14_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stratification</th>\n",
       "      <td>Mixed_F</td>\n",
       "      <td>Diarrhea_F</td>\n",
       "      <td>Mixed_F</td>\n",
       "      <td>Mixed_F</td>\n",
       "      <td>Diarrhea_M</td>\n",
       "      <td>Mixed_M</td>\n",
       "      <td>Constipation_F</td>\n",
       "      <td>Mixed_F</td>\n",
       "      <td>Mixed_F</td>\n",
       "      <td>Diarrhea_M</td>\n",
       "      <td>...</td>\n",
       "      <td>Mixed_F</td>\n",
       "      <td>Diarrhea_M</td>\n",
       "      <td>HC_M</td>\n",
       "      <td>Diarrhea_F</td>\n",
       "      <td>Constipation_F</td>\n",
       "      <td>HC_F</td>\n",
       "      <td>Diarrhea_F</td>\n",
       "      <td>Mixed_F</td>\n",
       "      <td>HC_M</td>\n",
       "      <td>HC_F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "pt_anon_ids     BGA_149     BGA_151  BGA_060  BGA_085     BGA_046  BGA_152  \\\n",
       "Category            IBS         IBS      IBS      IBS         IBS      IBS   \n",
       "Sub_category      Mixed    Diarrhea    Mixed    Mixed    Diarrhea    Mixed   \n",
       "Gender                F           F        F        F           M        M   \n",
       "Age                38.0        22.0     41.0     23.0        38.0     32.0   \n",
       "BIS_Q1_BL           1.0         1.0      3.0      3.0         0.0      1.0   \n",
       "BIS_Q2_BL           0.0         0.0      2.0      0.0         0.0      1.0   \n",
       "BIS_Q3_BL           0.0         0.0      0.0      4.0         3.0      1.0   \n",
       "BIS_Q4_BL           5.0         2.0      3.0      6.0         5.0      2.0   \n",
       "BIS_Q5_BL           4.0         1.0      3.0      3.0         5.0      1.0   \n",
       "BIS_Q6_BL           1.0         2.0      4.0      7.0         3.0      1.0   \n",
       "FSS_Q1_BL           4.0         3.0      4.0      4.0         5.0      4.0   \n",
       "FSS_Q10_BL          3.0         3.0      4.0      4.0         3.0      3.0   \n",
       "FSS_Q11_BL          3.0         3.0      5.0      3.0         3.0      4.0   \n",
       "FSS_Q12_BL          3.0         2.0      5.0      4.0         3.0      4.0   \n",
       "FSS_Q13_BL          4.0         1.0      2.0      2.0         1.0      2.0   \n",
       "FSS_Q2_BL           3.0         3.0      4.0      4.0         3.0      3.0   \n",
       "FSS_Q3_BL           4.0         2.0      4.0      5.0         4.0      4.0   \n",
       "FSS_Q4_BL           3.0         3.0      4.0      5.0         3.0      5.0   \n",
       "FSS_Q5_BL           3.0         3.0      4.0      4.0         4.0      4.0   \n",
       "FSS_Q6_BL           3.0         1.0      4.0      3.0         1.0      4.0   \n",
       "FSS_Q7_BL           3.0         3.0      4.0      3.0         3.0      4.0   \n",
       "FSS_Q8_BL           3.0         3.0      4.0      4.0         3.0      5.0   \n",
       "FSS_Q9_BL           4.0         3.0      3.0      5.0         3.0      3.0   \n",
       "HADS_q1_BL          2.0         1.0      2.0      3.0         0.0      2.0   \n",
       "HADS_q2_BL          0.0         1.0      0.0      1.0         0.0      2.0   \n",
       "HADS_q3_BL          0.0         0.0      1.0      2.0         0.0      2.0   \n",
       "HADS_q4_BL          0.0         0.0      0.0      0.0         0.0      1.0   \n",
       "HADS_q5_BL          1.0         1.0      0.0      1.0         0.0      2.0   \n",
       "HADS_q6_BL          0.0         0.0      0.0      1.0         0.0      1.0   \n",
       "HADS_q7_BL          1.0         0.0      2.0      2.0         0.0      2.0   \n",
       "HADS_q8_BL          0.0         0.0      2.0      2.0         1.0      2.0   \n",
       "HADS_q9_BL          0.0         0.0      1.0      2.0         0.0      2.0   \n",
       "HADS_q10_BL         0.0         0.0      1.0      0.0         0.0      1.0   \n",
       "HADS_q11_BL         1.0         2.0      1.0      1.0         1.0      2.0   \n",
       "HADS_q12_BL         0.0         0.0      0.0      1.0         0.0      1.0   \n",
       "HADS_q13_BL         1.0         0.0      0.0      2.0         0.0      1.0   \n",
       "HADS_q14_BL         1.0         0.0      1.0      0.0         3.0      1.0   \n",
       "stratification  Mixed_F  Diarrhea_F  Mixed_F  Mixed_F  Diarrhea_M  Mixed_M   \n",
       "\n",
       "pt_anon_ids            BGA_059  BGA_122  BGA_139     BGA_071  ...  BGA_161  \\\n",
       "Category                   IBS      IBS      IBS         IBS  ...      IBS   \n",
       "Sub_category      Constipation    Mixed    Mixed    Diarrhea  ...    Mixed   \n",
       "Gender                       F        F        F           M  ...        F   \n",
       "Age                       43.0     43.0     47.0        35.0  ...     21.0   \n",
       "BIS_Q1_BL                  2.0      1.0      0.0         2.0  ...      0.0   \n",
       "BIS_Q2_BL                  3.0      4.0      1.0         5.0  ...      0.0   \n",
       "BIS_Q3_BL                  2.0      4.0      1.0         3.0  ...      0.0   \n",
       "BIS_Q4_BL                  2.0      5.0      7.0         3.0  ...      3.0   \n",
       "BIS_Q5_BL                  2.0      5.0      7.0         3.0  ...      0.0   \n",
       "BIS_Q6_BL                  2.0      6.0      7.0         3.0  ...      0.0   \n",
       "FSS_Q1_BL                  4.0      4.0      4.0         4.0  ...      5.0   \n",
       "FSS_Q10_BL                 3.0      3.0      3.0         4.0  ...      4.0   \n",
       "FSS_Q11_BL                 4.0      4.0      3.0         4.0  ...      3.0   \n",
       "FSS_Q12_BL                 3.0      5.0      4.0         4.0  ...      5.0   \n",
       "FSS_Q13_BL                 1.0      2.0      4.0         1.0  ...      2.0   \n",
       "FSS_Q2_BL                  3.0      3.0      4.0         4.0  ...      4.0   \n",
       "FSS_Q3_BL                  3.0      4.0      4.0         4.0  ...      4.0   \n",
       "FSS_Q4_BL                  3.0      4.0      4.0         3.0  ...      5.0   \n",
       "FSS_Q5_BL                  3.0      4.0      5.0         4.0  ...      5.0   \n",
       "FSS_Q6_BL                  3.0      3.0      3.0         4.0  ...      3.0   \n",
       "FSS_Q7_BL                  3.0      3.0      3.0         4.0  ...      3.0   \n",
       "FSS_Q8_BL                  3.0      4.0      4.0         4.0  ...      4.0   \n",
       "FSS_Q9_BL                  3.0      3.0      3.0         4.0  ...      3.0   \n",
       "HADS_q1_BL                 2.0      2.0      1.0         2.0  ...      1.0   \n",
       "HADS_q2_BL                 1.0      2.0      1.0         1.0  ...      0.0   \n",
       "HADS_q3_BL                 2.0      0.0      2.0         1.0  ...      0.0   \n",
       "HADS_q4_BL                 1.0      1.0      1.0         1.0  ...      0.0   \n",
       "HADS_q5_BL                 2.0      1.0      0.0         0.0  ...      0.0   \n",
       "HADS_q6_BL                 1.0      1.0      0.0         1.0  ...      0.0   \n",
       "HADS_q7_BL                 2.0      2.0      0.0         1.0  ...      3.0   \n",
       "HADS_q8_BL                 1.0      2.0      3.0         2.0  ...      1.0   \n",
       "HADS_q9_BL                 2.0      1.0      1.0         1.0  ...      2.0   \n",
       "HADS_q10_BL                0.0      1.0      1.0         0.0  ...      0.0   \n",
       "HADS_q11_BL                2.0      2.0      0.0         2.0  ...      1.0   \n",
       "HADS_q12_BL                1.0      2.0      0.0         1.0  ...      0.0   \n",
       "HADS_q13_BL                1.0      1.0      1.0         1.0  ...      1.0   \n",
       "HADS_q14_BL                1.0      1.0      0.0         0.0  ...      0.0   \n",
       "stratification  Constipation_F  Mixed_F  Mixed_F  Diarrhea_M  ...  Mixed_F   \n",
       "\n",
       "pt_anon_ids        BGA_142 BGA_130     BGA_147         BGA_074 BGA_076  \\\n",
       "Category               IBS      HC         IBS             IBS      HC   \n",
       "Sub_category      Diarrhea      HC    Diarrhea    Constipation      HC   \n",
       "Gender                   M       M           F               F       F   \n",
       "Age                   46.0    38.0        61.0            48.0    24.0   \n",
       "BIS_Q1_BL              2.0     4.0         4.0             6.0     1.0   \n",
       "BIS_Q2_BL              3.0     6.0         3.0             1.0     0.0   \n",
       "BIS_Q3_BL              0.0     6.0         2.0             1.0     1.0   \n",
       "BIS_Q4_BL              5.0     3.0         3.0             1.0     2.0   \n",
       "BIS_Q5_BL              0.0     1.0         1.0             6.0     2.0   \n",
       "BIS_Q6_BL              4.0     4.0         3.0             6.0     1.0   \n",
       "FSS_Q1_BL              3.0     3.0         3.0             5.0     3.0   \n",
       "FSS_Q10_BL             4.0     4.0         4.0             5.0     3.0   \n",
       "FSS_Q11_BL             4.0     3.0         4.0             5.0     3.0   \n",
       "FSS_Q12_BL             1.0     1.0         5.0             5.0     1.0   \n",
       "FSS_Q13_BL             5.0     1.0         2.0             2.0     5.0   \n",
       "FSS_Q2_BL              3.0     3.0         3.0             5.0     3.0   \n",
       "FSS_Q3_BL              3.0     4.0         3.0             5.0     3.0   \n",
       "FSS_Q4_BL              3.0     4.0         4.0             4.0     3.0   \n",
       "FSS_Q5_BL              3.0     3.0         4.0             5.0     3.0   \n",
       "FSS_Q6_BL              4.0     1.0         4.0             4.0     3.0   \n",
       "FSS_Q7_BL              3.0     3.0         4.0             4.0     3.0   \n",
       "FSS_Q8_BL              3.0     3.0         3.0             4.0     3.0   \n",
       "FSS_Q9_BL              3.0     3.0         2.0             5.0     3.0   \n",
       "HADS_q1_BL             2.0     0.0         1.0             1.0     1.0   \n",
       "HADS_q2_BL             0.0     1.0         0.0             1.0     0.0   \n",
       "HADS_q3_BL             0.0     0.0         1.0             0.0     0.0   \n",
       "HADS_q4_BL             0.0     0.0         0.0             1.0     0.0   \n",
       "HADS_q5_BL             0.0     0.0         0.0             0.0     0.0   \n",
       "HADS_q6_BL             0.0     0.0         0.0             0.0     0.0   \n",
       "HADS_q7_BL             2.0     0.0         0.0             0.0     0.0   \n",
       "HADS_q8_BL             1.0     0.0         1.0             2.0     0.0   \n",
       "HADS_q9_BL             2.0     0.0         1.0             0.0     0.0   \n",
       "HADS_q10_BL            0.0     0.0         0.0             1.0     0.0   \n",
       "HADS_q11_BL            3.0     1.0         0.0             0.0     0.0   \n",
       "HADS_q12_BL            0.0     2.0         0.0             2.0     0.0   \n",
       "HADS_q13_BL            0.0     0.0         1.0             1.0     0.0   \n",
       "HADS_q14_BL            0.0     0.0         0.0             0.0     0.0   \n",
       "stratification  Diarrhea_M    HC_M  Diarrhea_F  Constipation_F    HC_F   \n",
       "\n",
       "pt_anon_ids        BGA_115  BGA_094 BGA_088 BGA_065  \n",
       "Category               IBS      IBS      HC      HC  \n",
       "Sub_category      Diarrhea    Mixed      HC      HC  \n",
       "Gender                   F        F       M       F  \n",
       "Age                   58.0     27.0    33.0    23.0  \n",
       "BIS_Q1_BL              2.0      6.0     2.0     7.0  \n",
       "BIS_Q2_BL              1.0      4.0     0.0     1.0  \n",
       "BIS_Q3_BL              2.0      1.0     0.0     3.0  \n",
       "BIS_Q4_BL              5.0      7.0     4.0     4.0  \n",
       "BIS_Q5_BL              3.0      6.0     1.0     1.0  \n",
       "BIS_Q6_BL              3.0      6.0     2.0     5.0  \n",
       "FSS_Q1_BL              5.0      5.0     3.0     3.0  \n",
       "FSS_Q10_BL             4.0      3.0     3.0     3.0  \n",
       "FSS_Q11_BL             3.0      4.0     3.0     3.0  \n",
       "FSS_Q12_BL             3.0      4.0     1.0     3.0  \n",
       "FSS_Q13_BL             3.0      2.0     5.0     1.0  \n",
       "FSS_Q2_BL              4.0      4.0     3.0     3.0  \n",
       "FSS_Q3_BL              4.0      4.0     3.0     4.0  \n",
       "FSS_Q4_BL              4.0      5.0     3.0     3.0  \n",
       "FSS_Q5_BL              4.0      4.0     3.0     3.0  \n",
       "FSS_Q6_BL              3.0      4.0     3.0     1.0  \n",
       "FSS_Q7_BL              3.0      4.0     3.0     2.0  \n",
       "FSS_Q8_BL              4.0      5.0     3.0     3.0  \n",
       "FSS_Q9_BL              3.0      3.0     3.0     3.0  \n",
       "HADS_q1_BL             2.0      2.0     0.0     1.0  \n",
       "HADS_q2_BL             2.0      3.0     0.0     1.0  \n",
       "HADS_q3_BL             1.0      2.0     0.0     0.0  \n",
       "HADS_q4_BL             0.0      1.0     0.0     0.0  \n",
       "HADS_q5_BL             1.0      2.0     1.0     1.0  \n",
       "HADS_q6_BL             0.0      2.0     0.0     1.0  \n",
       "HADS_q7_BL             1.0      1.0     1.0     1.0  \n",
       "HADS_q8_BL             3.0      3.0     0.0     1.0  \n",
       "HADS_q9_BL             2.0      1.0     0.0     0.0  \n",
       "HADS_q10_BL            0.0      3.0     1.0     1.0  \n",
       "HADS_q11_BL            1.0      0.0     2.0     2.0  \n",
       "HADS_q12_BL            1.0      2.0     0.0     1.0  \n",
       "HADS_q13_BL            1.0      2.0     0.0     0.0  \n",
       "HADS_q14_BL            1.0      0.0     0.0     1.0  \n",
       "stratification  Diarrhea_F  Mixed_F    HC_M    HC_F  \n",
       "\n",
       "[38 rows x 84 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stack the DataFrames on top of each other\n",
    "df_all = pd.concat([df_train, df_valid, df_test], axis=0)\n",
    "df_all.to_csv('data/train_valid_test.csv')\n",
    "df_all.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a6b4a7b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Sub_category</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>BIS_Q1_BL</th>\n",
       "      <th>BIS_Q2_BL</th>\n",
       "      <th>BIS_Q3_BL</th>\n",
       "      <th>BIS_Q4_BL</th>\n",
       "      <th>BIS_Q5_BL</th>\n",
       "      <th>BIS_Q6_BL</th>\n",
       "      <th>FSS_Q1_BL</th>\n",
       "      <th>...</th>\n",
       "      <th>HADS_q6_BL</th>\n",
       "      <th>HADS_q7_BL</th>\n",
       "      <th>HADS_q8_BL</th>\n",
       "      <th>HADS_q9_BL</th>\n",
       "      <th>HADS_q10_BL</th>\n",
       "      <th>HADS_q11_BL</th>\n",
       "      <th>HADS_q12_BL</th>\n",
       "      <th>HADS_q13_BL</th>\n",
       "      <th>HADS_q14_BL</th>\n",
       "      <th>stratification</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HC</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBS</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>...</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Sub_category  Gender  Age  BIS_Q1_BL  BIS_Q2_BL  BIS_Q3_BL  \\\n",
       "Category                                                               \n",
       "HC                  35      35   35         35         35         35   \n",
       "IBS                 49      49   49         49         49         49   \n",
       "\n",
       "          BIS_Q4_BL  BIS_Q5_BL  BIS_Q6_BL  FSS_Q1_BL  ...  HADS_q6_BL  \\\n",
       "Category                                              ...               \n",
       "HC               35         35         35         35  ...          35   \n",
       "IBS              49         49         49         49  ...          49   \n",
       "\n",
       "          HADS_q7_BL  HADS_q8_BL  HADS_q9_BL  HADS_q10_BL  HADS_q11_BL  \\\n",
       "Category                                                                 \n",
       "HC                35          35          35           35           35   \n",
       "IBS               49          49          49           49           49   \n",
       "\n",
       "          HADS_q12_BL  HADS_q13_BL  HADS_q14_BL  stratification  \n",
       "Category                                                         \n",
       "HC                 35           35           35              35  \n",
       "IBS                49           49           49              49  \n",
       "\n",
       "[2 rows x 37 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.groupby(['Category']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dbd1e7f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">HC</th>\n",
       "      <th>F</th>\n",
       "      <td>24.0</td>\n",
       "      <td>35.67</td>\n",
       "      <td>13.38</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>47.00</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>11.0</td>\n",
       "      <td>36.36</td>\n",
       "      <td>11.54</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>43.50</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IBS</th>\n",
       "      <th>F</th>\n",
       "      <td>38.0</td>\n",
       "      <td>36.45</td>\n",
       "      <td>11.96</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>42.75</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>11.0</td>\n",
       "      <td>36.45</td>\n",
       "      <td>6.53</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40.50</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Age                                             \n",
       "                count   mean    std   min   25%   50%    75%   max\n",
       "Category Gender                                                   \n",
       "HC       F       24.0  35.67  13.38  19.0  23.0  34.5  47.00  58.0\n",
       "         M       11.0  36.36  11.54  21.0  27.5  38.0  43.50  58.0\n",
       "IBS      F       38.0  36.45  11.96  19.0  27.0  35.5  42.75  61.0\n",
       "         M       11.0  36.45   6.53  28.0  31.5  35.0  40.50  47.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cf. Table 1 in the ORE paper\n",
    "df_all[['Category', 'Gender', 'Age']].groupby(['Category', 'Gender']).describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5a324d48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HC</th>\n",
       "      <td>35.0</td>\n",
       "      <td>35.89</td>\n",
       "      <td>12.66</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBS</th>\n",
       "      <td>49.0</td>\n",
       "      <td>36.45</td>\n",
       "      <td>10.91</td>\n",
       "      <td>19.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count   mean    std   min   25%   50%   75%   max\n",
       "Category                                                   \n",
       "HC         35.0  35.89  12.66  19.0  23.0  36.0  46.5  58.0\n",
       "IBS        49.0  36.45  10.91  19.0  28.0  35.0  43.0  61.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cf. Table 1 in the ORE paper\n",
    "df_all.groupby(['Category']).Age.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57af6d35",
   "metadata": {},
   "source": [
    "**... or, more explicit**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "385cf98e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_IBS = df_all[df_all['Category']=='IBS']\n",
    "df_all_HC = df_all[df_all['Category']=='HC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9ee776c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "l_1 = f\"Variable                  IBS            HC             p-value\"  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b12a0705",
   "metadata": {},
   "source": [
    "## Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a35e7dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "l_2 = f\"Participant count (%) \\t  \\\n",
    "{df_all_IBS['Category'].count()}({np.round(df_all_IBS['Category'].count()*100/df_all['Category'].count(), 2)}) \\t \\\n",
    "{df_all_HC['Category'].count()}({np.round(df_all_HC['Category'].count()*100/df_all['Category'].count(), 2)})\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d209da82",
   "metadata": {},
   "source": [
    "## Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0bce9a8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "l_3 = f\"Males             (%) \\t  \\\n",
    "{df_all_IBS[df_all_IBS['Gender']=='M'].Category.count()}({np.round(df_all_IBS[df_all_IBS['Gender']=='M'].Category.count()*100/df_all[df_all['Gender']=='M'].Category.count(), 2)}) \\t \\\n",
    "{df_all_HC[df_all_HC['Gender']=='M'].Category.count()}({np.round(df_all_HC[df_all_HC['Gender']=='M'].Category.count()*100/df_all[df_all['Gender']=='M'].Category.count(), 2)})\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "455f8c92",
   "metadata": {},
   "outputs": [],
   "source": [
    "l_4 = f\"Females           (%) \\t  \\\n",
    "{df_all_IBS[df_all_IBS['Gender']=='F'].Category.count()}({np.round(df_all_IBS[df_all_IBS['Gender']=='F'].Category.count()*100/df_all[df_all['Gender']=='F'].Category.count(), 2)}) \\t \\\n",
    "{df_all_HC[df_all_HC['Gender']=='F'].Category.count()}({np.round(df_all_HC[df_all_HC['Gender']=='F'].Category.count()*100/df_all[df_all['Gender']=='F'].Category.count(), 2)})\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eab9d9f3",
   "metadata": {},
   "source": [
    "## Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1d87872b",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_ibs = df_all[df_all['Category']=='IBS'].Age.values\n",
    "age_hc = df_all[df_all['Category']=='HC'].Age.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "814484f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Usual boxplot\n",
    "ax = sns.boxplot(x='Category', y='Age', data=df_all)\n",
    "# Add jitter with the swarmplot function\n",
    "ax = sns.swarmplot(x='Category', y='Age', data=df_all, color=\"grey\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "25be0a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>0.218088</td>\n",
       "      <td>82</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.827903</td>\n",
       "      <td>[-4.57, 5.7]</td>\n",
       "      <td>0.048266</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.055339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               T  dof alternative     p-val         CI95%   cohen-d   BF10  \\\n",
       "T-test  0.218088   82   two-sided  0.827903  [-4.57, 5.7]  0.048266  0.235   \n",
       "\n",
       "           power  \n",
       "T-test  0.055339  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To carry out a two-sample t-test using Pingouin we apply the ttest method:\n",
    "res_age = pg.ttest(age_ibs, age_hc, correction=False)\n",
    "res_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9a793114",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T: 0.218088 \t dof: 82 \t p-val: 0.827903\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.weightstats import ttest_ind\n",
    "# ttest_ind(age_ibs, age_hc))\n",
    "print(f\"T: {ttest_ind(age_ibs, age_hc)[0].round(6)} \\t \\\n",
    "dof: {ttest_ind(age_ibs, age_hc)[2].astype(np.int16)} \\t \\\n",
    "p-val: {ttest_ind(age_ibs, age_hc)[1].round(6)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9ae3ccd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "l_5 = f\"Mean age [yr]     (SD) \\t  \\\n",
    "{np.round(df_all[df_all['Category']=='IBS'].Age.mean(),2)}({np.round(df_all[df_all['Category']=='IBS'].Age.std(),2)}) \\t \\\n",
    "{np.round(df_all[df_all['Category']=='HC'].Age.mean(),2)}({np.round(df_all[df_all['Category']=='HC'].Age.std(),2)}) \\t \\\n",
    "{ttest_ind(age_ibs, age_hc)[1].round(3)}\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8a8becc",
   "metadata": {},
   "source": [
    "## Chalder fatigue scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ba99a315",
   "metadata": {},
   "outputs": [],
   "source": [
    "FSS_Chalder_cols = ['FSS_Q1_BL', 'FSS_Q2_BL','FSS_Q3_BL','FSS_Q4_BL','FSS_Q5_BL','FSS_Q6_BL','FSS_Q7_BL','FSS_Q8_BL','FSS_Q9_BL', \n",
    "'FSS_Q10_BL', 'FSS_Q11_BL', 'FSS_Q12_BL', 'FSS_Q13_BL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "53c3205b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fss_dict_no = {'Mye mer enn vanlig': 1,  # 1\n",
    "               'Mer enn vanlig': 1,      # 2\n",
    "               'Som vanlig': 0,          # 3\n",
    "               'Mindre enn vanlig': 0,   # 4\n",
    "               'Ikke mer enn vanlig':0}  # 5\n",
    "\n",
    "fss_dict_en = {'Much more than usual': 1, \n",
    "               'More than usual': 1, \n",
    "               'No more than usual': 0,\n",
    "               'Less than usual': 0,\n",
    "               'Not at all':0}\n",
    "\n",
    "# CFS_Q5 = FSS_Q12_BL\n",
    "QFS_Q5_dict_en = {'Sixth months or more': 1,\n",
    "                  'Between three and sixth months': 2,\n",
    "                  'Less than a month': 3,\n",
    "                  'Less than a week': 4,\n",
    "                  'I don’t feel tired': 5}\n",
    "\n",
    "# Binarized scores for Chadler fatigue scale\n",
    "fss_dict_bin_inv = {1: 1, \n",
    "                    2: 1, \n",
    "                    3: 0,\n",
    "                    4: 0,\n",
    "                    5: 0}\n",
    "\n",
    "# Inverted cores for Chadler fatigue scale\n",
    "fss_dict_inv = {1: 5, \n",
    "                2: 4, \n",
    "                3: 3,\n",
    "                4: 2,\n",
    "                5: 1}\n",
    "\n",
    "# Binarized scores for Chadler fatigue scale - high score is more severe\n",
    "fss_dict_bin = {5: 1, \n",
    "                4: 1, \n",
    "                3: 0,\n",
    "                2: 0,\n",
    "                1: 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d74b5d7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The correct items to include in Chalder scale for binary scores should be:\n",
    "FSS_qs = ['FSS_Q1_BL', 'FSS_Q2_BL', 'FSS_Q3_BL', 'FSS_Q4_BL', 'FSS_Q5_BL', 'FSS_Q6_BL', 'FSS_Q7_BL', \\\n",
    "'FSS_Q8_BL', 'FSS_Q9_BL', 'FSS_Q10_BL', 'FSS_Q11_BL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c9d57d17",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_FSS_bin = df_all.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a21dda0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>pt_anon_ids</th>\n",
       "      <th>BGA_149</th>\n",
       "      <th>BGA_151</th>\n",
       "      <th>BGA_060</th>\n",
       "      <th>BGA_085</th>\n",
       "      <th>BGA_046</th>\n",
       "      <th>BGA_152</th>\n",
       "      <th>BGA_059</th>\n",
       "      <th>BGA_122</th>\n",
       "      <th>BGA_139</th>\n",
       "      <th>BGA_071</th>\n",
       "      <th>...</th>\n",
       "      <th>BGA_161</th>\n",
       "      <th>BGA_142</th>\n",
       "      <th>BGA_130</th>\n",
       "      <th>BGA_147</th>\n",
       "      <th>BGA_074</th>\n",
       "      <th>BGA_076</th>\n",
       "      <th>BGA_115</th>\n",
       "      <th>BGA_094</th>\n",
       "      <th>BGA_088</th>\n",
       "      <th>BGA_065</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FSS_Q1_BL</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q2_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q3_BL</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q4_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q5_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q6_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q7_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q8_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q9_BL</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q10_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q11_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q12_BL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q13_BL</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "pt_anon_ids  BGA_149  BGA_151  BGA_060  BGA_085  BGA_046  BGA_152  BGA_059  \\\n",
       "FSS_Q1_BL        4.0      3.0      4.0      4.0      5.0      4.0      4.0   \n",
       "FSS_Q2_BL        3.0      3.0      4.0      4.0      3.0      3.0      3.0   \n",
       "FSS_Q3_BL        4.0      2.0      4.0      5.0      4.0      4.0      3.0   \n",
       "FSS_Q4_BL        3.0      3.0      4.0      5.0      3.0      5.0      3.0   \n",
       "FSS_Q5_BL        3.0      3.0      4.0      4.0      4.0      4.0      3.0   \n",
       "FSS_Q6_BL        3.0      1.0      4.0      3.0      1.0      4.0      3.0   \n",
       "FSS_Q7_BL        3.0      3.0      4.0      3.0      3.0      4.0      3.0   \n",
       "FSS_Q8_BL        3.0      3.0      4.0      4.0      3.0      5.0      3.0   \n",
       "FSS_Q9_BL        4.0      3.0      3.0      5.0      3.0      3.0      3.0   \n",
       "FSS_Q10_BL       3.0      3.0      4.0      4.0      3.0      3.0      3.0   \n",
       "FSS_Q11_BL       3.0      3.0      5.0      3.0      3.0      4.0      4.0   \n",
       "FSS_Q12_BL       3.0      2.0      5.0      4.0      3.0      4.0      3.0   \n",
       "FSS_Q13_BL       4.0      1.0      2.0      2.0      1.0      2.0      1.0   \n",
       "\n",
       "pt_anon_ids  BGA_122  BGA_139  BGA_071  ...  BGA_161  BGA_142  BGA_130  \\\n",
       "FSS_Q1_BL        4.0      4.0      4.0  ...      5.0      3.0      3.0   \n",
       "FSS_Q2_BL        3.0      4.0      4.0  ...      4.0      3.0      3.0   \n",
       "FSS_Q3_BL        4.0      4.0      4.0  ...      4.0      3.0      4.0   \n",
       "FSS_Q4_BL        4.0      4.0      3.0  ...      5.0      3.0      4.0   \n",
       "FSS_Q5_BL        4.0      5.0      4.0  ...      5.0      3.0      3.0   \n",
       "FSS_Q6_BL        3.0      3.0      4.0  ...      3.0      4.0      1.0   \n",
       "FSS_Q7_BL        3.0      3.0      4.0  ...      3.0      3.0      3.0   \n",
       "FSS_Q8_BL        4.0      4.0      4.0  ...      4.0      3.0      3.0   \n",
       "FSS_Q9_BL        3.0      3.0      4.0  ...      3.0      3.0      3.0   \n",
       "FSS_Q10_BL       3.0      3.0      4.0  ...      4.0      4.0      4.0   \n",
       "FSS_Q11_BL       4.0      3.0      4.0  ...      3.0      4.0      3.0   \n",
       "FSS_Q12_BL       5.0      4.0      4.0  ...      5.0      1.0      1.0   \n",
       "FSS_Q13_BL       2.0      4.0      1.0  ...      2.0      5.0      1.0   \n",
       "\n",
       "pt_anon_ids  BGA_147  BGA_074  BGA_076  BGA_115  BGA_094  BGA_088  BGA_065  \n",
       "FSS_Q1_BL        3.0      5.0      3.0      5.0      5.0      3.0      3.0  \n",
       "FSS_Q2_BL        3.0      5.0      3.0      4.0      4.0      3.0      3.0  \n",
       "FSS_Q3_BL        3.0      5.0      3.0      4.0      4.0      3.0      4.0  \n",
       "FSS_Q4_BL        4.0      4.0      3.0      4.0      5.0      3.0      3.0  \n",
       "FSS_Q5_BL        4.0      5.0      3.0      4.0      4.0      3.0      3.0  \n",
       "FSS_Q6_BL        4.0      4.0      3.0      3.0      4.0      3.0      1.0  \n",
       "FSS_Q7_BL        4.0      4.0      3.0      3.0      4.0      3.0      2.0  \n",
       "FSS_Q8_BL        3.0      4.0      3.0      4.0      5.0      3.0      3.0  \n",
       "FSS_Q9_BL        2.0      5.0      3.0      3.0      3.0      3.0      3.0  \n",
       "FSS_Q10_BL       4.0      5.0      3.0      4.0      3.0      3.0      3.0  \n",
       "FSS_Q11_BL       4.0      5.0      3.0      3.0      4.0      3.0      3.0  \n",
       "FSS_Q12_BL       5.0      5.0      1.0      3.0      4.0      1.0      3.0  \n",
       "FSS_Q13_BL       2.0      2.0      5.0      3.0      2.0      5.0      1.0  \n",
       "\n",
       "[13 rows x 84 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for c in FSS_Chalder_cols:\n",
    "    df_all_FSS_bin.replace({c: fss_dict_bin}, inplace=True)\n",
    "\n",
    "df_all[FSS_Chalder_cols].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "124b50a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>pt_anon_ids</th>\n",
       "      <th>BGA_149</th>\n",
       "      <th>BGA_151</th>\n",
       "      <th>BGA_060</th>\n",
       "      <th>BGA_085</th>\n",
       "      <th>BGA_046</th>\n",
       "      <th>BGA_152</th>\n",
       "      <th>BGA_059</th>\n",
       "      <th>BGA_122</th>\n",
       "      <th>BGA_139</th>\n",
       "      <th>BGA_071</th>\n",
       "      <th>...</th>\n",
       "      <th>BGA_161</th>\n",
       "      <th>BGA_142</th>\n",
       "      <th>BGA_130</th>\n",
       "      <th>BGA_147</th>\n",
       "      <th>BGA_074</th>\n",
       "      <th>BGA_076</th>\n",
       "      <th>BGA_115</th>\n",
       "      <th>BGA_094</th>\n",
       "      <th>BGA_088</th>\n",
       "      <th>BGA_065</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FSS_Q1_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q2_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q3_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q4_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q5_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q6_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q7_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q8_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q9_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q10_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q11_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q12_BL</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q13_BL</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "pt_anon_ids  BGA_149  BGA_151  BGA_060  BGA_085  BGA_046  BGA_152  BGA_059  \\\n",
       "FSS_Q1_BL        1.0      0.0      1.0      1.0      1.0      1.0      1.0   \n",
       "FSS_Q2_BL        0.0      0.0      1.0      1.0      0.0      0.0      0.0   \n",
       "FSS_Q3_BL        1.0      0.0      1.0      1.0      1.0      1.0      0.0   \n",
       "FSS_Q4_BL        0.0      0.0      1.0      1.0      0.0      1.0      0.0   \n",
       "FSS_Q5_BL        0.0      0.0      1.0      1.0      1.0      1.0      0.0   \n",
       "FSS_Q6_BL        0.0      0.0      1.0      0.0      0.0      1.0      0.0   \n",
       "FSS_Q7_BL        0.0      0.0      1.0      0.0      0.0      1.0      0.0   \n",
       "FSS_Q8_BL        0.0      0.0      1.0      1.0      0.0      1.0      0.0   \n",
       "FSS_Q9_BL        1.0      0.0      0.0      1.0      0.0      0.0      0.0   \n",
       "FSS_Q10_BL       0.0      0.0      1.0      1.0      0.0      0.0      0.0   \n",
       "FSS_Q11_BL       0.0      0.0      1.0      0.0      0.0      1.0      1.0   \n",
       "FSS_Q12_BL       0.0      0.0      1.0      1.0      0.0      1.0      0.0   \n",
       "FSS_Q13_BL       1.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "pt_anon_ids  BGA_122  BGA_139  BGA_071  ...  BGA_161  BGA_142  BGA_130  \\\n",
       "FSS_Q1_BL        1.0      1.0      1.0  ...      1.0      0.0      0.0   \n",
       "FSS_Q2_BL        0.0      1.0      1.0  ...      1.0      0.0      0.0   \n",
       "FSS_Q3_BL        1.0      1.0      1.0  ...      1.0      0.0      1.0   \n",
       "FSS_Q4_BL        1.0      1.0      0.0  ...      1.0      0.0      1.0   \n",
       "FSS_Q5_BL        1.0      1.0      1.0  ...      1.0      0.0      0.0   \n",
       "FSS_Q6_BL        0.0      0.0      1.0  ...      0.0      1.0      0.0   \n",
       "FSS_Q7_BL        0.0      0.0      1.0  ...      0.0      0.0      0.0   \n",
       "FSS_Q8_BL        1.0      1.0      1.0  ...      1.0      0.0      0.0   \n",
       "FSS_Q9_BL        0.0      0.0      1.0  ...      0.0      0.0      0.0   \n",
       "FSS_Q10_BL       0.0      0.0      1.0  ...      1.0      1.0      1.0   \n",
       "FSS_Q11_BL       1.0      0.0      1.0  ...      0.0      1.0      0.0   \n",
       "FSS_Q12_BL       1.0      1.0      1.0  ...      1.0      0.0      0.0   \n",
       "FSS_Q13_BL       0.0      1.0      0.0  ...      0.0      1.0      0.0   \n",
       "\n",
       "pt_anon_ids  BGA_147  BGA_074  BGA_076  BGA_115  BGA_094  BGA_088  BGA_065  \n",
       "FSS_Q1_BL        0.0      1.0      0.0      1.0      1.0      0.0      0.0  \n",
       "FSS_Q2_BL        0.0      1.0      0.0      1.0      1.0      0.0      0.0  \n",
       "FSS_Q3_BL        0.0      1.0      0.0      1.0      1.0      0.0      1.0  \n",
       "FSS_Q4_BL        1.0      1.0      0.0      1.0      1.0      0.0      0.0  \n",
       "FSS_Q5_BL        1.0      1.0      0.0      1.0      1.0      0.0      0.0  \n",
       "FSS_Q6_BL        1.0      1.0      0.0      0.0      1.0      0.0      0.0  \n",
       "FSS_Q7_BL        1.0      1.0      0.0      0.0      1.0      0.0      0.0  \n",
       "FSS_Q8_BL        0.0      1.0      0.0      1.0      1.0      0.0      0.0  \n",
       "FSS_Q9_BL        0.0      1.0      0.0      0.0      0.0      0.0      0.0  \n",
       "FSS_Q10_BL       1.0      1.0      0.0      1.0      0.0      0.0      0.0  \n",
       "FSS_Q11_BL       1.0      1.0      0.0      0.0      1.0      0.0      0.0  \n",
       "FSS_Q12_BL       1.0      1.0      0.0      0.0      1.0      0.0      0.0  \n",
       "FSS_Q13_BL       0.0      0.0      1.0      0.0      0.0      1.0      0.0  \n",
       "\n",
       "[13 rows x 84 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_FSS_bin[FSS_Chalder_cols].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4d882be6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save file with Chalder sum score beeing added\n",
    "df_all_TFS_Chalder = df_all_FSS_bin.copy()\n",
    "df_all_TFS_Chalder['TFS_Chalder'] =  df_all_FSS_bin[FSS_qs].sum(axis=1).values\n",
    "df_all_TFS_Chalder.to_csv('data/train_valid_test_TFS_Chalder.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3c038120",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chalder fatigue scale comprises: ['FSS_Q1_BL', 'FSS_Q2_BL', 'FSS_Q3_BL', 'FSS_Q4_BL', 'FSS_Q5_BL', 'FSS_Q6_BL', 'FSS_Q7_BL', 'FSS_Q8_BL', 'FSS_Q9_BL', 'FSS_Q10_BL', 'FSS_Q11_BL'] \n",
      "\n",
      "Total Chalder (Fatigue) score: mean across 49 IBS subjects, i.e. TFS_ibs.mean().round(2): 6.37\n",
      "Total Chalder (Fatigue) score: SD across 49 IBS subjects, i.e. TFS_ibs.std().round(2): 3.39\n",
      "Total Chalder (Fatigue) score: mean across 35 HC subjects, i.e. TFS_hc.mean().round(2): 1.6\n",
      "Total Chalder (Fatigue) score: SD across 35 HC subjects, i.e. TFS_hc.std().round(2): 2.46\n"
     ]
    }
   ],
   "source": [
    "df_all_FSS_bin_IBS = df_all_FSS_bin[df_all_FSS_bin['Category']=='IBS']\n",
    "df_all_FSS_bin_HC = df_all_FSS_bin[df_all_FSS_bin['Category']=='HC']\n",
    "\n",
    "print(f\"Chalder fatigue scale comprises: {FSS_qs} \\n\")\n",
    "TFS_ibs =  df_all_FSS_bin_IBS[FSS_qs].sum(axis=1).values\n",
    "print(f\"Total Chalder (Fatigue) score: mean across {df_all_FSS_bin_IBS['Category'].count()} IBS subjects, i.e. TFS_ibs.mean().round(2): {TFS_ibs.mean().round(2)}\")\n",
    "print(f\"Total Chalder (Fatigue) score: SD across {df_all_FSS_bin_IBS['Category'].count()} IBS subjects, i.e. TFS_ibs.std().round(2): {TFS_ibs.std().round(2)}\")\n",
    "\n",
    "TFS_hc =  df_all_FSS_bin_HC[FSS_qs].sum(axis=1).values\n",
    "print(f\"Total Chalder (Fatigue) score: mean across {df_all_FSS_bin_HC['Category'].count()} HC subjects, i.e. TFS_hc.mean().round(2): {TFS_hc.mean().round(2)}\")\n",
    "print(f\"Total Chalder (Fatigue) score: SD across {df_all_FSS_bin_HC['Category'].count()} HC subjects, i.e. TFS_hc.std().round(2): {TFS_hc.std().round(2)}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f63973e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FSS_Q1_BL</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q2_BL</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q3_BL</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q4_BL</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q5_BL</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q6_BL</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q7_BL</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q8_BL</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q9_BL</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q10_BL</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FSS_Q11_BL</th>\n",
       "      <td>84.0</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count  mean   std  min  25%  50%  75%  max\n",
       "FSS_Q1_BL    84.0  0.56  0.50  0.0  0.0  1.0  1.0  1.0\n",
       "FSS_Q2_BL    84.0  0.46  0.50  0.0  0.0  0.0  1.0  1.0\n",
       "FSS_Q3_BL    84.0  0.58  0.50  0.0  0.0  1.0  1.0  1.0\n",
       "FSS_Q4_BL    84.0  0.46  0.50  0.0  0.0  0.0  1.0  1.0\n",
       "FSS_Q5_BL    84.0  0.52  0.50  0.0  0.0  1.0  1.0  1.0\n",
       "FSS_Q6_BL    84.0  0.31  0.47  0.0  0.0  0.0  1.0  1.0\n",
       "FSS_Q7_BL    84.0  0.27  0.45  0.0  0.0  0.0  1.0  1.0\n",
       "FSS_Q8_BL    84.0  0.39  0.49  0.0  0.0  0.0  1.0  1.0\n",
       "FSS_Q9_BL    84.0  0.14  0.35  0.0  0.0  0.0  0.0  1.0\n",
       "FSS_Q10_BL   84.0  0.31  0.47  0.0  0.0  0.0  1.0  1.0\n",
       "FSS_Q11_BL   84.0  0.36  0.48  0.0  0.0  0.0  1.0  1.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_FSS_bin[FSS_qs].describe().T.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5ca39c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_TFS = df_all_FSS_bin.copy()\n",
    "df_all_TFS['TFS'] = df_all_FSS_bin[FSS_qs].sum(axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "19576192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x='Category', y='TFS', data=df_all_TFS)\n",
    "# Add jitter with the swarmplot function\n",
    "ax = sns.swarmplot(x='Category', y='TFS', data=df_all_TFS, color=\"grey\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e22bd563",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>7.003331</td>\n",
       "      <td>82</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>6.320670e-10</td>\n",
       "      <td>[3.41, 6.12]</td>\n",
       "      <td>1.54993</td>\n",
       "      <td>1.27e+07</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               T  dof alternative         p-val         CI95%  cohen-d  \\\n",
       "T-test  7.003331   82   two-sided  6.320670e-10  [3.41, 6.12]  1.54993   \n",
       "\n",
       "            BF10  power  \n",
       "T-test  1.27e+07    1.0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To carry out a two-sample t-test using Pingouin we apply the ttest method:\n",
    "res_tfs = pg.ttest(TFS_ibs, TFS_hc, correction=False)\n",
    "res_tfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dbbc4f0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'< 0.001'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if res_tfs['p-val'].values[0] < 0.001:\n",
    "    p_value_tfs = '< 0.001'\n",
    "else:\n",
    "    p_value_tfs = res_tfs['p-val'].values[0].round(3)\n",
    "    \n",
    "p_value_tfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "da38238b",
   "metadata": {},
   "outputs": [],
   "source": [
    "l_6 = f\"TFS (Chalder 0/1) (SD) \\t  \\\n",
    "{TFS_ibs.mean().round(2)}({TFS_ibs.std().round(2)}) \\t \\\n",
    "{TFS_hc.mean().round(2)}({TFS_hc.std().round(2)}) \\t {p_value_tfs}\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19870c44",
   "metadata": {},
   "source": [
    "## Sleep (BIS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c69bb45f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bergen insomnia sacle comprises: ['BIS_Q1_BL', 'BIS_Q2_BL', 'BIS_Q3_BL', 'BIS_Q4_BL', 'BIS_Q5_BL', 'BIS_Q6_BL'] \n",
      "\n",
      "Total Sleep (Bergen insomnia) score: mean across 49 IBS subjects, i.e. TSS_ibs.mean().round(2): 16.92\n",
      "Total Sleep (Bergen insomnia) score: SD across 49 IBS subjects, i.e. TSS_ibs.std().round(2): 6.78\n",
      "Total Sleep (Bergen insomnia) score: mean across 35 HC subjects, i.e. TSS_hc.mean().round(2): 10.29\n",
      "Total Sleep (Bergen insomnia) score: SD across 35 HC subjects, i.e. TSS_hc.std().round(2): 7.15\n"
     ]
    }
   ],
   "source": [
    "# Bergen insomnia scale\n",
    "BIS_qs = ['BIS_Q1_BL', 'BIS_Q2_BL', 'BIS_Q3_BL', 'BIS_Q4_BL', 'BIS_Q5_BL', 'BIS_Q6_BL']\n",
    "\n",
    "print(f\"Bergen insomnia sacle comprises: {BIS_qs} \\n\")\n",
    "TSS_ibs =  df_all_IBS[BIS_qs].sum(axis=1).values\n",
    "print(f\"Total Sleep (Bergen insomnia) score: mean across {df_all_IBS['Category'].count()} IBS subjects, i.e. TSS_ibs.mean().round(2): {TSS_ibs.mean().round(2)}\")\n",
    "print(f\"Total Sleep (Bergen insomnia) score: SD across {df_all_IBS['Category'].count()} IBS subjects, i.e. TSS_ibs.std().round(2): {TSS_ibs.std().round(2)}\")\n",
    "\n",
    "TSS_hc =  df_all_HC[BIS_qs].sum(axis=1).values\n",
    "print(f\"Total Sleep (Bergen insomnia) score: mean across {df_all_HC['Category'].count()} HC subjects, i.e. TSS_hc.mean().round(2): {TSS_hc.mean().round(2)}\")\n",
    "print(f\"Total Sleep (Bergen insomnia) score: SD across {df_all_HC['Category'].count()} HC subjects, i.e. TSS_hc.std().round(2): {TSS_hc.std().round(2)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9ed6698e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_TSS = df_all.copy()\n",
    "df_all_TSS['TSS'] = df_all[BIS_qs].sum(axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f912a1d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x='Category', y='TSS', data=df_all_TSS)\n",
    "# Add jitter with the swarmplot function\n",
    "ax = sns.swarmplot(x='Category', y='TSS', data=df_all_TSS, color=\"grey\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "84ce84ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>4.267143</td>\n",
       "      <td>82</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>[3.54, 9.72]</td>\n",
       "      <td>0.944376</td>\n",
       "      <td>389.334</td>\n",
       "      <td>0.987987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               T  dof alternative     p-val         CI95%   cohen-d     BF10  \\\n",
       "T-test  4.267143   82   two-sided  0.000053  [3.54, 9.72]  0.944376  389.334   \n",
       "\n",
       "           power  \n",
       "T-test  0.987987  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To carry out a two-sample t-test using Pingouin we apply the ttest method:\n",
    "res_tss = pg.ttest(TSS_ibs, TSS_hc, correction=False)\n",
    "res_tss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3ca64fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'< 0.001'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if res_tss['p-val'].values[0] < 0.001:\n",
    "    p_value_tss = '< 0.001'\n",
    "else:\n",
    "    p_value_tss = res_tss['p-val'].values[0].round(3)\n",
    "    \n",
    "p_value_tss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "20fb2b23",
   "metadata": {},
   "outputs": [],
   "source": [
    "l_7 = f\"TSS (BIS)         (SD) \\t  \\\n",
    "{TSS_ibs.mean().round(2)}({TSS_ibs.std().round(2)}) \\t \\\n",
    "{TSS_hc.mean().round(2)}({TSS_hc.std().round(2)}) \\t {p_value_tss}\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad6b138",
   "metadata": {},
   "source": [
    "## Anxiety (HADS odd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "dd94ae8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ANX_qs = ['HADS_q1_BL', 'HADS_q3_BL', 'HADS_q5_BL', 'HADS_q7_BL', 'HADS_q9_BL', 'HADS_q11_BL', 'HADS_q13_BL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ffdb6eb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anxiety scale comprises: ['HADS_q1_BL', 'HADS_q3_BL', 'HADS_q5_BL', 'HADS_q7_BL', 'HADS_q9_BL', 'HADS_q11_BL', 'HADS_q13_BL'] \n",
      "\n",
      "Total Anxiety score from HADS odd: mean across 49 IBS subjects, i.e. ANX_ibs.mean().round(2): 8.2\n",
      "Total Anxiety score from HADS odd: SD across 49 IBS subjects, i.e. ANX_ibs.std().round(2): 4.11\n",
      "Total Anxiety score from HADS odd: mean across 35 HC subjects, i.e. ANX_hc.mean().round(2): 4.09\n",
      "Total Anxiety score from HADS odd: SD across 35 HC subjects, i.e. ANX_hc.std().round(2): 3.25\n"
     ]
    }
   ],
   "source": [
    "print(f\"Anxiety scale comprises: {ANX_qs} \\n\")\n",
    "ANX_ibs =  df_all_IBS[ANX_qs].sum(axis=1).values\n",
    "print(f\"Total Anxiety score from HADS odd: mean across {df_all_IBS['Category'].count()} IBS subjects, i.e. ANX_ibs.mean().round(2): {ANX_ibs.mean().round(2)}\")\n",
    "print(f\"Total Anxiety score from HADS odd: SD across {df_all_IBS['Category'].count()} IBS subjects, i.e. ANX_ibs.std().round(2): {ANX_ibs.std().round(2)}\")\n",
    "ANX_hc =  df_all_HC[ANX_qs].sum(axis=1).values\n",
    "print(f\"Total Anxiety score from HADS odd: mean across {df_all_HC['Category'].count()} HC subjects, i.e. ANX_hc.mean().round(2): {ANX_hc.mean().round(2)}\")\n",
    "print(f\"Total Anxiety score from HADS odd: SD across {df_all_HC['Category'].count()} HC subjects, i.e. ANX_hc.std().round(2): {ANX_hc.std().round(2)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9aa9bdc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_ANX = df_all.copy()\n",
    "df_all_ANX['ANX'] = df_all[ANX_qs].sum(axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d9fcf700",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Category', y='ANX', data=df_all_ANX)\n",
    "# Add jitter with the swarmplot function\n",
    "ax = sns.swarmplot(x='Category', y='ANX', data=df_all_ANX, color=\"grey\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6b9f7268",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>4.871149</td>\n",
       "      <td>82</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>[2.44, 5.8]</td>\n",
       "      <td>1.07805</td>\n",
       "      <td>3021.433</td>\n",
       "      <td>0.997837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               T  dof alternative     p-val        CI95%  cohen-d      BF10  \\\n",
       "T-test  4.871149   82   two-sided  0.000005  [2.44, 5.8]  1.07805  3021.433   \n",
       "\n",
       "           power  \n",
       "T-test  0.997837  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To carry out a two-sample t-test using Pingouin we apply the ttest method:\n",
    "res_anx = pg.ttest(ANX_ibs, ANX_hc, correction=False)\n",
    "res_anx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3ff7b3ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'< 0.001'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if res_anx['p-val'].values[0] < 0.001:\n",
    "    p_value_anx = '< 0.001'\n",
    "else:\n",
    "    p_value_anx = res_anx['p-val'].values[0].round(3)\n",
    "    \n",
    "p_value_anx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4fdfb4ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "l_8 = f\"TSS (HADS odd)    (SD) \\t  \\\n",
    "{ANX_ibs.mean().round(2)}({ANX_ibs.std().round(2)}) \\t \\\n",
    "{ANX_hc.mean().round(2)}({ANX_hc.std().round(2)}) \\t {p_value_anx}\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19055fe8",
   "metadata": {},
   "source": [
    "## Depression (HADS even)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c5fa95bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "DEP_qs = ['HADS_q2_BL', 'HADS_q4_BL', 'HADS_q6_BL', 'HADS_q8_BL', 'HADS_q10_BL', 'HADS_q12_BL', 'HADS_q14_BL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "bc55a553",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depression scale comprises: ['HADS_q2_BL', 'HADS_q4_BL', 'HADS_q6_BL', 'HADS_q8_BL', 'HADS_q10_BL', 'HADS_q12_BL', 'HADS_q14_BL'] \n",
      "\n",
      "Total Depression score from HADS even: mean across 49 IBS subjects, i.e. DEP_ibs.mean().round(2): 4.9\n",
      "Total Depression score from HADS even: SD across 49 IBS subjects, i.e. DEP_ibs.std().round(2): 3.09\n",
      "Total Depression score from HADS even: mean across 35 HC subjects, i.e. DEP_hc.mean().round(2): 2.09\n",
      "Total Depression score from HADS even: SD across 35 HC subjects, i.e. DEP_hc.std().round(2): 2.3\n"
     ]
    }
   ],
   "source": [
    "print(f\"Depression scale comprises: {DEP_qs} \\n\")\n",
    "DEP_ibs =  df_all_IBS[DEP_qs].sum(axis=1).values\n",
    "print(f\"Total Depression score from HADS even: mean across {df_all_IBS['Category'].count()} IBS subjects, i.e. DEP_ibs.mean().round(2): {DEP_ibs.mean().round(2)}\")\n",
    "print(f\"Total Depression score from HADS even: SD across {df_all_IBS['Category'].count()} IBS subjects, i.e. DEP_ibs.std().round(2): {DEP_ibs.std().round(2)}\")\n",
    "DEP_hc =  df_all_HC[DEP_qs].sum(axis=1).values\n",
    "print(f\"Total Depression score from HADS even: mean across {df_all_HC['Category'].count()} HC subjects, i.e. DEP_hc.mean().round(2): {DEP_hc.mean().round(2)}\")\n",
    "print(f\"Total Depression score from HADS even: SD across {df_all_HC['Category'].count()} HC subjects, i.e. DEP_hc.std().round(2): {DEP_hc.std().round(2)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "bee9d4b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_DEP = df_all.copy()\n",
    "df_all_DEP['DEP'] = df_all[DEP_qs].sum(axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "57df7424",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Category', y='DEP', data=df_all_DEP)\n",
    "# Add jitter with the swarmplot function\n",
    "ax = sns.swarmplot(x='Category', y='DEP', data=df_all_DEP, color=\"grey\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2c2d5218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>dof</th>\n",
       "      <th>alternative</th>\n",
       "      <th>p-val</th>\n",
       "      <th>CI95%</th>\n",
       "      <th>cohen-d</th>\n",
       "      <th>BF10</th>\n",
       "      <th>power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T-test</th>\n",
       "      <td>4.502135</td>\n",
       "      <td>82</td>\n",
       "      <td>two-sided</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>[1.57, 4.05]</td>\n",
       "      <td>0.996383</td>\n",
       "      <td>845.684</td>\n",
       "      <td>0.993592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               T  dof alternative     p-val         CI95%   cohen-d     BF10  \\\n",
       "T-test  4.502135   82   two-sided  0.000022  [1.57, 4.05]  0.996383  845.684   \n",
       "\n",
       "           power  \n",
       "T-test  0.993592  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To carry out a two-sample t-test using Pingouin we apply the ttest method:\n",
    "res_dep = pg.ttest(DEP_ibs, DEP_hc, correction=False)\n",
    "res_dep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f5becd0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'< 0.001'"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if res_dep['p-val'].values[0] < 0.001:\n",
    "    p_value_dep = '< 0.001'\n",
    "else:\n",
    "    p_value_dep = res_dep['p-val'].values[0].round(3)\n",
    "    \n",
    "p_value_dep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4ff56587",
   "metadata": {},
   "outputs": [],
   "source": [
    "l_9 = f\"TDS (HADS even)   (SD) \\t  \\\n",
    "{DEP_ibs.mean().round(2)}({DEP_ibs.std().round(2)}) \\t \\\n",
    "{DEP_hc.mean().round(2)}({DEP_hc.std().round(2)}) \\t {p_value_anx}\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776873ea",
   "metadata": {},
   "source": [
    " ## Finally, print the content of Table 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "973cfe3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table 1: Key characteristics of the participants included in the study (n=84)\n",
      "\n",
      "Variable                  IBS            HC             p-value\n",
      "Participant count (%) \t  49(58.33) \t 35(41.67)\n",
      "Males             (%) \t  11(50.0) \t 11(50.0)\n",
      "Females           (%) \t  38(61.29) \t 24(38.71)\n",
      "Mean age [yr]     (SD) \t  36.45(10.91) \t 35.89(12.66) \t 0.828\n",
      "TFS (Chalder 0/1) (SD) \t  6.37(3.39) \t 1.6(2.46) \t < 0.001\n",
      "TSS (BIS)         (SD) \t  16.92(6.78) \t 10.29(7.15) \t < 0.001\n",
      "TSS (HADS odd)    (SD) \t  8.2(4.11) \t 4.09(3.25) \t < 0.001\n",
      "TDS (HADS even)   (SD) \t  4.9(3.09) \t 2.09(2.3) \t < 0.001\n"
     ]
    }
   ],
   "source": [
    "print(f\"Table 1: Key characteristics of the participants included in the study (n={df_all.Category.count()})\\n\")\n",
    "for i in range(9):\n",
    "    cmd = f\"print(l_{i+1})\"\n",
    "    eval(cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c3b9ff5",
   "metadata": {},
   "source": [
    "**Some more info and checking**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a4285d57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub_category</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>BIS_Q1_BL</th>\n",
       "      <th>BIS_Q2_BL</th>\n",
       "      <th>BIS_Q3_BL</th>\n",
       "      <th>BIS_Q4_BL</th>\n",
       "      <th>BIS_Q5_BL</th>\n",
       "      <th>BIS_Q6_BL</th>\n",
       "      <th>...</th>\n",
       "      <th>HADS_q7_BL</th>\n",
       "      <th>HADS_q8_BL</th>\n",
       "      <th>HADS_q9_BL</th>\n",
       "      <th>HADS_q10_BL</th>\n",
       "      <th>HADS_q11_BL</th>\n",
       "      <th>HADS_q12_BL</th>\n",
       "      <th>HADS_q13_BL</th>\n",
       "      <th>HADS_q14_BL</th>\n",
       "      <th>Gender_q</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stratification</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Constipation_F</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "</table>\n",
       "<p>7 rows × 39 columns</p>\n",
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      ],
      "text/plain": [
       "                Category  Sub_category  Gender  Age  BIS_Q1_BL  BIS_Q2_BL  \\\n",
       "stratification                                                              \n",
       "Constipation_F         3             3       3    3          3          3   \n",
       "Diarrhea_F             7             7       7    7          7          7   \n",
       "Diarrhea_M             4             4       4    4          4          4   \n",
       "HC_F                  14            14      14   14         14         14   \n",
       "HC_M                   7             7       7    7          7          7   \n",
       "Mixed_F               12            12      12   12         12         12   \n",
       "Mixed_M                3             3       3    3          3          3   \n",
       "\n",
       "                BIS_Q3_BL  BIS_Q4_BL  BIS_Q5_BL  BIS_Q6_BL  ...  HADS_q7_BL  \\\n",
       "stratification                                              ...               \n",
       "Constipation_F          3          3          3          3  ...           3   \n",
       "Diarrhea_F              7          7          7          7  ...           7   \n",
       "Diarrhea_M              4          4          4          4  ...           4   \n",
       "HC_F                   14         14         14         14  ...          14   \n",
       "HC_M                    7          7          7          7  ...           7   \n",
       "Mixed_F                12         12         12         12  ...          12   \n",
       "Mixed_M                 3          3          3          3  ...           3   \n",
       "\n",
       "                HADS_q8_BL  HADS_q9_BL  HADS_q10_BL  HADS_q11_BL  HADS_q12_BL  \\\n",
       "stratification                                                                  \n",
       "Constipation_F           3           3            3            3            3   \n",
       "Diarrhea_F               7           7            7            7            7   \n",
       "Diarrhea_M               4           4            4            4            4   \n",
       "HC_F                    14          14           14           14           14   \n",
       "HC_M                     7           7            7            7            7   \n",
       "Mixed_F                 12          12           12           12           12   \n",
       "Mixed_M                  3           3            3            3            3   \n",
       "\n",
       "                HADS_q13_BL  HADS_q14_BL  Gender_q  label  \n",
       "stratification                                             \n",
       "Constipation_F            3            3         3      3  \n",
       "Diarrhea_F                7            7         7      7  \n",
       "Diarrhea_M                4            4         4      4  \n",
       "HC_F                     14           14        14     14  \n",
       "HC_M                      7            7         7      7  \n",
       "Mixed_F                  12           12        12     12  \n",
       "Mixed_M                   3            3         3      3  \n",
       "\n",
       "[7 rows x 39 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.groupby(['stratification']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "05ada16d",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "                Category  Sub_category  Gender  Age  BIS_Q1_BL  BIS_Q2_BL  \\\n",
       "stratification                                                              \n",
       "Constipation_F         1             1       1    1          1          1   \n",
       "Diarrhea_F             3             3       3    3          3          3   \n",
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       "\n",
       "                BIS_Q3_BL  BIS_Q4_BL  BIS_Q5_BL  BIS_Q6_BL  ...  HADS_q7_BL  \\\n",
       "stratification                                              ...               \n",
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       "\n",
       "                HADS_q8_BL  HADS_q9_BL  HADS_q10_BL  HADS_q11_BL  HADS_q12_BL  \\\n",
       "stratification                                                                  \n",
       "Constipation_F           1           1            1            1            1   \n",
       "Diarrhea_F               3           3            3            3            3   \n",
       "Diarrhea_M               1           1            1            1            1   \n",
       "HC_F                     5           5            5            5            5   \n",
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       "Mixed_F                  4           4            4            4            4   \n",
       "Mixed_M                  1           1            1            1            1   \n",
       "\n",
       "                HADS_q13_BL  HADS_q14_BL  Gender_q  label  \n",
       "stratification                                             \n",
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       "Diarrhea_F                3            3         3      3  \n",
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       "\n",
       "[7 rows x 39 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_set.groupby(['stratification']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "6d6d3f28",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>stratification</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Constipation_F</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diarrhea_F</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diarrhea_M</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HC_F</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HC_M</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mixed_F</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mixed_M</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Category  Sub_category  Gender  Age  BIS_Q1_BL  BIS_Q2_BL  \\\n",
       "stratification                                                              \n",
       "Constipation_F         1             1       1    1          1          1   \n",
       "Diarrhea_F             3             3       3    3          3          3   \n",
       "Diarrhea_M             1             1       1    1          1          1   \n",
       "HC_F                   5             5       5    5          5          5   \n",
       "HC_M                   2             2       2    2          2          2   \n",
       "Mixed_F                4             4       4    4          4          4   \n",
       "Mixed_M                1             1       1    1          1          1   \n",
       "\n",
       "                BIS_Q3_BL  BIS_Q4_BL  BIS_Q5_BL  BIS_Q6_BL  ...  HADS_q7_BL  \\\n",
       "stratification                                              ...               \n",
       "Constipation_F          1          1          1          1  ...           1   \n",
       "Diarrhea_F              3          3          3          3  ...           3   \n",
       "Diarrhea_M              1          1          1          1  ...           1   \n",
       "HC_F                    5          5          5          5  ...           5   \n",
       "HC_M                    2          2          2          2  ...           2   \n",
       "Mixed_F                 4          4          4          4  ...           4   \n",
       "Mixed_M                 1          1          1          1  ...           1   \n",
       "\n",
       "                HADS_q8_BL  HADS_q9_BL  HADS_q10_BL  HADS_q11_BL  HADS_q12_BL  \\\n",
       "stratification                                                                  \n",
       "Constipation_F           1           1            1            1            1   \n",
       "Diarrhea_F               3           3            3            3            3   \n",
       "Diarrhea_M               1           1            1            1            1   \n",
       "HC_F                     5           5            5            5            5   \n",
       "HC_M                     2           2            2            2            2   \n",
       "Mixed_F                  4           4            4            4            4   \n",
       "Mixed_M                  1           1            1            1            1   \n",
       "\n",
       "                HADS_q13_BL  HADS_q14_BL  Gender_q  label  \n",
       "stratification                                             \n",
       "Constipation_F            1            1         1      1  \n",
       "Diarrhea_F                3            3         3      3  \n",
       "Diarrhea_M                1            1         1      1  \n",
       "HC_F                      5            5         5      5  \n",
       "HC_M                      2            2         2      2  \n",
       "Mixed_F                   4            4         4      4  \n",
       "Mixed_M                   1            1         1      1  \n",
       "\n",
       "[7 rows x 39 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.groupby(['stratification']).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41477cc0",
   "metadata": {},
   "source": [
    "## Generating all possible combinations of four questionnaires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fe302185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Anx',\n",
       " 'Dep',\n",
       " 'Bis',\n",
       " 'Fss',\n",
       " 'Anx_Dep',\n",
       " 'Anx_Bis',\n",
       " 'Anx_Fss',\n",
       " 'Dep_Bis',\n",
       " 'Dep_Fss',\n",
       " 'Bis_Fss',\n",
       " 'Anx_Dep_Bis',\n",
       " 'Anx_Dep_Fss',\n",
       " 'Anx_Bis_Fss',\n",
       " 'Dep_Bis_Fss',\n",
       " 'Anx_Dep_Bis_Fss']"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questionnaire_combinations = generate_questionnaire_combinations(questionnaires)\n",
    "list(questionnaire_combinations.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed6fa671",
   "metadata": {},
   "source": [
    "#### Generating ten folds of train_set in accordance with participant stratification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a397403b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 6,  8, 48, 45, 10, 25, 41, 11, 29, 47, 26,  1, 37, 22, 39,  2,  3,\n",
      "       24, 20, 31,  7,  9, 28, 43, 14, 38, 15, 17, 44,  5, 36, 30, 46, 12,\n",
      "       13, 16,  4]), array([35, 42, 27, 34, 40, 18,  0, 19, 49, 21, 32, 33, 23])) \n",
      "\n",
      "(array([23, 39, 40, 44, 37,  7, 42, 17, 41,  5, 11, 36, 29, 19, 26,  3, 16,\n",
      "       10, 31, 28, 45, 38, 14,  4,  8, 25,  9, 22,  1, 30, 48, 33, 18, 15,\n",
      "       13, 24, 20]), array([43, 35, 12, 32, 21, 34,  6,  2, 27,  0, 47, 49, 46])) \n",
      "\n",
      "(array([38, 24, 29,  8,  1,  9, 23, 17, 36, 41, 25, 26,  7,  4, 44, 37, 39,\n",
      "       34, 20, 43, 40, 32, 28, 10, 13, 48, 18, 33, 14, 16,  6, 49, 27, 42,\n",
      "       22, 45,  5]), array([19,  0, 11, 30, 21, 35,  3, 15, 12,  2, 46, 47, 31])) \n",
      "\n",
      "(array([14, 45, 44, 16, 31, 34, 20,  9, 24, 18, 32, 29, 25, 19, 11,  8, 13,\n",
      "        4, 26, 40, 21, 33, 42, 35, 23, 38,  1, 39,  6,  7, 46,  3, 27,  2,\n",
      "        5,  0, 36]), array([43, 22, 17, 48, 49, 12, 28, 47, 15, 37, 41, 30, 10])) \n",
      "\n",
      "(array([ 4, 48, 22,  3, 13, 14, 19, 17, 31, 34, 39, 15, 20,  0,  2, 25, 11,\n",
      "       46, 38, 23,  6, 27,  8, 35, 40, 41,  5, 29, 21, 33, 24, 43, 26,  7,\n",
      "       47, 45, 32]), array([12,  1, 28, 44, 10, 18, 36, 16, 30, 49,  9, 42, 37])) \n",
      "\n",
      "(array([24, 23,  4, 20, 37, 15,  3,  7, 18,  9, 26, 12, 42,  1, 35, 17, 11,\n",
      "       49,  2, 22, 14, 10, 38, 32, 44, 29, 39, 34, 30, 46, 28, 48, 19, 41,\n",
      "       45, 33, 13]), array([40, 25,  5, 16,  8, 43, 47, 36, 27, 21,  6,  0, 31])) \n",
      "\n",
      "(array([25, 30, 42,  9, 35, 10,  6, 38, 16, 41, 17,  3, 36, 45, 20, 43, 14,\n",
      "       26, 46, 32, 40, 34, 13, 39, 24, 49, 28, 19, 18,  0, 33,  2, 22, 37,\n",
      "       12, 47, 23]), array([31, 21,  5,  1, 27,  8, 15, 48, 11,  7,  4, 29, 44])) \n",
      "\n",
      "(array([16, 12,  6,  7,  2, 33, 26, 31, 19, 14,  5,  1, 32, 46, 17, 30, 45,\n",
      "       41, 39, 38,  8, 44, 42, 18,  4, 47, 11, 10, 49, 25, 20, 27, 43, 24,\n",
      "       36,  9, 40]), array([48,  0, 35, 23, 37, 22, 13, 34, 28, 29,  3, 15, 21])) \n",
      "\n",
      "(array([47, 18, 24, 16,  0, 12,  4, 45, 10, 13, 25, 44, 34, 11, 32, 41, 22,\n",
      "       43, 38, 33,  1, 23, 42, 30, 29, 27, 20,  7, 48, 21, 49, 36, 17, 14,\n",
      "        3, 46,  6]), array([26, 40, 19, 28, 37,  2, 35,  5,  8,  9, 31, 15, 39])) \n",
      "\n",
      "(array([29, 26, 38, 20, 17, 35, 42, 22, 12,  7, 47, 36, 24, 40, 31, 23, 27,\n",
      "       30, 16, 44, 37, 46, 39, 25,  1,  6,  5, 48, 19,  8, 10,  0, 14,  9,\n",
      "       43, 49, 41]), array([45, 28, 21, 15, 32,  4, 34, 33, 11,  3, 18,  2, 13])) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Generating ten folds of train_set in accordance with participant stratification (as opposed to 4 folds in the previous version of the same analysis)\n",
    "\n",
    "train_set_stratified_folds = custom_cross_validator(train_set, 10)\n",
    "\n",
    "for x in train_set_stratified_folds:\n",
    "    print(x,'\\n') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9e2f6451",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set 10-fold partioning:\n",
      "len(x[0]: 37, len(x[1]): 13\n"
     ]
    }
   ],
   "source": [
    "print(\"Training set 10-fold partioning:\")\n",
    "print(f\"len(x[0]: {len(x[0])}, len(x[1]): {len(x[1])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8fd7b7c",
   "metadata": {},
   "source": [
    "## Using Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8aef6ac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 20 µs, sys: 1e+03 ns, total: 21 µs\n",
      "Wall time: 23.1 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Sequential feature selection based on train_set\n",
    "\n",
    "model = LogisticRegression(max_iter=5000)\n",
    "model_name = 'LR'\n",
    "\n",
    "# Run sequential feature selector, which may take some time (Wall time: 8-15 min depending of HW)\n",
    "\n",
    "#results_LR = feature_combination_finder(questionnaire_combinations, train_set,train_set_stratified_folds, model, model_name)\n",
    "\n",
    "#results_LR.to_pickle('data/10fold_results_LR.pickle')\n",
    "\n",
    "# or read pre-stored results from this step\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "6e1bbe51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# or read pre-stored results from this step\n",
    "results_LR = pd.read_pickle('data/10fold_results_LR.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3c13f756",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Questionnaire(s)</th>\n",
       "      <th>Selected Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx</td>\n",
       "      <td>[HADS_q7_BL]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx</td>\n",
       "      <td>[HADS_q5_BL, HADS_q7_BL]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx</td>\n",
       "      <td>[HADS_q5_BL, HADS_q7_BL, HADS_q13_BL]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx</td>\n",
       "      <td>[HADS_q5_BL, HADS_q7_BL, HADS_q13_BL, Age]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx</td>\n",
       "      <td>[HADS_q1_BL, HADS_q5_BL, HADS_q7_BL, HADS_q13_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Dep_Bis_Fss</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Dep_Bis_Fss</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Dep_Bis_Fss</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Dep_Bis_Fss</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Dep_Bis_Fss</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Model Questionnaire(s)                                  Selected Features\n",
       "0      LR              Anx                                       [HADS_q7_BL]\n",
       "1      LR              Anx                           [HADS_q5_BL, HADS_q7_BL]\n",
       "2      LR              Anx              [HADS_q5_BL, HADS_q7_BL, HADS_q13_BL]\n",
       "3      LR              Anx         [HADS_q5_BL, HADS_q7_BL, HADS_q13_BL, Age]\n",
       "4      LR              Anx  [HADS_q1_BL, HADS_q5_BL, HADS_q7_BL, HADS_q13_...\n",
       "..    ...              ...                                                ...\n",
       "289    LR  Anx_Dep_Bis_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...\n",
       "290    LR  Anx_Dep_Bis_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...\n",
       "291    LR  Anx_Dep_Bis_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...\n",
       "292    LR  Anx_Dep_Bis_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...\n",
       "293    LR  Anx_Dep_Bis_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...\n",
       "\n",
       "[294 rows x 3 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_LR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50acbc6a",
   "metadata": {},
   "source": [
    "**Deleting instances with non representative features**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "551d8dec",
   "metadata": {},
   "source": [
    "###### \"Some of these feature combinations were not valid representation of the participating questionnaires as they did not included any features from each of the questionnaires in the group in some instances. For example, if a feature combination was selected from ANX+DEP+FSS questionnaire group but it only contained features related to anxiety due to relatively superior discriminability of ANX related features compared to DEP and FSS, it was excluded from further analysis on grounds of non-representativeness as it represented ANX questionnaire instead of the group of ANX+DEP+FSS questionnaires.\" Taken from manuscript "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "97bbf12c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41 is not representative \n",
      " Questionnaires: Anx_Dep \n",
      " Features: ['HADS_q8_BL']\n",
      "42 is not representative \n",
      " Questionnaires: Anx_Dep \n",
      " Features: ['HADS_q8_BL', 'HADS_q10_BL']\n",
      "43 is not representative \n",
      " Questionnaires: Anx_Dep \n",
      " Features: ['HADS_q8_BL', 'HADS_q10_BL', 'HADS_q14_BL']\n",
      "57 is not representative \n",
      " Questionnaires: Anx_Bis \n",
      " Features: ['HADS_q1_BL']\n",
      "72 is not representative \n",
      " Questionnaires: Anx_Fss \n",
      " Features: ['FSS_Q1_BL']\n",
      "73 is not representative \n",
      " Questionnaires: Anx_Fss \n",
      " Features: ['FSS_Q1_BL', 'FSS_Q6_BL']\n",
      "94 is not representative \n",
      " Questionnaires: Dep_Bis \n",
      " Features: ['BIS_Q5_BL']\n",
      "95 is not representative \n",
      " Questionnaires: Dep_Bis \n",
      " Features: ['BIS_Q1_BL', 'BIS_Q5_BL']\n",
      "109 is not representative \n",
      " Questionnaires: Dep_Fss \n",
      " Features: ['FSS_Q3_BL']\n",
      "131 is not representative \n",
      " Questionnaires: Bis_Fss \n",
      " Features: ['FSS_Q1_BL']\n",
      "132 is not representative \n",
      " Questionnaires: Bis_Fss \n",
      " Features: ['FSS_Q1_BL', 'FSS_Q6_BL']\n",
      "152 is not representative \n",
      " Questionnaires: Anx_Dep_Bis \n",
      " Features: ['HADS_q8_BL']\n",
      "153 is not representative \n",
      " Questionnaires: Anx_Dep_Bis \n",
      " Features: ['HADS_q8_BL', 'HADS_q10_BL']\n",
      "154 is not representative \n",
      " Questionnaires: Anx_Dep_Bis \n",
      " Features: ['HADS_q1_BL', 'HADS_q8_BL', 'HADS_q10_BL']\n",
      "174 is not representative \n",
      " Questionnaires: Anx_Dep_Fss \n",
      " Features: ['FSS_Q1_BL']\n",
      "175 is not representative \n",
      " Questionnaires: Anx_Dep_Fss \n",
      " Features: ['FSS_Q1_BL', 'FSS_Q6_BL']\n",
      "176 is not representative \n",
      " Questionnaires: Anx_Dep_Fss \n",
      " Features: ['HADS_q13_BL', 'FSS_Q1_BL', 'FSS_Q6_BL']\n",
      "177 is not representative \n",
      " Questionnaires: Anx_Dep_Fss \n",
      " Features: ['HADS_q13_BL', 'FSS_Q1_BL', 'FSS_Q6_BL', 'Gender_q']\n",
      "203 is not representative \n",
      " Questionnaires: Anx_Bis_Fss \n",
      " Features: ['FSS_Q3_BL']\n",
      "204 is not representative \n",
      " Questionnaires: Anx_Bis_Fss \n",
      " Features: ['FSS_Q3_BL', 'FSS_Q6_BL']\n",
      "205 is not representative \n",
      " Questionnaires: Anx_Bis_Fss \n",
      " Features: ['BIS_Q3_BL', 'FSS_Q3_BL', 'FSS_Q6_BL']\n",
      "231 is not representative \n",
      " Questionnaires: Dep_Bis_Fss \n",
      " Features: ['FSS_Q1_BL']\n",
      "232 is not representative \n",
      " Questionnaires: Dep_Bis_Fss \n",
      " Features: ['FSS_Q1_BL', 'FSS_Q6_BL']\n",
      "233 is not representative \n",
      " Questionnaires: Dep_Bis_Fss \n",
      " Features: ['BIS_Q3_BL', 'FSS_Q1_BL', 'FSS_Q6_BL']\n",
      "234 is not representative \n",
      " Questionnaires: Dep_Bis_Fss \n",
      " Features: ['BIS_Q1_BL', 'BIS_Q3_BL', 'FSS_Q1_BL', 'FSS_Q6_BL']\n",
      "259 is not representative \n",
      " Questionnaires: Anx_Dep_Bis_Fss \n",
      " Features: ['FSS_Q1_BL']\n",
      "260 is not representative \n",
      " Questionnaires: Anx_Dep_Bis_Fss \n",
      " Features: ['FSS_Q1_BL', 'FSS_Q6_BL']\n",
      "261 is not representative \n",
      " Questionnaires: Anx_Dep_Bis_Fss \n",
      " Features: ['BIS_Q3_BL', 'FSS_Q1_BL', 'FSS_Q6_BL']\n",
      "262 is not representative \n",
      " Questionnaires: Anx_Dep_Bis_Fss \n",
      " Features: ['HADS_q3_BL', 'BIS_Q3_BL', 'FSS_Q1_BL', 'FSS_Q6_BL']\n",
      "263 is not representative \n",
      " Questionnaires: Anx_Dep_Bis_Fss \n",
      " Features: ['HADS_q3_BL', 'HADS_q13_BL', 'BIS_Q3_BL', 'FSS_Q1_BL', 'FSS_Q6_BL']\n",
      "264 is not representative \n",
      " Questionnaires: Anx_Dep_Bis_Fss \n",
      " Features: ['HADS_q3_BL', 'HADS_q13_BL', 'BIS_Q3_BL', 'FSS_Q1_BL', 'FSS_Q6_BL', 'Gender_q']\n"
     ]
    }
   ],
   "source": [
    "non_representative_combinations = []\n",
    "\n",
    "for x in results_LR.index:\n",
    "\n",
    "    temp1 = []\n",
    "    temp2 = [questionnaire_combinations[y] for y in results_LR.loc[x, 'Questionnaire(s)'].split('_')]\n",
    "\n",
    "    features = results_LR.loc[x, 'Selected Features']\n",
    "\n",
    "    for z in temp2:\n",
    "\n",
    "        temp1.append([a for a in z if a in features])\n",
    "\n",
    "    if not all(temp1):\n",
    "\n",
    "        print('{} is not representative \\n Questionnaires: {} \\n Features: {}'.format(x,results_LR.loc[x, 'Questionnaire(s)'], features))\n",
    "    \n",
    "        non_representative_combinations.append(x)\n",
    "\n",
    "results_LR.drop(non_representative_combinations, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "eb06fb46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Questionnaire(s)</th>\n",
       "      <th>Selected Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx</td>\n",
       "      <td>[HADS_q7_BL]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx</td>\n",
       "      <td>[HADS_q5_BL, HADS_q7_BL]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx</td>\n",
       "      <td>[HADS_q5_BL, HADS_q7_BL, HADS_q13_BL]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx</td>\n",
       "      <td>[HADS_q5_BL, HADS_q7_BL, HADS_q13_BL, Age]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx</td>\n",
       "      <td>[HADS_q1_BL, HADS_q5_BL, HADS_q7_BL, HADS_q13_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Dep_Bis_Fss</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Dep_Bis_Fss</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Dep_Bis_Fss</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Dep_Bis_Fss</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Dep_Bis_Fss</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>263 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Model Questionnaire(s)                                  Selected Features\n",
       "0      LR              Anx                                       [HADS_q7_BL]\n",
       "1      LR              Anx                           [HADS_q5_BL, HADS_q7_BL]\n",
       "2      LR              Anx              [HADS_q5_BL, HADS_q7_BL, HADS_q13_BL]\n",
       "3      LR              Anx         [HADS_q5_BL, HADS_q7_BL, HADS_q13_BL, Age]\n",
       "4      LR              Anx  [HADS_q1_BL, HADS_q5_BL, HADS_q7_BL, HADS_q13_...\n",
       "..    ...              ...                                                ...\n",
       "289    LR  Anx_Dep_Bis_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...\n",
       "290    LR  Anx_Dep_Bis_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...\n",
       "291    LR  Anx_Dep_Bis_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...\n",
       "292    LR  Anx_Dep_Bis_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...\n",
       "293    LR  Anx_Dep_Bis_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...\n",
       "\n",
       "[263 rows x 3 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_LR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d768c5",
   "metadata": {},
   "source": [
    "**Calculating balanced accuracy cross validation score during training and balanced accuracy score on validation dataset.\n",
    "All operations use balanced accuracy to account for differences in number of participants in both HC and IBS classes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "29460644",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 14.5 s, sys: 42.5 ms, total: 14.6 s\n",
      "Wall time: 14.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model = LogisticRegression(max_iter=5000)\n",
    "\n",
    "train_valid_set_stratified_folds =  custom_cross_validator(train_valid_set,10)\n",
    "\n",
    "for x in results_LR.index:\n",
    "\n",
    "    PL = Pipeline([\n",
    "        ('Scaler', StandardScaler()),\n",
    "        ('Model', model)\n",
    "    ])\n",
    "\n",
    "    features = results_LR.loc[x,'Selected Features']\n",
    "\n",
    "    cv_score = np.mean(cross_val_score(PL, train_set[features], train_set['label'], cv= train_set_stratified_folds, scoring='balanced_accuracy'))\n",
    "    results_LR.loc[x,'train cv score'] = cv_score\n",
    "\n",
    "    PL.fit(train_set[features], train_set['label'])\n",
    "    results_LR.loc[x,'validation score'] = balanced_accuracy_score(valid_set['label'], PL.predict(valid_set[features]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "631af5c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Questionnaire(s)</th>\n",
       "      <th>Selected Features</th>\n",
       "      <th>train cv score</th>\n",
       "      <th>validation score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Fss</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...</td>\n",
       "      <td>0.72375</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Fss</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, FSS_Q1_BL...</td>\n",
       "      <td>0.82500</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Dep</td>\n",
       "      <td>[HADS_q13_BL, HADS_q2_BL, HADS_q8_BL, HADS_q10...</td>\n",
       "      <td>0.80375</td>\n",
       "      <td>0.828571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Dep</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q13_BL, HADS_q2_...</td>\n",
       "      <td>0.76125</td>\n",
       "      <td>0.828571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Dep_Bis_Fss</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...</td>\n",
       "      <td>0.77250</td>\n",
       "      <td>0.828571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>LR</td>\n",
       "      <td>Dep_Bis</td>\n",
       "      <td>[HADS_q8_BL, HADS_q10_BL, HADS_q12_BL, BIS_Q1_...</td>\n",
       "      <td>0.76500</td>\n",
       "      <td>0.564286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx_Fss</td>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, FSS_Q1_BL...</td>\n",
       "      <td>0.83125</td>\n",
       "      <td>0.564286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>LR</td>\n",
       "      <td>Dep_Bis</td>\n",
       "      <td>[HADS_q4_BL, HADS_q8_BL, HADS_q10_BL, HADS_q12...</td>\n",
       "      <td>0.75875</td>\n",
       "      <td>0.564286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx</td>\n",
       "      <td>[HADS_q5_BL, HADS_q7_BL]</td>\n",
       "      <td>0.74125</td>\n",
       "      <td>0.564286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>Anx</td>\n",
       "      <td>[HADS_q7_BL]</td>\n",
       "      <td>0.74125</td>\n",
       "      <td>0.564286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>263 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Model Questionnaire(s)                                  Selected Features  \\\n",
       "92     LR          Anx_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...   \n",
       "80     LR          Anx_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, FSS_Q1_BL...   \n",
       "46     LR          Anx_Dep  [HADS_q13_BL, HADS_q2_BL, HADS_q8_BL, HADS_q10...   \n",
       "50     LR          Anx_Dep  [HADS_q1_BL, HADS_q3_BL, HADS_q13_BL, HADS_q2_...   \n",
       "292    LR  Anx_Dep_Bis_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...   \n",
       "..    ...              ...                                                ...   \n",
       "102    LR          Dep_Bis  [HADS_q8_BL, HADS_q10_BL, HADS_q12_BL, BIS_Q1_...   \n",
       "78     LR          Anx_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, FSS_Q1_BL...   \n",
       "103    LR          Dep_Bis  [HADS_q4_BL, HADS_q8_BL, HADS_q10_BL, HADS_q12...   \n",
       "1      LR              Anx                           [HADS_q5_BL, HADS_q7_BL]   \n",
       "0      LR              Anx                                       [HADS_q7_BL]   \n",
       "\n",
       "     train cv score  validation score  \n",
       "92          0.72375          0.857143  \n",
       "80          0.82500          0.857143  \n",
       "46          0.80375          0.828571  \n",
       "50          0.76125          0.828571  \n",
       "292         0.77250          0.828571  \n",
       "..              ...               ...  \n",
       "102         0.76500          0.564286  \n",
       "78          0.83125          0.564286  \n",
       "103         0.75875          0.564286  \n",
       "1           0.74125          0.564286  \n",
       "0           0.74125          0.564286  \n",
       "\n",
       "[263 rows x 5 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_LR.sort_values(['validation score'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60b7eb49",
   "metadata": {},
   "source": [
    "**Selecting best model in each of 16 combinations of the questionnaires based on (i) test score on validation dataset and if this test score is equal for two different models then (ii) on basis of cross validation score during training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "033ac4fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model_in_category_LR = pd.DataFrame([], columns=['Best features', 'validation score'])\n",
    "\n",
    "for k in questionnaire_combinations.keys():\n",
    "\n",
    "    questionnaire_specific_results = results_LR[(results_LR['Questionnaire(s)']==k)]\n",
    "    questionnaire_specific_best_results = questionnaire_specific_results[(questionnaire_specific_results['validation score'] == questionnaire_specific_results['validation score'].max())]\n",
    "    questionnaire_specific_best_results_final_model = questionnaire_specific_best_results[(questionnaire_specific_best_results['train cv score'] == questionnaire_specific_best_results['train cv score'].max())]\n",
    "\n",
    "    best_model_in_category_LR.loc[k, 'Best features'] = questionnaire_specific_best_results_final_model.iloc[0]['Selected Features']\n",
    "    best_model_in_category_LR.loc[k, 'validation score'] = questionnaire_specific_best_results_final_model.iloc[0]['validation score']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb5cf10",
   "metadata": {},
   "source": [
    "**Best models in each combination of questionnaires in terms of validation score**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f1a7449c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Best features</th>\n",
       "      <th>validation score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Anx</th>\n",
       "      <td>[HADS_q5_BL, HADS_q7_BL, HADS_q13_BL]</td>\n",
       "      <td>0.707143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep</th>\n",
       "      <td>[HADS_q8_BL]</td>\n",
       "      <td>0.735714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bis</th>\n",
       "      <td>[BIS_Q2_BL, BIS_Q4_BL]</td>\n",
       "      <td>0.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fss</th>\n",
       "      <td>[FSS_Q1_BL, FSS_Q10_BL, FSS_Q11_BL, FSS_Q12_BL...</td>\n",
       "      <td>0.757143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anx_Dep</th>\n",
       "      <td>[HADS_q13_BL, HADS_q2_BL, HADS_q8_BL, HADS_q10...</td>\n",
       "      <td>0.828571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anx_Bis</th>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...</td>\n",
       "      <td>0.807143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anx_Fss</th>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, FSS_Q1_BL...</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Bis</th>\n",
       "      <td>[HADS_q10_BL, BIS_Q1_BL, BIS_Q5_BL]</td>\n",
       "      <td>0.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Fss</th>\n",
       "      <td>[HADS_q8_BL, HADS_q14_BL, FSS_Q3_BL, FSS_Q6_BL...</td>\n",
       "      <td>0.807143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bis_Fss</th>\n",
       "      <td>[BIS_Q3_BL, FSS_Q1_BL, FSS_Q6_BL]</td>\n",
       "      <td>0.757143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anx_Dep_Bis</th>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q13_BL, HADS_q2_...</td>\n",
       "      <td>0.807143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anx_Dep_Fss</th>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...</td>\n",
       "      <td>0.807143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anx_Bis_Fss</th>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...</td>\n",
       "      <td>0.807143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Bis_Fss</th>\n",
       "      <td>[HADS_q10_BL, BIS_Q1_BL, BIS_Q3_BL, FSS_Q1_BL,...</td>\n",
       "      <td>0.757143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anx_Dep_Bis_Fss</th>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q13_BL, HADS_q8_...</td>\n",
       "      <td>0.828571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     Best features  \\\n",
       "Anx                          [HADS_q5_BL, HADS_q7_BL, HADS_q13_BL]   \n",
       "Dep                                                   [HADS_q8_BL]   \n",
       "Bis                                         [BIS_Q2_BL, BIS_Q4_BL]   \n",
       "Fss              [FSS_Q1_BL, FSS_Q10_BL, FSS_Q11_BL, FSS_Q12_BL...   \n",
       "Anx_Dep          [HADS_q13_BL, HADS_q2_BL, HADS_q8_BL, HADS_q10...   \n",
       "Anx_Bis          [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...   \n",
       "Anx_Fss          [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, FSS_Q1_BL...   \n",
       "Dep_Bis                        [HADS_q10_BL, BIS_Q1_BL, BIS_Q5_BL]   \n",
       "Dep_Fss          [HADS_q8_BL, HADS_q14_BL, FSS_Q3_BL, FSS_Q6_BL...   \n",
       "Bis_Fss                          [BIS_Q3_BL, FSS_Q1_BL, FSS_Q6_BL]   \n",
       "Anx_Dep_Bis      [HADS_q1_BL, HADS_q3_BL, HADS_q13_BL, HADS_q2_...   \n",
       "Anx_Dep_Fss      [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...   \n",
       "Anx_Bis_Fss      [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...   \n",
       "Dep_Bis_Fss      [HADS_q10_BL, BIS_Q1_BL, BIS_Q3_BL, FSS_Q1_BL,...   \n",
       "Anx_Dep_Bis_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q13_BL, HADS_q8_...   \n",
       "\n",
       "                validation score  \n",
       "Anx                     0.707143  \n",
       "Dep                     0.735714  \n",
       "Bis                     0.714286  \n",
       "Fss                     0.757143  \n",
       "Anx_Dep                 0.828571  \n",
       "Anx_Bis                 0.807143  \n",
       "Anx_Fss                 0.857143  \n",
       "Dep_Bis                 0.714286  \n",
       "Dep_Fss                 0.807143  \n",
       "Bis_Fss                 0.757143  \n",
       "Anx_Dep_Bis             0.807143  \n",
       "Anx_Dep_Fss             0.807143  \n",
       "Anx_Bis_Fss             0.807143  \n",
       "Dep_Bis_Fss             0.757143  \n",
       "Anx_Dep_Bis_Fss         0.828571  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_in_category_LR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43bf8d2e",
   "metadata": {},
   "source": [
    "**Testing the best model in each combination of questionnaires on unseen data i.e. X_test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "aadc8bb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "supplementary_data_LR = pd.DataFrame([], columns= ['Classifier','X_train','y_train','X_test','y_test'])\n",
    "\n",
    "for b in best_model_in_category_LR.index:\n",
    "\n",
    "    final_features = best_model_in_category_LR.loc[b,'Best features']\n",
    "    \n",
    "    X_train = train_valid_set[final_features].to_numpy()\n",
    "    y_train = train_valid_set['label'].to_numpy()\n",
    "    X_test = test_set[final_features].to_numpy()\n",
    "    y_test = test_set['label'].to_numpy()\n",
    "\n",
    "    final_PL = Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('model', model)\n",
    "    ])\n",
    "\n",
    "    final_PL.fit(X_train,y_train)\n",
    "\n",
    "    ac = accuracy_score(y_test,final_PL.predict(X_test)) \n",
    "    bas = balanced_accuracy_score(y_test,final_PL.predict(X_test)) \n",
    "    rs = recall_score(y_test,final_PL.predict(X_test)) \n",
    "    ps = precision_score(y_test,final_PL.predict(X_test)) \n",
    "    f1s = f1_score(y_test,final_PL.predict(X_test)) \n",
    "    ras = roc_auc_score(y_test,final_PL.predict(X_test)) \n",
    "\n",
    "    best_model_in_category_LR.loc[b,'Accuracy score'] = ac\n",
    "    best_model_in_category_LR.loc[b,'Balanced accuracy score'] = bas\n",
    "    best_model_in_category_LR.loc[b,'Recall score'] = rs\n",
    "    best_model_in_category_LR.loc[b,'Precision score'] = ps\n",
    "    best_model_in_category_LR.loc[b,'F1 score'] = f1s\n",
    "    best_model_in_category_LR.loc[b,'Roc auc score'] = ras\n",
    "\n",
    "    supplementary_data_LR.loc[b] = [final_PL, X_train, y_train, X_test, y_test]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5335e051",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Best features</th>\n",
       "      <th>validation score</th>\n",
       "      <th>Accuracy score</th>\n",
       "      <th>Balanced accuracy score</th>\n",
       "      <th>Recall score</th>\n",
       "      <th>Precision score</th>\n",
       "      <th>F1 score</th>\n",
       "      <th>Roc auc score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Anx</th>\n",
       "      <td>[HADS_q5_BL, HADS_q7_BL, HADS_q13_BL]</td>\n",
       "      <td>0.707143</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.828571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep</th>\n",
       "      <td>[HADS_q8_BL]</td>\n",
       "      <td>0.735714</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.869565</td>\n",
       "      <td>0.785714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bis</th>\n",
       "      <td>[BIS_Q2_BL, BIS_Q4_BL]</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.664286</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.782609</td>\n",
       "      <td>0.664286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fss</th>\n",
       "      <td>[FSS_Q1_BL, FSS_Q10_BL, FSS_Q11_BL, FSS_Q12_BL...</td>\n",
       "      <td>0.757143</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.878571</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.878571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anx_Dep</th>\n",
       "      <td>[HADS_q13_BL, HADS_q2_BL, HADS_q8_BL, HADS_q10...</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.828571</td>\n",
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       "    <tr>\n",
       "      <th>Anx_Bis</th>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...</td>\n",
       "      <td>0.807143</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.757143</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.757143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anx_Fss</th>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, FSS_Q1_BL...</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Bis</th>\n",
       "      <td>[HADS_q10_BL, BIS_Q1_BL, BIS_Q5_BL]</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.614286</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.614286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Fss</th>\n",
       "      <td>[HADS_q8_BL, HADS_q14_BL, FSS_Q3_BL, FSS_Q6_BL...</td>\n",
       "      <td>0.807143</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.707143</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.707143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bis_Fss</th>\n",
       "      <td>[BIS_Q3_BL, FSS_Q1_BL, FSS_Q6_BL]</td>\n",
       "      <td>0.757143</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.807143</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.807143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anx_Dep_Bis</th>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q13_BL, HADS_q2_...</td>\n",
       "      <td>0.807143</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.757143</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.757143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anx_Dep_Fss</th>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...</td>\n",
       "      <td>0.807143</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anx_Bis_Fss</th>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...</td>\n",
       "      <td>0.807143</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.807143</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.807143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Bis_Fss</th>\n",
       "      <td>[HADS_q10_BL, BIS_Q1_BL, BIS_Q3_BL, FSS_Q1_BL,...</td>\n",
       "      <td>0.757143</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.807143</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.807143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anx_Dep_Bis_Fss</th>\n",
       "      <td>[HADS_q1_BL, HADS_q3_BL, HADS_q13_BL, HADS_q8_...</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.807143</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.807143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     Best features  \\\n",
       "Anx                          [HADS_q5_BL, HADS_q7_BL, HADS_q13_BL]   \n",
       "Dep                                                   [HADS_q8_BL]   \n",
       "Bis                                         [BIS_Q2_BL, BIS_Q4_BL]   \n",
       "Fss              [FSS_Q1_BL, FSS_Q10_BL, FSS_Q11_BL, FSS_Q12_BL...   \n",
       "Anx_Dep          [HADS_q13_BL, HADS_q2_BL, HADS_q8_BL, HADS_q10...   \n",
       "Anx_Bis          [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...   \n",
       "Anx_Fss          [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, FSS_Q1_BL...   \n",
       "Dep_Bis                        [HADS_q10_BL, BIS_Q1_BL, BIS_Q5_BL]   \n",
       "Dep_Fss          [HADS_q8_BL, HADS_q14_BL, FSS_Q3_BL, FSS_Q6_BL...   \n",
       "Bis_Fss                          [BIS_Q3_BL, FSS_Q1_BL, FSS_Q6_BL]   \n",
       "Anx_Dep_Bis      [HADS_q1_BL, HADS_q3_BL, HADS_q13_BL, HADS_q2_...   \n",
       "Anx_Dep_Fss      [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q9_B...   \n",
       "Anx_Bis_Fss      [HADS_q1_BL, HADS_q3_BL, HADS_q5_BL, HADS_q7_B...   \n",
       "Dep_Bis_Fss      [HADS_q10_BL, BIS_Q1_BL, BIS_Q3_BL, FSS_Q1_BL,...   \n",
       "Anx_Dep_Bis_Fss  [HADS_q1_BL, HADS_q3_BL, HADS_q13_BL, HADS_q8_...   \n",
       "\n",
       "                validation score  Accuracy score  Balanced accuracy score  \\\n",
       "Anx                     0.707143        0.823529                 0.828571   \n",
       "Dep                     0.735714        0.823529                 0.785714   \n",
       "Bis                     0.714286        0.705882                 0.664286   \n",
       "Fss                     0.757143        0.882353                 0.878571   \n",
       "Anx_Dep                 0.828571        0.823529                 0.828571   \n",
       "Anx_Bis                 0.807143        0.764706                 0.757143   \n",
       "Anx_Fss                 0.857143        0.941176                 0.928571   \n",
       "Dep_Bis                 0.714286        0.647059                 0.614286   \n",
       "Dep_Fss                 0.807143        0.705882                 0.707143   \n",
       "Bis_Fss                 0.757143        0.823529                 0.807143   \n",
       "Anx_Dep_Bis             0.807143        0.764706                 0.757143   \n",
       "Anx_Dep_Fss             0.807143        0.941176                 0.928571   \n",
       "Anx_Bis_Fss             0.807143        0.823529                 0.807143   \n",
       "Dep_Bis_Fss             0.757143        0.823529                 0.807143   \n",
       "Anx_Dep_Bis_Fss         0.828571        0.823529                 0.807143   \n",
       "\n",
       "                 Recall score  Precision score  F1 score  Roc auc score  \n",
       "Anx                       0.8         0.888889  0.842105       0.828571  \n",
       "Dep                       1.0         0.769231  0.869565       0.785714  \n",
       "Bis                       0.9         0.692308  0.782609       0.664286  \n",
       "Fss                       0.9         0.900000  0.900000       0.878571  \n",
       "Anx_Dep                   0.8         0.888889  0.842105       0.828571  \n",
       "Anx_Bis                   0.8         0.800000  0.800000       0.757143  \n",
       "Anx_Fss                   1.0         0.909091  0.952381       0.928571  \n",
       "Dep_Bis                   0.8         0.666667  0.727273       0.614286  \n",
       "Dep_Fss                   0.7         0.777778  0.736842       0.707143  \n",
       "Bis_Fss                   0.9         0.818182  0.857143       0.807143  \n",
       "Anx_Dep_Bis               0.8         0.800000  0.800000       0.757143  \n",
       "Anx_Dep_Fss               1.0         0.909091  0.952381       0.928571  \n",
       "Anx_Bis_Fss               0.9         0.818182  0.857143       0.807143  \n",
       "Dep_Bis_Fss               0.9         0.818182  0.857143       0.807143  \n",
       "Anx_Dep_Bis_Fss           0.9         0.818182  0.857143       0.807143  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_in_category_LR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9039d95d",
   "metadata": {},
   "source": [
    "**Best performance is achieved using ANX_FSS and ANX_DEP_FSS categories. We select ANX_FSS for obvious reason of minimal number of questionnaires needed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "64303205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HADS_q1_BL',\n",
       " 'HADS_q3_BL',\n",
       " 'HADS_q5_BL',\n",
       " 'FSS_Q1_BL',\n",
       " 'FSS_Q12_BL',\n",
       " 'FSS_Q5_BL',\n",
       " 'FSS_Q6_BL',\n",
       " 'FSS_Q8_BL',\n",
       " 'Gender_q']"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_in_category_LR.loc['Anx_Fss', 'Best features']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "56414c7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balanced test Accuracy: 92.86%\n",
      "Test Accuracy: 94.12%\n"
     ]
    }
   ],
   "source": [
    "# Plotting confusion matrix for the model achieved in ANX_FSS questionnaire\n",
    "\n",
    "index = 'Anx_Fss'\n",
    "features = best_model_in_category_LR.loc[index, 'Best features']\n",
    "model = supplementary_data_LR.loc[index, 'Classifier']\n",
    "X_train = supplementary_data_LR.loc[index, 'X_train']\n",
    "y_train = supplementary_data_LR.loc[index, 'y_train']\n",
    "X_test = supplementary_data_LR.loc[index, 'X_test']\n",
    "y_test = supplementary_data_LR.loc[index, 'y_test']\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "cm_labels=['HC','IBS']\n",
    "cm = metrics.confusion_matrix(y_test,model.predict(X_test))\n",
    "#cm_fig = annotated_confusion_matrix(cm, cm_labels)\n",
    "#cm_fig.show()\n",
    "\n",
    "\n",
    "print('Balanced test Accuracy: {:.02f}%'.format(balanced_accuracy_score(y_test,model.predict(X_test))*100))\n",
    "print('Test Accuracy: {:.02f}%'.format(model.score(X_test,y_test)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "3b99e0fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['True Neg','False Pos','False Neg','True Pos']\n",
    "categories = ['HC', 'IBS']\n",
    "make_confusion_matrix(cm, \n",
    "                      group_names=labels,\n",
    "                      categories=categories,\n",
    "                      sum_stats = False,\n",
    "                      cmap='binary')"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "87d2353f7118e6edc46981a12778b83b672e36fea913c9125f9bd438b73ddb0d"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}