Omdena_ASD_Predictor_MVP_template.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cdc8c0b6-5c3c-4393-ba22-546639182c77",
   "metadata": {
    "id": "cdc8c0b6-5c3c-4393-ba22-546639182c77"
   },
   "source": [
    "# 1 - IMPORTS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e891d36-08f1-40ce-bb79-d2d0dc12557e",
   "metadata": {
    "id": "3e891d36-08f1-40ce-bb79-d2d0dc12557e"
   },
   "source": [
    "## 1.1 - Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "809cad3b-c3fb-4147-b5dc-f3cd88735d4c",
   "metadata": {
    "id": "809cad3b-c3fb-4147-b5dc-f3cd88735d4c"
   },
   "source": [
    "*Load required libraries for the project*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "Oc8yj88gmkbU",
   "metadata": {
    "id": "Oc8yj88gmkbU"
   },
   "outputs": [],
   "source": [
    "!pip install --upgrade -q pylangacq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2hctJgPnrG4x",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2hctJgPnrG4x",
    "outputId": "9ca26400-caea-436a-a3a2-bd3d816ab5a0",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting catboost\n",
      "  Downloading catboost-1.1.1-cp39-none-win_amd64.whl (74.0 MB)\n",
      "Requirement already satisfied: shap in c:\\users\\admin\\anaconda3\\lib\\site-packages (0.41.0)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\admin\\anaconda3\\lib\\site-packages (from catboost) (3.4.3)\n",
      "Requirement already satisfied: six in c:\\users\\admin\\anaconda3\\lib\\site-packages (from catboost) (1.16.0)\n",
      "Requirement already satisfied: scipy in c:\\users\\admin\\anaconda3\\lib\\site-packages (from catboost) (1.7.1)\n",
      "Collecting graphviz\n",
      "  Downloading graphviz-0.20.1-py3-none-any.whl (47 kB)\n",
      "Requirement already satisfied: numpy>=1.16.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from catboost) (1.20.3)\n",
      "Requirement already satisfied: pandas>=0.24.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from catboost) (1.3.4)\n",
      "Requirement already satisfied: plotly in c:\\users\\admin\\anaconda3\\lib\\site-packages (from catboost) (5.9.0)\n",
      "Requirement already satisfied: slicer==0.0.7 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from shap) (0.0.7)\n",
      "Requirement already satisfied: numba in c:\\users\\admin\\anaconda3\\lib\\site-packages (from shap) (0.54.1)\n",
      "Requirement already satisfied: packaging>20.9 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from shap) (21.0)\n",
      "Requirement already satisfied: cloudpickle in c:\\users\\admin\\anaconda3\\lib\\site-packages (from shap) (2.0.0)\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\admin\\anaconda3\\lib\\site-packages (from shap) (1.1.2)\n",
      "Requirement already satisfied: tqdm>4.25.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from shap) (4.62.3)\n",
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      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from pandas>=0.24.0->catboost) (2021.3)\n",
      "Requirement already satisfied: colorama in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tqdm>4.25.0->shap) (0.4.4)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (1.3.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (8.4.0)\n",
      "Requirement already satisfied: setuptools in c:\\users\\admin\\anaconda3\\lib\\site-packages (from numba->shap) (67.1.0)\n",
      "Requirement already satisfied: llvmlite<0.38,>=0.37.0rc1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from numba->shap) (0.37.0)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from plotly->catboost) (8.0.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from scikit-learn->shap) (2.2.0)\n",
      "Requirement already satisfied: joblib>=1.0.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from scikit-learn->shap) (1.1.0)\n",
      "Installing collected packages: graphviz, catboost\n",
      "Successfully installed catboost-1.1.1 graphviz-0.20.1\n"
     ]
    }
   ],
   "source": [
    "!pip install catboost shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "538bd70b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ydata-profiling in c:\\users\\admin\\anaconda3\\lib\\site-packages (0.0.dev0)\n",
      "Collecting ydata-profiling\n",
      "  Using cached ydata_profiling-4.0.0-py2.py3-none-any.whl (344 kB)\n",
      "Requirement already satisfied: tqdm<4.65,>=4.48.2 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from ydata-profiling) (4.62.3)\n",
      "Requirement already satisfied: matplotlib<3.7,>=3.2 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from ydata-profiling) (3.4.3)\n",
      "Requirement already satisfied: pandas!=1.4.0,<1.6,>1.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from ydata-profiling) (1.5.3)\n",
      "Requirement already satisfied: requests<2.29,>=2.24.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from ydata-profiling) (2.26.0)\n",
      "Requirement already satisfied: multimethod<1.10,>=1.4 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from ydata-profiling) (1.9.1)\n",
      "Requirement already satisfied: seaborn<0.13,>=0.10.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from ydata-profiling) (0.11.2)\n",
      "Requirement already satisfied: statsmodels<0.14,>=0.13.2 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from ydata-profiling) (0.13.5)\n",
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      "Requirement already satisfied: networkx>=2.4 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from visions[type_image_path]==0.7.5->ydata-profiling) (2.6.3)\n",
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      "Requirement already satisfied: six in c:\\users\\admin\\anaconda3\\lib\\site-packages (from cycler>=0.10->matplotlib<3.7,>=3.2->ydata-profiling) (1.16.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from pandas!=1.4.0,<1.6,>1.1->ydata-profiling) (2021.3)\n",
      "Requirement already satisfied: joblib>=0.14.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from phik<0.13,>=0.11.1->ydata-profiling) (1.1.0)\n",
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      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from requests<2.29,>=2.24.0->ydata-profiling) (1.26.7)\n",
      "Requirement already satisfied: patsy>=0.5.2 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from statsmodels<0.14,>=0.13.2->ydata-profiling) (0.5.2)\n",
      "Requirement already satisfied: packaging>=21.3 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from statsmodels<0.14,>=0.13.2->ydata-profiling) (23.0)\n",
      "Requirement already satisfied: colorama in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tqdm<4.65,>=4.48.2->ydata-profiling) (0.4.4)\n",
      "Requirement already satisfied: PyWavelets in c:\\users\\admin\\anaconda3\\lib\\site-packages (from imagehash->visions[type_image_path]==0.7.5->ydata-profiling) (1.1.1)\n",
      "Installing collected packages: ydata-profiling\n",
      "  Attempting uninstall: ydata-profiling\n",
      "    Found existing installation: ydata-profiling 0.0.dev0\n",
      "    Uninstalling ydata-profiling-0.0.dev0:\n",
      "      Successfully uninstalled ydata-profiling-0.0.dev0\n",
      "Successfully installed ydata-profiling-4.0.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install -U ydata-profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be8995cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "conda install -c conda-forge ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6578c55-3ccb-43b5-b874-b73e6fb8410a",
   "metadata": {
    "id": "d6578c55-3ccb-43b5-b874-b73e6fb8410a"
   },
   "outputs": [],
   "source": [
    "# OS\n",
    "import os\n",
    "from   pathlib import Path\n",
    "\n",
    "# import required libraries\n",
    "#from google.colab import files\n",
    "\n",
    "# date\n",
    "from datetime import datetime\n",
    "\n",
    "# data manipulation\n",
    "import re\n",
    "import numpy      as     np\n",
    "import pandas     as     pd\n",
    "\n",
    "# plot and images\n",
    "import seaborn              as sns\n",
    "import matplotlib.pyplot    as plt\n",
    "\n",
    "# NLP \n",
    "import pylangacq\n",
    "# from nltk.stem import WordNetLemmatizer\n",
    "# from nltk import pos_tag\n",
    "\n",
    "# Generating Synthetic data\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN, SMOTENC\n",
    "from imblearn.under_sampling import RandomUnderSampler, NearMiss, TomekLinks\n",
    "\n",
    "# modelling\n",
    "from sklearn.model_selection import train_test_split, LeavePOut, StratifiedKFold, learning_curve, cross_validate\n",
    "from sklearn.dummy           import DummyClassifier\n",
    "from sklearn.linear_model    import LogisticRegression\n",
    "from sklearn.preprocessing   import StandardScaler\n",
    "from sklearn.metrics         import log_loss, f1_score, accuracy_score, precision_score, recall_score, roc_auc_score, confusion_matrix, ConfusionMatrixDisplay, precision_recall_curve, auc, log_loss\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import BernoulliNB, ComplementNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#from lightgbm import LGBMClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "# explainability\n",
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "qZDmSDsWyQsR",
   "metadata": {
    "id": "qZDmSDsWyQsR"
   },
   "outputs": [],
   "source": [
    "# # download required corpus, POS, etc\n",
    "# import nltk\n",
    "# nltk.download('wordnet')\n",
    "# nltk.download('omw-1.4')\n",
    "# nltk.download('averaged_perceptron_tagger')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b188f9ed-2942-4e91-9acc-6d2c668d44cf",
   "metadata": {
    "id": "b188f9ed-2942-4e91-9acc-6d2c668d44cf"
   },
   "source": [
    "## 1.2 - Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd53f9fe-60be-4604-a0af-d42a533e8b35",
   "metadata": {
    "id": "cd53f9fe-60be-4604-a0af-d42a533e8b35"
   },
   "source": [
    "*Define functions that will be used on the project*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0f8b2efe-780d-4c96-925f-5410f338549b",
   "metadata": {
    "id": "0f8b2efe-780d-4c96-925f-5410f338549b"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9fdafd-e7f0-410a-b9f6-85bf1c5ab688",
   "metadata": {
    "id": "3b9fdafd-e7f0-410a-b9f6-85bf1c5ab688"
   },
   "source": [
    "## 1.3 - Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c756b15-17ad-498a-b843-004ce876d4a5",
   "metadata": {
    "id": "9c756b15-17ad-498a-b843-004ce876d4a5"
   },
   "source": [
    "*Set some configurations for the whole project*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e81fca56-315b-487a-8c9e-1e5758a095c6",
   "metadata": {
    "id": "e81fca56-315b-487a-8c9e-1e5758a095c6"
   },
   "outputs": [],
   "source": [
    "# e.g.     \n",
    "# set cientific notation for pandas\n",
    "pd.set_option(\n",
    "    \"display.float_format\", \"{:,.3f}\".format \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "807d9dc9-6720-49e5-899e-73f883c290e0",
   "metadata": {
    "id": "807d9dc9-6720-49e5-899e-73f883c290e0"
   },
   "source": [
    "## 1.4 - Constants "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc4e1368-9560-49c3-a955-f7e6efa9aad9",
   "metadata": {
    "id": "bc4e1368-9560-49c3-a955-f7e6efa9aad9"
   },
   "source": [
    "*Define constants that will be used throughout the project*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d8b6e6b2-6724-4cd8-b043-e6ae04461a3d",
   "metadata": {
    "id": "d8b6e6b2-6724-4cd8-b043-e6ae04461a3d"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ad7cd30-a440-4fb4-98a5-aacd5d2654db",
   "metadata": {
    "id": "9ad7cd30-a440-4fb4-98a5-aacd5d2654db"
   },
   "outputs": [],
   "source": [
    "# define seed and random states\n",
    "seed = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b984e706-7376-42fe-97a6-4f068f547f1d",
   "metadata": {
    "id": "b984e706-7376-42fe-97a6-4f068f547f1d"
   },
   "source": [
    "# 2 - DATA EXTRACTION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "009ee3a8-0861-4d32-a3d0-752594227611",
   "metadata": {
    "id": "009ee3a8-0861-4d32-a3d0-752594227611"
   },
   "source": [
    "## 2.1 - Data dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6623340-078b-46f2-83c3-2abbb1e370b6",
   "metadata": {
    "id": "f6623340-078b-46f2-83c3-2abbb1e370b6"
   },
   "source": [
    "*Describe the meaning of every column of the dataset*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "xPeXrmXJrmmf",
   "metadata": {
    "id": "xPeXrmXJrmmf"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf1e1984-ea0e-4ecf-87d2-68756c055a9e",
   "metadata": {
    "id": "cf1e1984-ea0e-4ecf-87d2-68756c055a9e"
   },
   "source": [
    "## 2.2 - Data Loading"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "241024c3-8e04-4958-bc71-aeb5b164bf72",
   "metadata": {
    "id": "241024c3-8e04-4958-bc71-aeb5b164bf72"
   },
   "source": [
    "*Load data from required files*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "SBRVW-QxrqqF",
   "metadata": {
    "id": "SBRVW-QxrqqF"
   },
   "outputs": [],
   "source": [
    "# # upload data\n",
    "# uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "I-ttrZoEr-Zb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I-ttrZoEr-Zb",
    "outputId": "0cf52ebc-936e-49f7-d4fc-f0d14445196e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['.ipynb_checkpoints', '.log', 'Available Sheets.png', 'Baseline_Dataset_EDA.ipynb', 'Datasets', 'data_analytics_model_on_behavioural_challenges_of_asd_kids.csv', 'data_csv.csv', 'Nadig.xlsx', 'Nadig_EDA.html', 'Omdena_ASD_Predictor_MVP_template - Copy.ipynb', 'Omdena_ASD_Predictor_MVP_template.ipynb', 'Research', 'Teamate', 'Toddler Autism dataset July 2018.csv', 'Toddler data description.docx', 'Toddler Proportion Comparison across Datasets.ipynb', 'your_report.html', 'Zoom Recordings']\n"
     ]
    }
   ],
   "source": [
    "# list files in colab folder\n",
    "print(os.listdir(\".\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6bGRmZA7m264",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6bGRmZA7m264",
    "outputId": "bd4d9352-547c-4705-f7a0-3c50f4e1512e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nadig = pylangacq.read_chat( \"Datasets/NLP/textual/Nadig.zip\" )\n",
    "nadig.n_files() # how many chat files are present for this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "afeaa0c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38 files\n",
      "9453 utterances\n",
      "46271 words\n",
      "       Utterance Count    Word Count  File Path\n",
      "---  -----------------  ------------  -------------\n",
      "#1                 292          1679  Nadig\\101.cha\n",
      "#2                 312          1512  Nadig\\102.cha\n",
      "#3                 230          1305  Nadig\\103.cha\n",
      "#4                 245          1076  Nadig\\104.cha\n",
      "#5                 180          1022  Nadig\\108.cha\n",
      "#6                 242          1462  Nadig\\109.cha\n",
      "#7                 231          1198  Nadig\\110.cha\n",
      "#8                 167          1143  Nadig\\111.cha\n",
      "#9                 197           857  Nadig\\115.cha\n",
      "#10                337          1941  Nadig\\117.cha\n",
      "#11                186           900  Nadig\\119.cha\n",
      "#12                173           670  Nadig\\120.cha\n",
      "#13                304          1879  Nadig\\121.cha\n",
      "#14                237          1397  Nadig\\122.cha\n",
      "#15                323          2010  Nadig\\123.cha\n",
      "#16                284          1207  Nadig\\124.cha\n",
      "#17                402          1318  Nadig\\125.cha\n",
      "#18                323          1229  Nadig\\126.cha\n",
      "#19                184           813  Nadig\\127.cha\n",
      "#20                331          1219  Nadig\\128.cha\n",
      "#21                275          1478  Nadig\\132.cha\n",
      "#22                231          1034  Nadig\\133.cha\n",
      "#23                353          1550  Nadig\\134.cha\n",
      "#24                245          1350  Nadig\\135.cha\n",
      "#25                202          1163  Nadig\\138.cha\n",
      "#26                253          1395  Nadig\\139.cha\n",
      "#27                 54           276  Nadig\\140.cha\n",
      "#28                267          1313  Nadig\\141.cha\n",
      "#29                314          1405  Nadig\\143.cha\n",
      "#30                172           841  Nadig\\145.cha\n",
      "#31                345          1652  Nadig\\149.cha\n",
      "#32                311          1224  Nadig\\157.cha\n",
      "#33                163           666  Nadig\\159.cha\n",
      "#34                152           747  Nadig\\161.cha\n",
      "#35                213           778  Nadig\\163.cha\n",
      "#36                290          1298  Nadig\\165.cha\n",
      "#37                179           936  Nadig\\167.cha\n",
      "#38                254          1328  Nadig\\169.cha\n"
     ]
    }
   ],
   "source": [
    "# Gettting the high level information of the .cha files present in zip file\n",
    "nadig.info(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "90409ca4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[42.166666666666664,\n",
       " 50.13333333333333,\n",
       " 31.7,\n",
       " 43.06666666666667,\n",
       " 78.9,\n",
       " 37.0,\n",
       " 73.0,\n",
       " 34.43333333333333,\n",
       " 52.7,\n",
       " 37.36666666666667,\n",
       " 32.0,\n",
       " 42.833333333333336,\n",
       " 32.53333333333333,\n",
       " 58.56666666666667,\n",
       " 34.2,\n",
       " 61.7,\n",
       " 23.633333333333333,\n",
       " 64.83333333333333,\n",
       " 37.166666666666664,\n",
       " 52.43333333333333,\n",
       " 75.33333333333333,\n",
       " 23.166666666666668,\n",
       " 63.233333333333334,\n",
       " 31.666666666666668,\n",
       " 65.5,\n",
       " 32.266666666666666,\n",
       " 58.93333333333333,\n",
       " 31.9,\n",
       " 28.8,\n",
       " 26.166666666666668,\n",
       " 27.4,\n",
       " 26.966666666666665,\n",
       " 26.1,\n",
       " 22.933333333333334,\n",
       " 20.966666666666665,\n",
       " 20.466666666666665,\n",
       " 43.53333333333333,\n",
       " 42.96666666666667]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nadig.ages(months=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d48d80c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[43, 51, 32, 44, 79, 37, 73, 35, 53, 38, 32, 43, 33, 59, 35, 62, 24, 65, 38, 53, 76, 24, 64, 32, 66, 33, 59, 32, 29, 27, 28, 27, 27, 23, 21, 21, 44, 43]\n"
     ]
    }
   ],
   "source": [
    "# Get the toddlers age from the given files\n",
    "# First it is extracted in months which is floating and then converted to integer\n",
    "\n",
    "import math\n",
    "\n",
    "child_age_float = nadig.ages(months=True)\n",
    "\n",
    "child_age = [math.ceil(num) for num in child_age_float]\n",
    "\n",
    "print(child_age) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "z_hb2JNXsf3z",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "z_hb2JNXsf3z",
    "outputId": "d4bd13d0-6fce-4ac1-c2ce-eb44330aa4ad",
    "require": [
     "base/js/events",
     "datatables.net",
     "d3",
     "chartjs",
     "dt-config",
     "dt-components",
     "dt-graph-objects",
     "dt-toolbar",
     "dt-tooltips",
     "jupyter-datatables"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ASD',\n",
       " 'age_in_months',\n",
       " 'child_mlu',\n",
       " 'percent_child_parents_mlu',\n",
       " 'child_mluw',\n",
       " 'percent_child_parents_mluw',\n",
       " 'child_ttr',\n",
       " 'percent_child_parents_ttr',\n",
       " 'child_ipsyn',\n",
       " 'percent_child_parents_ipsyn',\n",
       " 'child_num_words',\n",
       " 'perct_child_parents_words',\n",
       " 'child_num_unique_words',\n",
       " 'perct_child_parents_unique_words',\n",
       " 'child_num_tokens',\n",
       " 'perct_child_parents_tokens',\n",
       " 'child_utterances',\n",
       " 'perct_child_parents_utterances',\n",
       " 'pos_percent_co',\n",
       " 'pos_percent_.',\n",
       " 'pos_percent_None',\n",
       " 'pos_percent_n',\n",
       " 'pos_percent_v',\n",
       " 'pos_percent_!',\n",
       " 'pos_percent_pro:sub',\n",
       " 'pos_percent_adv',\n",
       " 'pos_percent_neg',\n",
       " 'pos_percent_qn',\n",
       " 'pos_percent_?',\n",
       " 'pos_percent_prep',\n",
       " 'pos_percent_mod',\n",
       " 'pos_percent_pro:per',\n",
       " 'pos_percent_pro:dem',\n",
       " 'pos_percent_adj',\n",
       " 'pos_percent_pro:rel',\n",
       " 'pos_percent_det:art',\n",
       " 'pos_percent_pro:int',\n",
       " 'pos_percent_aux',\n",
       " 'pos_percent_cop',\n",
       " 'pos_percent_+...',\n",
       " 'pos_percent_inf',\n",
       " 'pos_percent_conj',\n",
       " 'pos_percent_coord',\n",
       " 'pos_percent_+!?',\n",
       " 'pos_percent_post',\n",
       " 'pos_percent_pro:indef',\n",
       " 'pos_percent_part',\n",
       " 'pos_percent_det:poss',\n",
       " 'pos_percent_pro:obj',\n",
       " 'pos_percent_cm',\n",
       " 'pos_percent_det:num',\n",
       " 'pos_percent_adv:tem',\n",
       " 'pos_percent_+/.',\n",
       " 'pos_percent_det:dem',\n",
       " 'pos_percent_pro:exist',\n",
       " 'pos_percent_comp',\n",
       " 'pos_percent_n:prop',\n",
       " 'pos_percent_mid#n',\n",
       " 'pos_percent_on',\n",
       " 'pos_percent_re#v',\n",
       " 'pos_percent_n:gerund',\n",
       " 'pos_percent_sub#n',\n",
       " 'pos_percent_n:pt',\n",
       " 'pos_percent_pro:refl',\n",
       " 'pos_percent_un#part',\n",
       " 'pos_percent_L2',\n",
       " 'pos_percent_chi',\n",
       " 'pos_percent_pro:poss',\n",
       " 'pos_percent_n:let',\n",
       " 'pos_percent_bab',\n",
       " 'pos_percent_sing']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>ASD</th>\n",
       "      <th>age_in_months</th>\n",
       "      <th>child_mlu</th>\n",
       "      <th>percent_child_parents_mlu</th>\n",
       "      <th>child_mluw</th>\n",
       "      <th>percent_child_parents_mluw</th>\n",
       "      <th>child_ttr</th>\n",
       "      <th>percent_child_parents_ttr</th>\n",
       "      <th>child_ipsyn</th>\n",
       "      <th>percent_child_parents_ipsyn</th>\n",
       "      <th>...</th>\n",
       "      <th>pos_percent_sub#n</th>\n",
       "      <th>pos_percent_n:pt</th>\n",
       "      <th>pos_percent_pro:refl</th>\n",
       "      <th>pos_percent_un#part</th>\n",
       "      <th>pos_percent_L2</th>\n",
       "      <th>pos_percent_chi</th>\n",
       "      <th>pos_percent_pro:poss</th>\n",
       "      <th>pos_percent_n:let</th>\n",
       "      <th>pos_percent_bab</th>\n",
       "      <th>pos_percent_sing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>TYP</td>\n",
       "      <td>28</td>\n",
       "      <td>1.952</td>\n",
       "      <td>0.341</td>\n",
       "      <td>1.382</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.438</td>\n",
       "      <td>1.827</td>\n",
       "      <td>42</td>\n",
       "      <td>0.494</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.042</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>0</th>\n",
       "      <td>TYP</td>\n",
       "      <td>43</td>\n",
       "      <td>2.641</td>\n",
       "      <td>0.398</td>\n",
       "      <td>2.604</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.558</td>\n",
       "      <td>2.215</td>\n",
       "      <td>61</td>\n",
       "      <td>0.701</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ASD</td>\n",
       "      <td>43</td>\n",
       "      <td>2.568</td>\n",
       "      <td>0.651</td>\n",
       "      <td>1.816</td>\n",
       "      <td>0.626</td>\n",
       "      <td>0.435</td>\n",
       "      <td>1.465</td>\n",
       "      <td>20</td>\n",
       "      <td>0.328</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009</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>37</th>\n",
       "      <td>TYP</td>\n",
       "      <td>43</td>\n",
       "      <td>3.742</td>\n",
       "      <td>0.648</td>\n",
       "      <td>2.982</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.494</td>\n",
       "      <td>1.639</td>\n",
       "      <td>55</td>\n",
       "      <td>0.733</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ASD</td>\n",
       "      <td>73</td>\n",
       "      <td>3.625</td>\n",
       "      <td>0.639</td>\n",
       "      <td>3.410</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.526</td>\n",
       "      <td>2.072</td>\n",
       "      <td>53</td>\n",
       "      <td>0.654</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>TYP</td>\n",
       "      <td>21</td>\n",
       "      <td>1.096</td>\n",
       "      <td>0.224</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.733</td>\n",
       "      <td>2.632</td>\n",
       "      <td>4</td>\n",
       "      <td>0.059</td>\n",
       "      <td>...</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>0.075</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ASD</td>\n",
       "      <td>65</td>\n",
       "      <td>1.389</td>\n",
       "      <td>0.317</td>\n",
       "      <td>1.125</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.254</td>\n",
       "      <td>1.144</td>\n",
       "      <td>15</td>\n",
       "      <td>0.197</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ASD  age_in_months  child_mlu  percent_child_parents_mlu  child_mluw  \\\n",
       "30  TYP             28      1.952                      0.341       1.382   \n",
       "0   TYP             43      2.641                      0.398       2.604   \n",
       "11  ASD             43      2.568                      0.651       1.816   \n",
       "37  TYP             43      3.742                      0.648       2.982   \n",
       "6   ASD             73      3.625                      0.639       3.410   \n",
       "35  TYP             21      1.096                      0.224       1.000   \n",
       "17  ASD             65      1.389                      0.317       1.125   \n",
       "\n",
       "    percent_child_parents_mluw  child_ttr  percent_child_parents_ttr  \\\n",
       "30                       0.302      0.438                      1.827   \n",
       "0                        0.472      0.558                      2.215   \n",
       "11                       0.626      0.435                      1.465   \n",
       "37                       0.679      0.494                      1.639   \n",
       "6                        0.793      0.526                      2.072   \n",
       "35                       0.249      0.733                      2.632   \n",
       "17                       0.320      0.254                      1.144   \n",
       "\n",
       "    child_ipsyn  percent_child_parents_ipsyn  ...  pos_percent_sub#n  \\\n",
       "30           42                        0.494  ...                NaN   \n",
       "0            61                        0.701  ...                NaN   \n",
       "11           20                        0.328  ...                NaN   \n",
       "37           55                        0.733  ...                NaN   \n",
       "6            53                        0.654  ...                NaN   \n",
       "35            4                        0.059  ...                NaN   \n",
       "17           15                        0.197  ...                NaN   \n",
       "\n",
       "    pos_percent_n:pt  pos_percent_pro:refl  pos_percent_un#part  \\\n",
       "30               NaN                   NaN                  NaN   \n",
       "0                NaN                   NaN                  NaN   \n",
       "11               NaN                   NaN                  NaN   \n",
       "37               NaN                   NaN                  NaN   \n",
       "6                NaN                   NaN                  NaN   \n",
       "35               NaN                   NaN                  NaN   \n",
       "17               NaN                   NaN                  NaN   \n",
       "\n",
       "    pos_percent_L2  pos_percent_chi  pos_percent_pro:poss  pos_percent_n:let  \\\n",
       "30           0.042              NaN                   NaN                NaN   \n",
       "0              NaN              NaN                   NaN                NaN   \n",
       "11           0.009              NaN                   NaN                NaN   \n",
       "37             NaN              NaN                   NaN                NaN   \n",
       "6              NaN              NaN                   NaN                NaN   \n",
       "35             NaN              NaN                   NaN                NaN   \n",
       "17             NaN              NaN                   NaN                NaN   \n",
       "\n",
       "    pos_percent_bab  pos_percent_sing  \n",
       "30              NaN               NaN  \n",
       "0               NaN               NaN  \n",
       "11              NaN               NaN  \n",
       "37              NaN               NaN  \n",
       "6               NaN               NaN  \n",
       "35            0.075               NaN  \n",
       "17              NaN               NaN  \n",
       "\n",
       "[7 rows x 71 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create an empty dataframe \n",
    "df_extraction = pd.DataFrame()\n",
    "\n",
    "# iterate over transcripts\n",
    "for nadig_i in nadig:\n",
    "    # create a dictionary for extracted features\n",
    "    features_dict = {}\n",
    "    \n",
    "    ####### add values to dataframe #######\n",
    "    # add ASD label\n",
    "    features_dict[\"ASD\"] = nadig_i.headers()[0][\"Participants\"][\"CHI\"][\"group\"]\n",
    "    \n",
    "    # get age of the child in months\n",
    "    features_dict[\"age_in_months\"] = math.ceil(nadig_i.ages(months=True)[0])\n",
    "    \n",
    "    # get non-child participants tags\n",
    "    non_child_participants = list(nadig_i.headers()[0][\"Participants\"].keys())\n",
    "    non_child_participants.remove('CHI')\n",
    "\n",
    "    # get mlu\n",
    "    child_mlu = nadig_i.mlu(participant='CHI')[0]    \n",
    "    # get mean mlu for non-child participants\n",
    "    mean_mlu_non_child = np.mean([nadig_i.mlu(participant=non_child)[0] \n",
    "                                  for non_child \n",
    "                                  in non_child_participants])\n",
    "    # add child mlu\n",
    "    features_dict[\"child_mlu\"] = child_mlu\n",
    "    # get percentage of child mlu / non-child mean mlu\n",
    "    features_dict[\"percent_child_parents_mlu\"] = child_mlu / mean_mlu_non_child\n",
    "\n",
    "    # get mluw\n",
    "    child_mluw = nadig_i.mluw(participant='CHI')[0]\n",
    "    # get mean mluw for non-child participants\n",
    "    mean_mluw_non_child = np.mean([nadig_i.mluw(participant=non_child)[0] \n",
    "                                   for non_child \n",
    "                                   in non_child_participants])\n",
    "    # add child mluw\n",
    "    features_dict[\"child_mluw\"] = child_mluw\n",
    "    # get percentage of child mluw / non-child mean mluw\n",
    "    features_dict[\"percent_child_parents_mluw\"] = child_mluw / mean_mluw_non_child\n",
    "\n",
    "    # get ttr\n",
    "    child_ttr = nadig_i.ttr(participant='CHI')[0]\n",
    "    # get mean ttr for non-child participants\n",
    "    mean_ttr_non_child = np.mean([nadig_i.ttr(participant=non_child)[0] \n",
    "                                  for non_child \n",
    "                                  in non_child_participants])\n",
    "    # add child ttr\n",
    "    features_dict[\"child_ttr\"] = child_ttr\n",
    "    # get percentage of child ttr / non-child mean ttr\n",
    "    features_dict[\"percent_child_parents_ttr\"] = child_ttr / mean_ttr_non_child\n",
    "\n",
    "    # get ipsyn\n",
    "    child_ipsyn = nadig_i.ipsyn(participant='CHI')[0]\n",
    "    # get mean ipsyn for non-child participants\n",
    "    mean_ipsyn_non_child = np.mean([nadig_i.ipsyn(participant=non_child)[0] \n",
    "                                    for non_child \n",
    "                                    in non_child_participants])\n",
    "    # add child ipsyn\n",
    "    features_dict[\"child_ipsyn\"] = child_ipsyn\n",
    "    # get percentage of child ipsyn / non-child mean ipsyn\n",
    "    features_dict[\"percent_child_parents_ipsyn\"] = child_ipsyn / mean_ipsyn_non_child\n",
    "\n",
    "    # get number of words\n",
    "    features_dict[\"child_num_words\"] = len(nadig_i.words(participants=\"CHI\"))\n",
    "    # get percentage of child words / parent words\n",
    "    features_dict[\"perct_child_parents_words\"] = len(nadig_i.words(participants=\"CHI\")) / len(nadig_i.words(exclude=(\"CHI\")))\n",
    "    # get number of unique words\n",
    "    features_dict[\"child_num_unique_words\"] = len(set(nadig_i.words(participants=\"CHI\")))\n",
    "    # get percentage of child unique words / parent unique words\n",
    "    features_dict[\"perct_child_parents_unique_words\"] = len(set(nadig_i.words(participants=\"CHI\"))) / len(set(nadig_i.words(exclude=(\"CHI\"))))\n",
    "    # get number of tokens\n",
    "    features_dict[\"child_num_tokens\"] = len(nadig_i.tokens(participants=\"CHI\"))\n",
    "    # get percentage of child tokens / parent tokens\n",
    "    features_dict[\"perct_child_parents_tokens\"] = len(nadig_i.tokens(participants=\"CHI\")) / len(nadig_i.tokens(exclude=(\"CHI\")))\n",
    "    # get number of utterances\n",
    "    features_dict[\"child_utterances\"] = len(nadig_i.utterances(participants=\"CHI\"))\n",
    "    # get percentage of child utterances / parent utterances\n",
    "    features_dict[\"perct_child_parents_utterances\"] = len(nadig_i.utterances(participants=\"CHI\")) / len(nadig_i.utterances(exclude=\"CHI\"))\n",
    "\n",
    "    # get part of speech (POS) for every token in child utterances\n",
    "    child_pos = [token.pos for token in nadig_i.tokens(participants=\"CHI\")]   \n",
    "    # get number of tokens\n",
    "    num_tokens = features_dict[\"child_num_tokens\"]\n",
    "    # sanity check\n",
    "    assert num_tokens == len(child_pos), \"Missing POS tags!\"\n",
    "    \n",
    "    # get values counts for pos\n",
    "    pos_value_counts = pd.Series(child_pos).value_counts(dropna=False)\n",
    "    # convert to dict\n",
    "    pos_value_counts_dict = pos_value_counts.to_dict()\n",
    "    # add pos_ prefix before pos tags\n",
    "    pos_value_counts_dict = { (\"pos_percent_\" + str(k)): (v / num_tokens) for k, v in pos_value_counts_dict.items() }\n",
    "    # sanity check \n",
    "    assert abs(1 - sum([*pos_value_counts_dict.values()])) < 0.01, \"Missing POS tags!\"\n",
    "    \n",
    "    # add pos_value_counts_dict to features_dict\n",
    "    features_dict = {**features_dict, **pos_value_counts_dict}\n",
    "    # create a pandas dataframe from features dict\n",
    "    df_features = pd.DataFrame(data=features_dict, index=[0])\n",
    "    # concatenate features dataframe with extraction dataframe\n",
    "    df_extraction = pd.concat(objs=[df_extraction, df_features], ignore_index=True)\n",
    "\n",
    "# inspect dataframe\n",
    "display(df_extraction.columns.tolist(), df_extraction.sample(7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "xNOezzdZp9uh",
   "metadata": {
    "id": "xNOezzdZp9uh"
   },
   "outputs": [],
   "source": [
    "# print(\n",
    "#     len(nadig.mlu(participant='CHI')),\n",
    "#     len(nadig.mluw(participant='CHI'))\n",
    "#         )\n",
    "\n",
    "# # tagged_words -> Deprecated since version 0.13.0: Please use tokens() with by_utterances=False instead.\n",
    "# # tokens(participants=None, exclude=None, by_utterances=False, by_files=False) → Union[List[pylangacq.objects.Token], List[List[pylangacq.objects.Token]], List[List[List[pylangacq.objects.Token]]]]\n",
    "# # ttr(keep_case=True, participant='CHI') → List[float\n",
    "# # utterances(participants=None, exclude=None, by_files=False) → Union[List[pylangacq.objects.Utterance], List[List[pylangacq.objects.Utterance]]]\n",
    "# # ipsyn(participant='CHI') → List[int][source] -> Return the indexes of productive syntax (IPSyn)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "Tljql138n0GK",
   "metadata": {
    "id": "Tljql138n0GK"
   },
   "outputs": [],
   "source": [
    "# nadig_utterance = nadig.utterances(by_files = True) # get all the utterrance of the chat files of Nadig chat data\n",
    "# print(len(nadig_utterance))\n",
    "# nadig_utterance[0][0] # describe the first utterrance in the first conversation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "VHLi_J15n0xe",
   "metadata": {
    "id": "VHLi_J15n0xe"
   },
   "outputs": [],
   "source": [
    "# first_utter = nadig_utterance[0][0]\n",
    "# participant = first_utter.participant #Get participant name of the utterrance\n",
    "# list_of_tokens = first_utter.tokens #Get the tokens of the utterrance\n",
    "\n",
    "# print(f'Participant: {participant}')\n",
    "# print(f'Tokens: ')\n",
    "# #Let's anaylyse each token\n",
    "# for i, token in enumerate(list_of_tokens):\n",
    "#   print(f'token {i+1}: {token}')\n",
    "#   token_word = token.word #extract the word of token\n",
    "#   print(f'\\t token word: {token_word}')\n",
    "#   token_pos = token.pos #get the parts of speech of the token\n",
    "#   print(f'\\t token pos: {token_pos}')\n",
    "#   token_mor = token.mor #get the morphological information of the token\n",
    "#   print(f'\\t token mor: {token_mor}')\n",
    "#   token_gra = token.gra #get the grammatical info. of the token\n",
    "#   print(f'\\t token gra: {token_gra}: {token_gra.dep} | {token_gra.head} | {token_gra.rel}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "12xSBuhfn1FO",
   "metadata": {
    "id": "12xSBuhfn1FO"
   },
   "outputs": [],
   "source": [
    "# tier = first_utter.tiers #tier is a dictionary that contains the utterrance, %mor and %gra info in a summarised form\n",
    "# print(tier)\n",
    "# utter_sentence = tier[participant] #get the dialogue sentence\n",
    "# print(f'sentence: {utter_sentence}')\n",
    "# morphological_info = tier['%mor'] #get the morphological info of all the words\n",
    "# print(f'%mor: {morphological_info}')\n",
    "# grammatical_info = tier['%gra'] #get the grammatical relation info of all the words\n",
    "# print(f'%gra: {grammatical_info}')\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7f9e9015-9810-47b0-8187-e9cff9885b22",
   "metadata": {
    "id": "7f9e9015-9810-47b0-8187-e9cff9885b22"
   },
   "outputs": [],
   "source": [
    "# # instanciate an empty dataframe\n",
    "# df_extraction = pd.DataFrame(columns=[\"participant\", \"sentence\", \"csv\"])\n",
    "\n",
    "# # iterate over files and get names from csv files\n",
    "# csv_files = [file for file in os.listdir(\".\") if file.endswith(\".csv\")]\n",
    "\n",
    "# # iterate over csv names\n",
    "# for csv in csv_files:\n",
    "#     # read csv data\n",
    "#     df_ = pd.read_csv(csv)\n",
    "#     # create a new row to indicate source origin\n",
    "#     df_[\"csv\"] = int(csv[:-4])\n",
    "#     # concatenate csv data to main dataframe    \n",
    "#     df_extraction = pd.concat(objs=[df_, df_extraction])\n",
    "\n",
    "# # print unique participants and a sample of dataframe\n",
    "# print(f'Unique participants: {df_extraction[\"participant\"].unique()}')\n",
    "# display(df_extraction.sample(10, random_state=7))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11a6ad33-826c-4fab-bf1a-c78507176f69",
   "metadata": {
    "id": "11a6ad33-826c-4fab-bf1a-c78507176f69"
   },
   "source": [
    "# 3 - DATA DESCRIPTION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31c82291-5ce8-47fe-bce7-4b422750f8df",
   "metadata": {
    "id": "31c82291-5ce8-47fe-bce7-4b422750f8df"
   },
   "source": [
    "## 3.1 - Restore Point"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6003f2c1-705b-4be0-b623-d6cad51de9b9",
   "metadata": {
    "id": "6003f2c1-705b-4be0-b623-d6cad51de9b9"
   },
   "source": [
    "*Create a checkpoint of the last dataframe from previous section*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "892a84ee-b475-4942-81ad-fdd2c0bc4c83",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 237
    },
    "id": "892a84ee-b475-4942-81ad-fdd2c0bc4c83",
    "outputId": "4a063ad1-c95d-4795-b520-d1b56cd243f0",
    "require": [
     "base/js/events",
     "datatables.net",
     "d3",
     "chartjs",
     "dt-config",
     "dt-components",
     "dt-graph-objects",
     "dt-toolbar",
     "dt-tooltips",
     "jupyter-datatables"
    ]
   },
   "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>ASD</th>\n",
       "      <th>age_in_months</th>\n",
       "      <th>child_mlu</th>\n",
       "      <th>percent_child_parents_mlu</th>\n",
       "      <th>child_mluw</th>\n",
       "      <th>percent_child_parents_mluw</th>\n",
       "      <th>child_ttr</th>\n",
       "      <th>percent_child_parents_ttr</th>\n",
       "      <th>child_ipsyn</th>\n",
       "      <th>percent_child_parents_ipsyn</th>\n",
       "      <th>...</th>\n",
       "      <th>pos_percent_sub#n</th>\n",
       "      <th>pos_percent_n:pt</th>\n",
       "      <th>pos_percent_pro:refl</th>\n",
       "      <th>pos_percent_un#part</th>\n",
       "      <th>pos_percent_L2</th>\n",
       "      <th>pos_percent_chi</th>\n",
       "      <th>pos_percent_pro:poss</th>\n",
       "      <th>pos_percent_n:let</th>\n",
       "      <th>pos_percent_bab</th>\n",
       "      <th>pos_percent_sing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TYP</td>\n",
       "      <td>37</td>\n",
       "      <td>4.358</td>\n",
       "      <td>0.634</td>\n",
       "      <td>3.672</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.443</td>\n",
       "      <td>1.496</td>\n",
       "      <td>77</td>\n",
       "      <td>0.885</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>TYP</td>\n",
       "      <td>24</td>\n",
       "      <td>1.051</td>\n",
       "      <td>0.218</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.255</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.445</td>\n",
       "      <td>3</td>\n",
       "      <td>0.043</td>\n",
       "      <td>...</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>0.024</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ASD</td>\n",
       "      <td>64</td>\n",
       "      <td>1.444</td>\n",
       "      <td>0.269</td>\n",
       "      <td>1.344</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.512</td>\n",
       "      <td>3.007</td>\n",
       "      <td>37</td>\n",
       "      <td>0.597</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ASD  age_in_months  child_mlu  percent_child_parents_mlu  child_mluw  \\\n",
       "5   TYP             37      4.358                      0.634       3.672   \n",
       "21  TYP             24      1.051                      0.218       1.000   \n",
       "22  ASD             64      1.444                      0.269       1.344   \n",
       "\n",
       "    percent_child_parents_mluw  child_ttr  percent_child_parents_ttr  \\\n",
       "5                        0.665      0.443                      1.496   \n",
       "21                       0.255      1.000                      4.445   \n",
       "22                       0.324      0.512                      3.007   \n",
       "\n",
       "    child_ipsyn  percent_child_parents_ipsyn  ...  pos_percent_sub#n  \\\n",
       "5            77                        0.885  ...                NaN   \n",
       "21            3                        0.043  ...                NaN   \n",
       "22           37                        0.597  ...                NaN   \n",
       "\n",
       "    pos_percent_n:pt  pos_percent_pro:refl  pos_percent_un#part  \\\n",
       "5                NaN                   NaN                  NaN   \n",
       "21               NaN                   NaN                  NaN   \n",
       "22               NaN                   NaN                  NaN   \n",
       "\n",
       "    pos_percent_L2  pos_percent_chi  pos_percent_pro:poss  pos_percent_n:let  \\\n",
       "5              NaN              NaN                   NaN                NaN   \n",
       "21             NaN              NaN                   NaN                NaN   \n",
       "22             NaN              NaN                   NaN                NaN   \n",
       "\n",
       "    pos_percent_bab  pos_percent_sing  \n",
       "5               NaN               NaN  \n",
       "21            0.024               NaN  \n",
       "22              NaN               NaN  \n",
       "\n",
       "[3 rows x 71 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a restore point for the previous section dataframe\n",
    "df_description = df_extraction.copy()\n",
    "\n",
    "# check dataframe\n",
    "df_description.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7b73eaf-1647-43bd-8029-139cbd8e1b94",
   "metadata": {
    "id": "c7b73eaf-1647-43bd-8029-139cbd8e1b94"
   },
   "source": [
    "## 3.2 - Column Names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b7d6f7-ad5b-4fb7-9bf7-c9f651144d4a",
   "metadata": {
    "id": "76b7d6f7-ad5b-4fb7-9bf7-c9f651144d4a"
   },
   "source": [
    "*Search for misleading or error-prone column names*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8951f194-ced8-40b5-979a-24a1aec9bc78",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8951f194-ced8-40b5-979a-24a1aec9bc78",
    "outputId": "14bd3ec3-9be5-44b5-efa8-53b63ea22933"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ASD', 'age_in_months', 'child_mlu', 'percent_child_parents_mlu',\n",
       "       'child_mluw', 'percent_child_parents_mluw', 'child_ttr',\n",
       "       'percent_child_parents_ttr', 'child_ipsyn',\n",
       "       'percent_child_parents_ipsyn', 'child_num_words',\n",
       "       'perct_child_parents_words', 'child_num_unique_words',\n",
       "       'perct_child_parents_unique_words', 'child_num_tokens',\n",
       "       'perct_child_parents_tokens', 'child_utterances',\n",
       "       'perct_child_parents_utterances', 'pos_percent_co', 'pos_percent_.',\n",
       "       'pos_percent_None', 'pos_percent_n', 'pos_percent_v', 'pos_percent_!',\n",
       "       'pos_percent_pro:sub', 'pos_percent_adv', 'pos_percent_neg',\n",
       "       'pos_percent_qn', 'pos_percent_?', 'pos_percent_prep',\n",
       "       'pos_percent_mod', 'pos_percent_pro:per', 'pos_percent_pro:dem',\n",
       "       'pos_percent_adj', 'pos_percent_pro:rel', 'pos_percent_det:art',\n",
       "       'pos_percent_pro:int', 'pos_percent_aux', 'pos_percent_cop',\n",
       "       'pos_percent_+...', 'pos_percent_inf', 'pos_percent_conj',\n",
       "       'pos_percent_coord', 'pos_percent_+!?', 'pos_percent_post',\n",
       "       'pos_percent_pro:indef', 'pos_percent_part', 'pos_percent_det:poss',\n",
       "       'pos_percent_pro:obj', 'pos_percent_cm', 'pos_percent_det:num',\n",
       "       'pos_percent_adv:tem', 'pos_percent_+/.', 'pos_percent_det:dem',\n",
       "       'pos_percent_pro:exist', 'pos_percent_comp', 'pos_percent_n:prop',\n",
       "       'pos_percent_mid#n', 'pos_percent_on', 'pos_percent_re#v',\n",
       "       'pos_percent_n:gerund', 'pos_percent_sub#n', 'pos_percent_n:pt',\n",
       "       'pos_percent_pro:refl', 'pos_percent_un#part', 'pos_percent_L2',\n",
       "       'pos_percent_chi', 'pos_percent_pro:poss', 'pos_percent_n:let',\n",
       "       'pos_percent_bab', 'pos_percent_sing'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check column names\n",
    "df_description.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82df00c8-b83a-450e-944d-baa18b27e0fc",
   "metadata": {
    "id": "82df00c8-b83a-450e-944d-baa18b27e0fc"
   },
   "source": [
    "## 3.3 - Data Types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeff2471-88d2-4636-9d52-b59ea55116ca",
   "metadata": {
    "id": "eeff2471-88d2-4636-9d52-b59ea55116ca"
   },
   "source": [
    "*Check if data types on dataframe makes sense according to database information*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bb804129-bef8-4b98-9e02-4416e95aee68",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bb804129-bef8-4b98-9e02-4416e95aee68",
    "outputId": "095e7d5d-5a95-4147-836a-eaef721c3069"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ASD                           object\n",
       "age_in_months                  int64\n",
       "child_mlu                    float64\n",
       "percent_child_parents_mlu    float64\n",
       "child_mluw                   float64\n",
       "                              ...   \n",
       "pos_percent_chi              float64\n",
       "pos_percent_pro:poss         float64\n",
       "pos_percent_n:let            float64\n",
       "pos_percent_bab              float64\n",
       "pos_percent_sing             float64\n",
       "Length: 71, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data types\n",
    "df_description.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40c474b2-9df6-456c-95ff-93ced9481a76",
   "metadata": {
    "id": "40c474b2-9df6-456c-95ff-93ced9481a76"
   },
   "source": [
    "## 3.4 - Check Duplicated Rows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0da0878-91db-4e34-b8f9-a5aafb4959a3",
   "metadata": {
    "id": "e0da0878-91db-4e34-b8f9-a5aafb4959a3"
   },
   "source": [
    "*Inspect duplicated rows (based on dataframe granularity) and handle them properly*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4c8f21f9-1fc6-49ca-bf64-cf54412cc732",
   "metadata": {
    "id": "4c8f21f9-1fc6-49ca-bf64-cf54412cc732"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2cf33dc-1446-4285-ba7e-1ddf2ceb9adb",
   "metadata": {
    "id": "b2cf33dc-1446-4285-ba7e-1ddf2ceb9adb"
   },
   "source": [
    "## 3.5 - Check Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623c4ef2-ee8c-4d0c-aada-bb1ee973c7e0",
   "metadata": {
    "id": "623c4ef2-ee8c-4d0c-aada-bb1ee973c7e0"
   },
   "source": [
    "*Inspect number and percentage of missing value per column to decide what to do with them*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aec810a2-633c-43a4-bdc8-b036692f1f08",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aec810a2-633c-43a4-bdc8-b036692f1f08",
    "outputId": "305df7f6-a446-48b3-a562-206e02b288b3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ASD                           0\n",
       "age_in_months                 0\n",
       "child_mlu                     0\n",
       "percent_child_parents_mlu     0\n",
       "child_mluw                    0\n",
       "                             ..\n",
       "pos_percent_chi              33\n",
       "pos_percent_pro:poss         34\n",
       "pos_percent_n:let            37\n",
       "pos_percent_bab              36\n",
       "pos_percent_sing             37\n",
       "Length: 71, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check NAs\n",
    "df_description.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af603ed1-d856-4203-b7cb-de98af5a662d",
   "metadata": {
    "id": "af603ed1-d856-4203-b7cb-de98af5a662d"
   },
   "source": [
    "## 3.6 - Handle Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca853543-99b5-45ba-a4b0-3af8b543d5aa",
   "metadata": {
    "id": "ca853543-99b5-45ba-a4b0-3af8b543d5aa"
   },
   "source": [
    "*Handle missing value for columns*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d48c56fc-d84b-4ffd-b695-f2fdf233221b",
   "metadata": {
    "id": "d48c56fc-d84b-4ffd-b695-f2fdf233221b"
   },
   "outputs": [],
   "source": [
    "# fill missing values with 0\n",
    "df_description = df_description.fillna(value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "xPcCc_THz6gG",
   "metadata": {
    "id": "xPcCc_THz6gG"
   },
   "outputs": [],
   "source": [
    "# sanity check\n",
    "assert df_description.isna().sum().sum() == 0, \"There are missing values!\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e61914a1-a4f6-4a06-b585-c55fe879c8f8",
   "metadata": {
    "id": "e61914a1-a4f6-4a06-b585-c55fe879c8f8"
   },
   "source": [
    "# 4 - FEATURE ENGINEERING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2b77ee0-c217-4d6f-971c-bd962df74a93",
   "metadata": {
    "id": "a2b77ee0-c217-4d6f-971c-bd962df74a93"
   },
   "source": [
    "## 4.1 - Restore Point"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b023802f-2c08-4e91-b36a-f3b98219c3bc",
   "metadata": {
    "id": "b023802f-2c08-4e91-b36a-f3b98219c3bc"
   },
   "source": [
    "*Create a checkpoint of the last dataframe from previous section*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0cc624c9-4d7e-4ac6-b272-06fbab9f51c2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 237
    },
    "id": "0cc624c9-4d7e-4ac6-b272-06fbab9f51c2",
    "outputId": "03a7a92d-77e2-44cb-de07-e091aaf044ee",
    "require": [
     "base/js/events",
     "datatables.net",
     "d3",
     "chartjs",
     "dt-config",
     "dt-components",
     "dt-graph-objects",
     "dt-toolbar",
     "dt-tooltips",
     "jupyter-datatables"
    ]
   },
   "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>ASD</th>\n",
       "      <th>age_in_months</th>\n",
       "      <th>child_mlu</th>\n",
       "      <th>percent_child_parents_mlu</th>\n",
       "      <th>child_mluw</th>\n",
       "      <th>percent_child_parents_mluw</th>\n",
       "      <th>child_ttr</th>\n",
       "      <th>percent_child_parents_ttr</th>\n",
       "      <th>child_ipsyn</th>\n",
       "      <th>percent_child_parents_ipsyn</th>\n",
       "      <th>...</th>\n",
       "      <th>pos_percent_sub#n</th>\n",
       "      <th>pos_percent_n:pt</th>\n",
       "      <th>pos_percent_pro:refl</th>\n",
       "      <th>pos_percent_un#part</th>\n",
       "      <th>pos_percent_L2</th>\n",
       "      <th>pos_percent_chi</th>\n",
       "      <th>pos_percent_pro:poss</th>\n",
       "      <th>pos_percent_n:let</th>\n",
       "      <th>pos_percent_bab</th>\n",
       "      <th>pos_percent_sing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TYP</td>\n",
       "      <td>24</td>\n",
       "      <td>1.333</td>\n",
       "      <td>0.317</td>\n",
       "      <td>1.074</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.397</td>\n",
       "      <td>1.487</td>\n",
       "      <td>10</td>\n",
       "      <td>0.145</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>TYP</td>\n",
       "      <td>23</td>\n",
       "      <td>1.083</td>\n",
       "      <td>0.215</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.253</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.801</td>\n",
       "      <td>1</td>\n",
       "      <td>0.014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TYP</td>\n",
       "      <td>53</td>\n",
       "      <td>3.341</td>\n",
       "      <td>0.745</td>\n",
       "      <td>3.183</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.518</td>\n",
       "      <td>1.161</td>\n",
       "      <td>56</td>\n",
       "      <td>0.767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ASD  age_in_months  child_mlu  percent_child_parents_mlu  child_mluw  \\\n",
       "16  TYP             24      1.333                      0.317       1.074   \n",
       "33  TYP             23      1.083                      0.215       1.000   \n",
       "8   TYP             53      3.341                      0.745       3.183   \n",
       "\n",
       "    percent_child_parents_mluw  child_ttr  percent_child_parents_ttr  \\\n",
       "16                       0.331      0.397                      1.487   \n",
       "33                       0.253      1.000                      3.801   \n",
       "8                        0.873      0.518                      1.161   \n",
       "\n",
       "    child_ipsyn  percent_child_parents_ipsyn  ...  pos_percent_sub#n  \\\n",
       "16           10                        0.145  ...              0.000   \n",
       "33            1                        0.014  ...              0.000   \n",
       "8            56                        0.767  ...              0.000   \n",
       "\n",
       "    pos_percent_n:pt  pos_percent_pro:refl  pos_percent_un#part  \\\n",
       "16             0.000                 0.000                0.000   \n",
       "33             0.000                 0.000                0.000   \n",
       "8              0.007                 0.003                0.003   \n",
       "\n",
       "    pos_percent_L2  pos_percent_chi  pos_percent_pro:poss  pos_percent_n:let  \\\n",
       "16           0.000            0.005                 0.000              0.000   \n",
       "33           0.000            0.000                 0.000              0.000   \n",
       "8            0.000            0.000                 0.000              0.000   \n",
       "\n",
       "    pos_percent_bab  pos_percent_sing  \n",
       "16            0.000             0.000  \n",
       "33            0.000             0.000  \n",
       "8             0.000             0.000  \n",
       "\n",
       "[3 rows x 71 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a restore point for the previous section dataframe\n",
    "df_f_eng = df_description.copy()\n",
    "\n",
    "# check dataframe\n",
    "df_f_eng.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11793090-dc5c-4992-ae69-f6e9b97b6af7",
   "metadata": {
    "id": "11793090-dc5c-4992-ae69-f6e9b97b6af7"
   },
   "source": [
    "## 4.2 - Feature Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad73620a-74e0-4aa7-863e-7cdf0151b868",
   "metadata": {
    "id": "ad73620a-74e0-4aa7-863e-7cdf0151b868"
   },
   "source": [
    "*Create new features (columns) that can be meaningful for EDA and, especially, machine learning modelling.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "CCMjorEistuK",
   "metadata": {
    "id": "CCMjorEistuK"
   },
   "outputs": [],
   "source": [
    "# # create ASD label\n",
    "# # odd csv = TYP\n",
    "# # even csv = ASD\n",
    "# df_f_eng[\"label\"] = df_f_eng[\"csv\"].apply(lambda x: \"ASD\" if (x % 2 == 0) else \"TYP\")\n",
    "\n",
    "# # check operation\n",
    "# df_f_eng.sample(5, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6203bca9-c78d-4341-a2ca-83982f2a05e0",
   "metadata": {
    "id": "6203bca9-c78d-4341-a2ca-83982f2a05e0"
   },
   "outputs": [],
   "source": [
    "# # split sentence into [\".\", \"!\", \"?\", \",\"]\n",
    "# df_f_eng[\"clean_sentence\"] = df_f_eng[\"sentence\"].apply(\n",
    "#     lambda sentence: re.split(f\"[,.!? ]+\", sentence)\n",
    "#     )\n",
    "# # inspect transformation\n",
    "# display('split sentence into [\".\", \"!\", \"?\", \",\"]', df_f_eng[\"clean_sentence\"].head(3))\n",
    "# print(\"\\n\\n\")\n",
    "\n",
    "# # remove any spaces with regex\n",
    "# df_f_eng[\"clean_sentence\"] = df_f_eng[\"clean_sentence\"].apply(\n",
    "#     lambda sentence_list: [re.sub(\"\\s+\", \"\", item) for item in sentence_list] \n",
    "#     )\n",
    "# # inspect transformation\n",
    "# display('remove any spaces with regex', df_f_eng[\"clean_sentence\"].head(3))\n",
    "# print(\"\\n\\n\")\n",
    "\n",
    "# # lower the case of the words in a text\n",
    "# df_f_eng[\"clean_sentence\"] = df_f_eng[\"clean_sentence\"].apply(\n",
    "#     lambda sentence_list: [item.lower() for item in sentence_list] \n",
    "#     )\n",
    "# # inspect transformation\n",
    "# display('lower the case of the words in a text', df_f_eng[\"clean_sentence\"].head(3))\n",
    "# print(\"\\n\\n\")\n",
    "\n",
    "# # remove empty tokens\n",
    "# df_f_eng[\"clean_sentence\"] = df_f_eng[\"clean_sentence\"].apply(\n",
    "#     lambda sentence_list: [item for item in sentence_list if item != \"\"] \n",
    "#     )\n",
    "# # inspect transformation\n",
    "# display('remove empty tokens', df_f_eng[\"clean_sentence\"].head(3))\n",
    "# print(\"\\n\\n\")\n",
    "\n",
    "# # instanciate lemmatizer\n",
    "# lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "# # lemmatize tokens\n",
    "# df_f_eng[\"clean_sentence\"] = df_f_eng[\"clean_sentence\"].apply(\n",
    "#     lambda sentence_list: [lemmatizer.lemmatize(item) for item in sentence_list] \n",
    "#     )\n",
    "# # inspect transformation\n",
    "# display('lemmatize tokens', df_f_eng[\"clean_sentence\"].head(3))\n",
    "# print(\"\\n\\n\")\n",
    "\n",
    "# # NLTK part of speech\n",
    "# # https://www.nltk.org/book/ch05.html\n",
    "# # https://stackoverflow.com/questions/15388831/what-are-all-possible-pos-tags-of-nltk\n",
    "# # ADJ \tadjective \tnew, good, high, special, big, local\n",
    "# # ADP \tadposition \ton, of, at, with, by, into, under\n",
    "# # ADV \tadverb \treally, already, still, early, now\n",
    "# # CONJ \tconjunction \tand, or, but, if, while, although\n",
    "# # DET \tdeterminer, article \tthe, a, some, most, every, no, which\n",
    "# # NOUN \tnoun \tyear, home, costs, time, Africa\n",
    "# # NUM \tnumeral \ttwenty-four, fourth, 1991, 14:24\n",
    "# # PRT \tparticle \tat, on, out, over per, that, up, with\n",
    "# # PRON \tpronoun \the, their, her, its, my, I, us\n",
    "# # VERB \tverb \tis, say, told, given, playing, would\n",
    "# # . \tpunctuation marks \t. , ; !\n",
    "# # X \tother \tersatz, esprit, dunno, gr8, univeristy\n",
    "\n",
    "# # define POS tags\n",
    "# pos_tags = [\n",
    "#     \"JJ\", # adj\n",
    "#     \"NN\", # noun\n",
    "#     \"RB\", # adverb\n",
    "#     \"VB\" # verbs\n",
    "# ]\n",
    "\n",
    "# # iterate over tags\n",
    "# for idx, tag in enumerate(pos_tags):\n",
    "\n",
    "#     # check apply POS to each word (word, POS)\n",
    "#     # and return the POS per row\n",
    "#     df_f_eng[f\"pos_tag_{tag}\"] = df_f_eng[\"clean_sentence\"].apply( \n",
    "#         lambda sentence_list: [ word for word, pos in pos_tag(sentence_list) if pos == tag]\n",
    "#     )\n",
    "    \n",
    "#     # inspect the first transformation\n",
    "#     if idx == 0: \n",
    "#         display('apply POS tags', df_f_eng[f\"pos_tag_{tag}\"].head(3))\n",
    "#         print(\"\\n\\n\")\n",
    "\n",
    "#     # get total number of tags\n",
    "#     df_f_eng[f\"num_pos_tag_{tag}\"] = df_f_eng[f\"pos_tag_{tag}\"].apply(lambda tags_list: len(tags_list))\n",
    "#     # inspect the first transformation\n",
    "#     if idx == 0: \n",
    "#         display('count number of POS tags', df_f_eng[f\"pos_tag_{tag}\"].head(3))\n",
    "#         print(\"\\n\\n\")\n",
    "\n",
    "# # inspect results\n",
    "# print(\"*\"*49)\n",
    "# df_f_eng.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "LplmeFPA6lul",
   "metadata": {
    "id": "LplmeFPA6lul"
   },
   "outputs": [],
   "source": [
    "# # get number of tokens\n",
    "# df_f_eng[\"num_tokens\"] = df_f_eng[\"clean_sentence\"].apply(lambda sentence_list: len(sentence_list))\n",
    "\n",
    "# # get number of chars\n",
    "# df_f_eng[\"num_chars\"] = df_f_eng[\"clean_sentence\"].apply(lambda sentence_list: len(\"\".join(sentence_list)))\n",
    "\n",
    "# # inspect results\n",
    "# df_f_eng.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "BS9UnfSu77hz",
   "metadata": {
    "id": "BS9UnfSu77hz"
   },
   "outputs": [],
   "source": [
    "# # create a empty dataframe to hold modelling data\n",
    "# df_modelling = pd.DataFrame()\n",
    "\n",
    "# # define features to model\n",
    "# features = [\"num_pos_tag_JJ\", \"num_pos_tag_NN\", \"num_pos_tag_RB\", \"num_pos_tag_VB\", \"num_tokens\", \"num_chars\"]\n",
    "\n",
    "# # get unique csv (childs)\n",
    "# for idx, child in enumerate(df_f_eng[\"csv\"].unique().tolist()):\n",
    "#     # filter dataframe to the given child\n",
    "#     df_child = df_f_eng[df_f_eng[\"csv\"] == child]\n",
    "\n",
    "#     # extract label\n",
    "#     label = df_child[\"label\"].unique().tolist()[0]\n",
    "\n",
    "#     # groupby participant and create features per participant\n",
    "#     df_agg = df_child.groupby(\"participant\", as_index=False).agg(\n",
    "#         sum_num_pos_tag_JJ = (\"num_pos_tag_JJ\", \"sum\"), \n",
    "#         mean_num_pos_tag_JJ = (\"num_pos_tag_JJ\", \"mean\"), \n",
    "#         std_num_pos_tag_JJ = (\"num_pos_tag_JJ\", \"std\"),\n",
    "#         sum_num_pos_tag_NN = (\"num_pos_tag_NN\", \"sum\"),\n",
    "#         mean_num_pos_tag_NN = (\"num_pos_tag_NN\", \"mean\"),\n",
    "#         std_num_pos_tag_NN = (\"num_pos_tag_NN\", \"std\"), \n",
    "#         sum_num_pos_tag_RB = (\"num_pos_tag_RB\", \"sum\"),\n",
    "#         mean_num_pos_tag_RB = (\"num_pos_tag_RB\", \"mean\"),\n",
    "#         std_num_pos_tag_RB = (\"num_pos_tag_RB\", \"std\"), \n",
    "#         sum_num_pos_tag_VB = (\"num_pos_tag_VB\", \"sum\"),\n",
    "#         mean_num_pos_tag_VB = (\"num_pos_tag_VB\", \"mean\"),\n",
    "#         std_num_pos_tag_VB = (\"num_pos_tag_VB\", \"std\"),\n",
    "#         sum_num_tokens = (\"num_tokens\", \"sum\"), \n",
    "#         mean_num_tokens = (\"num_tokens\", \"mean\"), \n",
    "#         std_num_tokens = (\"num_tokens\", \"std\"), \n",
    "#         sum_num_chars = (\"num_chars\", \"sum\"),\n",
    "#         mean_num_chars = (\"num_chars\", \"mean\"),\n",
    "#         std_num_chars = (\"num_chars\", \"std\")\n",
    "#         )\n",
    "#     # inspect aggregation dataframe\n",
    "#     if idx == 0: \n",
    "#         display(\"df_agg just after aggregation\", df_agg.head(3))\n",
    "#         print(\"\\n\\n\")\n",
    "\n",
    "#     # iterate over pre-defined features\n",
    "#     for feature in features:\n",
    "#         # for each feature, create a percentage number of child / (child + parent)\n",
    "#         df_agg[f\"percent_child_total_of_{feature[-2:]}\"] = df_agg.loc[df_agg[\"participant\"] == \"CHI\", f\"sum_{feature}\"] / df_agg[f\"sum_{feature}\"].sum()\n",
    "#     # inspect aggregation dataframe\n",
    "#     if idx == 0: \n",
    "#         display(\"df_agg just after precent columns creation\", df_agg.head(3))\n",
    "#         print(\"\\n\\n\")\n",
    "\n",
    "#     # filter dataframe to only child features\n",
    "#     df_child_features = df_agg[df_agg[\"participant\"] == \"CHI\"]\n",
    "#     # inspect aggregation dataframe\n",
    "#     if idx == 0: \n",
    "#         display(\"df_agg just after CHI filtering\", df_agg.head(3))\n",
    "#         print(\"\\n\\n\")\n",
    "\n",
    "#     # define label\n",
    "#     df_child_features[\"label\"] = label\n",
    "#     df_child_features[\"csv\"] = child\n",
    "\n",
    "#     # append child features to modelling dataframe\n",
    "#     df_modelling = pd.concat(objs=[df_modelling, df_child_features], ignore_index=True)\n",
    "#     # inspect modelling dataframe\n",
    "#     if idx == 0: \n",
    "#         display(\"df_modelling just after concatenation\", df_modelling.head(3))\n",
    "#         print(\"\\n\\n\")\n",
    "\n",
    "# # inspect results\n",
    "# print(\"*\"*49)\n",
    "# df_modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "caIvhfZ4CTbh",
   "metadata": {
    "id": "caIvhfZ4CTbh"
   },
   "outputs": [],
   "source": [
    "# # reassing section dataframe\n",
    "# df_f_eng = df_modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f175924-8104-4183-b3c3-88ce24a96a95",
   "metadata": {
    "id": "7f175924-8104-4183-b3c3-88ce24a96a95"
   },
   "source": [
    "# 5 - DATA FILTERING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db87ecb-54d3-4a7c-92a3-bd7608812e56",
   "metadata": {
    "id": "3db87ecb-54d3-4a7c-92a3-bd7608812e56"
   },
   "source": [
    "## 5.1 - Restore Point"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea28e2b0-7968-4506-9a6e-f42a395cc213",
   "metadata": {
    "id": "ea28e2b0-7968-4506-9a6e-f42a395cc213"
   },
   "source": [
    "*Create a checkpoint of the last dataframe from previous section*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c2c97507-6e12-40d3-ac53-c15ac611bfc4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 237
    },
    "id": "c2c97507-6e12-40d3-ac53-c15ac611bfc4",
    "outputId": "b76ae003-d8b2-4ef7-d357-bdb85dcf5165",
    "require": [
     "base/js/events",
     "datatables.net",
     "d3",
     "chartjs",
     "dt-config",
     "dt-components",
     "dt-graph-objects",
     "dt-toolbar",
     "dt-tooltips",
     "jupyter-datatables"
    ]
   },
   "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>ASD</th>\n",
       "      <th>age_in_months</th>\n",
       "      <th>child_mlu</th>\n",
       "      <th>percent_child_parents_mlu</th>\n",
       "      <th>child_mluw</th>\n",
       "      <th>percent_child_parents_mluw</th>\n",
       "      <th>child_ttr</th>\n",
       "      <th>percent_child_parents_ttr</th>\n",
       "      <th>child_ipsyn</th>\n",
       "      <th>percent_child_parents_ipsyn</th>\n",
       "      <th>...</th>\n",
       "      <th>pos_percent_sub#n</th>\n",
       "      <th>pos_percent_n:pt</th>\n",
       "      <th>pos_percent_pro:refl</th>\n",
       "      <th>pos_percent_un#part</th>\n",
       "      <th>pos_percent_L2</th>\n",
       "      <th>pos_percent_chi</th>\n",
       "      <th>pos_percent_pro:poss</th>\n",
       "      <th>pos_percent_n:let</th>\n",
       "      <th>pos_percent_bab</th>\n",
       "      <th>pos_percent_sing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ASD</td>\n",
       "      <td>59</td>\n",
       "      <td>4.392</td>\n",
       "      <td>0.611</td>\n",
       "      <td>3.621</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.467</td>\n",
       "      <td>1.507</td>\n",
       "      <td>74</td>\n",
       "      <td>0.892</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>TYP</td>\n",
       "      <td>27</td>\n",
       "      <td>2.606</td>\n",
       "      <td>0.509</td>\n",
       "      <td>2.308</td>\n",
       "      <td>0.569</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.051</td>\n",
       "      <td>45</td>\n",
       "      <td>0.616</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ASD</td>\n",
       "      <td>51</td>\n",
       "      <td>1.105</td>\n",
       "      <td>0.218</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.500</td>\n",
       "      <td>2.556</td>\n",
       "      <td>2</td>\n",
       "      <td>0.026</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ASD  age_in_months  child_mlu  percent_child_parents_mlu  child_mluw  \\\n",
       "13  ASD             59      4.392                      0.611       3.621   \n",
       "32  TYP             27      2.606                      0.509       2.308   \n",
       "1   ASD             51      1.105                      0.218       1.000   \n",
       "\n",
       "    percent_child_parents_mluw  child_ttr  percent_child_parents_ttr  \\\n",
       "13                       0.640      0.467                      1.507   \n",
       "32                       0.569      0.500                      1.051   \n",
       "1                        0.265      0.500                      2.556   \n",
       "\n",
       "    child_ipsyn  percent_child_parents_ipsyn  ...  pos_percent_sub#n  \\\n",
       "13           74                        0.892  ...              0.000   \n",
       "32           45                        0.616  ...              0.000   \n",
       "1             2                        0.026  ...              0.000   \n",
       "\n",
       "    pos_percent_n:pt  pos_percent_pro:refl  pos_percent_un#part  \\\n",
       "13             0.000                 0.000                0.000   \n",
       "32             0.000                 0.000                0.000   \n",
       "1              0.000                 0.000                0.000   \n",
       "\n",
       "    pos_percent_L2  pos_percent_chi  pos_percent_pro:poss  pos_percent_n:let  \\\n",
       "13           0.000            0.002                 0.000              0.000   \n",
       "32           0.000            0.000                 0.000              0.000   \n",
       "1            0.000            0.000                 0.000              0.000   \n",
       "\n",
       "    pos_percent_bab  pos_percent_sing  \n",
       "13            0.000             0.000  \n",
       "32            0.000             0.000  \n",
       "1             0.000             0.000  \n",
       "\n",
       "[3 rows x 71 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a restore point for the previous section dataframe\n",
    "df_filter = df_f_eng.copy()\n",
    "\n",
    "# check dataframe\n",
    "df_filter.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de764f6f-e4c0-42af-a7dc-1caeb8e634d1",
   "metadata": {
    "id": "de764f6f-e4c0-42af-a7dc-1caeb8e634d1"
   },
   "source": [
    "## 5.2 - Rows Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7090d373-2060-47aa-8cd4-76f360cddaa2",
   "metadata": {
    "id": "7090d373-2060-47aa-8cd4-76f360cddaa2"
   },
   "source": [
    "*Remove rows with meaningless (or unimportant) data*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "113bebaf-72d0-43b4-8542-9b8ba80ae064",
   "metadata": {
    "id": "113bebaf-72d0-43b4-8542-9b8ba80ae064"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c3ea85-8d74-4995-bc67-d7e1fa81ccb9",
   "metadata": {
    "id": "80c3ea85-8d74-4995-bc67-d7e1fa81ccb9"
   },
   "source": [
    "## 5.3 - Columns Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ffe601-d210-407d-86cf-5352097747b4",
   "metadata": {
    "id": "49ffe601-d210-407d-86cf-5352097747b4"
   },
   "source": [
    "*Remove auxiliary columns or columns that won't be available in the prediction moment*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "wm2XkQ_PCbNp",
   "metadata": {
    "id": "wm2XkQ_PCbNp"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80dc1dd0-4a71-4e3c-b028-55745369f2cd",
   "metadata": {
    "id": "80dc1dd0-4a71-4e3c-b028-55745369f2cd"
   },
   "source": [
    "# 6 - Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1771d58-8a2d-4f96-b02b-ba3e24da3c92",
   "metadata": {
    "id": "c1771d58-8a2d-4f96-b02b-ba3e24da3c92"
   },
   "source": [
    "## 6.1 - Restore Point"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48b73ff0-6c16-4a82-9d27-b1c638970f45",
   "metadata": {
    "id": "48b73ff0-6c16-4a82-9d27-b1c638970f45"
   },
   "source": [
    "*Create a checkpoint of the last dataframe from previous section*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fd8459ce-2965-4249-be05-284ea01f1962",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 237
    },
    "id": "fd8459ce-2965-4249-be05-284ea01f1962",
    "outputId": "b42d79a6-5913-4700-95b9-a5108a42bf31",
    "require": [
     "base/js/events",
     "datatables.net",
     "d3",
     "chartjs",
     "dt-config",
     "dt-components",
     "dt-graph-objects",
     "dt-toolbar",
     "dt-tooltips",
     "jupyter-datatables"
    ]
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ASD</th>\n",
       "      <th>age_in_months</th>\n",
       "      <th>child_mlu</th>\n",
       "      <th>percent_child_parents_mlu</th>\n",
       "      <th>child_mluw</th>\n",
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       "      <th>child_ttr</th>\n",
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       "      <th>child_ipsyn</th>\n",
       "      <th>percent_child_parents_ipsyn</th>\n",
       "      <th>...</th>\n",
       "      <th>pos_percent_sub#n</th>\n",
       "      <th>pos_percent_n:pt</th>\n",
       "      <th>pos_percent_pro:refl</th>\n",
       "      <th>pos_percent_un#part</th>\n",
       "      <th>pos_percent_L2</th>\n",
       "      <th>pos_percent_chi</th>\n",
       "      <th>pos_percent_pro:poss</th>\n",
       "      <th>pos_percent_n:let</th>\n",
       "      <th>pos_percent_bab</th>\n",
       "      <th>pos_percent_sing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>TYP</td>\n",
       "      <td>24</td>\n",
       "      <td>1.051</td>\n",
       "      <td>0.218</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.255</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.445</td>\n",
       "      <td>3</td>\n",
       "      <td>0.043</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ASD</td>\n",
       "      <td>59</td>\n",
       "      <td>4.392</td>\n",
       "      <td>0.611</td>\n",
       "      <td>3.621</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.467</td>\n",
       "      <td>1.507</td>\n",
       "      <td>74</td>\n",
       "      <td>0.892</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ASD</td>\n",
       "      <td>65</td>\n",
       "      <td>1.389</td>\n",
       "      <td>0.317</td>\n",
       "      <td>1.125</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.254</td>\n",
       "      <td>1.144</td>\n",
       "      <td>15</td>\n",
       "      <td>0.197</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ASD  age_in_months  child_mlu  percent_child_parents_mlu  child_mluw  \\\n",
       "21  TYP             24      1.051                      0.218       1.000   \n",
       "13  ASD             59      4.392                      0.611       3.621   \n",
       "17  ASD             65      1.389                      0.317       1.125   \n",
       "\n",
       "    percent_child_parents_mluw  child_ttr  percent_child_parents_ttr  \\\n",
       "21                       0.255      1.000                      4.445   \n",
       "13                       0.640      0.467                      1.507   \n",
       "17                       0.320      0.254                      1.144   \n",
       "\n",
       "    child_ipsyn  percent_child_parents_ipsyn  ...  pos_percent_sub#n  \\\n",
       "21            3                        0.043  ...              0.000   \n",
       "13           74                        0.892  ...              0.000   \n",
       "17           15                        0.197  ...              0.000   \n",
       "\n",
       "    pos_percent_n:pt  pos_percent_pro:refl  pos_percent_un#part  \\\n",
       "21             0.000                 0.000                0.000   \n",
       "13             0.000                 0.000                0.000   \n",
       "17             0.000                 0.000                0.000   \n",
       "\n",
       "    pos_percent_L2  pos_percent_chi  pos_percent_pro:poss  pos_percent_n:let  \\\n",
       "21           0.000            0.000                 0.000              0.000   \n",
       "13           0.000            0.002                 0.000              0.000   \n",
       "17           0.000            0.000                 0.000              0.000   \n",
       "\n",
       "    pos_percent_bab  pos_percent_sing  \n",
       "21            0.024             0.000  \n",
       "13            0.000             0.000  \n",
       "17            0.000             0.000  \n",
       "\n",
       "[3 rows x 71 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a restore point for the previous section dataframe\n",
    "df_eda = df_filter.copy()\n",
    "\n",
    "# check dataframe\n",
    "df_eda.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "371e1623",
   "metadata": {
    "require": [
     "base/js/events",
     "datatables.net",
     "d3",
     "chartjs",
     "dt-config",
     "dt-components",
     "dt-graph-objects",
     "dt-toolbar",
     "dt-tooltips",
     "jupyter-datatables"
    ],
    "scrolled": true
   },
   "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>ASD</th>\n",
       "      <th>age_in_months</th>\n",
       "      <th>child_mlu</th>\n",
       "      <th>percent_child_parents_mlu</th>\n",
       "      <th>child_mluw</th>\n",
       "      <th>percent_child_parents_mluw</th>\n",
       "      <th>child_ttr</th>\n",
       "      <th>percent_child_parents_ttr</th>\n",
       "      <th>child_ipsyn</th>\n",
       "      <th>percent_child_parents_ipsyn</th>\n",
       "      <th>...</th>\n",
       "      <th>pos_percent_sub#n</th>\n",
       "      <th>pos_percent_n:pt</th>\n",
       "      <th>pos_percent_pro:refl</th>\n",
       "      <th>pos_percent_un#part</th>\n",
       "      <th>pos_percent_L2</th>\n",
       "      <th>pos_percent_chi</th>\n",
       "      <th>pos_percent_pro:poss</th>\n",
       "      <th>pos_percent_n:let</th>\n",
       "      <th>pos_percent_bab</th>\n",
       "      <th>pos_percent_sing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TYP</td>\n",
       "      <td>43</td>\n",
       "      <td>2.641</td>\n",
       "      <td>0.398</td>\n",
       "      <td>2.604</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.558</td>\n",
       "      <td>2.215</td>\n",
       "      <td>61</td>\n",
       "      <td>0.701</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ASD</td>\n",
       "      <td>51</td>\n",
       "      <td>1.105</td>\n",
       "      <td>0.218</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.500</td>\n",
       "      <td>2.556</td>\n",
       "      <td>2</td>\n",
       "      <td>0.026</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TYP</td>\n",
       "      <td>32</td>\n",
       "      <td>3.158</td>\n",
       "      <td>0.449</td>\n",
       "      <td>2.719</td>\n",
       "      <td>0.501</td>\n",
       "      <td>0.394</td>\n",
       "      <td>1.218</td>\n",
       "      <td>57</td>\n",
       "      <td>0.606</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASD</td>\n",
       "      <td>44</td>\n",
       "      <td>2.394</td>\n",
       "      <td>0.462</td>\n",
       "      <td>1.979</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.527</td>\n",
       "      <td>1.905</td>\n",
       "      <td>40</td>\n",
       "      <td>0.519</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ASD</td>\n",
       "      <td>79</td>\n",
       "      <td>5.213</td>\n",
       "      <td>0.841</td>\n",
       "      <td>4.023</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.972</td>\n",
       "      <td>81</td>\n",
       "      <td>0.931</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TYP</td>\n",
       "      <td>37</td>\n",
       "      <td>4.358</td>\n",
       "      <td>0.634</td>\n",
       "      <td>3.672</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.443</td>\n",
       "      <td>1.496</td>\n",
       "      <td>77</td>\n",
       "      <td>0.885</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ASD</td>\n",
       "      <td>73</td>\n",
       "      <td>3.625</td>\n",
       "      <td>0.639</td>\n",
       "      <td>3.410</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.526</td>\n",
       "      <td>2.072</td>\n",
       "      <td>53</td>\n",
       "      <td>0.654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TYP</td>\n",
       "      <td>35</td>\n",
       "      <td>7.855</td>\n",
       "      <td>1.203</td>\n",
       "      <td>6.642</td>\n",
       "      <td>1.277</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.834</td>\n",
       "      <td>74</td>\n",
       "      <td>0.881</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TYP</td>\n",
       "      <td>53</td>\n",
       "      <td>3.341</td>\n",
       "      <td>0.745</td>\n",
       "      <td>3.183</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.518</td>\n",
       "      <td>1.161</td>\n",
       "      <td>56</td>\n",
       "      <td>0.767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TYP</td>\n",
       "      <td>38</td>\n",
       "      <td>3.976</td>\n",
       "      <td>0.581</td>\n",
       "      <td>2.807</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.391</td>\n",
       "      <td>1.597</td>\n",
       "      <td>71</td>\n",
       "      <td>0.826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>TYP</td>\n",
       "      <td>32</td>\n",
       "      <td>4.241</td>\n",
       "      <td>0.730</td>\n",
       "      <td>3.753</td>\n",
       "      <td>0.784</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.777</td>\n",
       "      <td>64</td>\n",
       "      <td>0.865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ASD</td>\n",
       "      <td>43</td>\n",
       "      <td>2.568</td>\n",
       "      <td>0.651</td>\n",
       "      <td>1.816</td>\n",
       "      <td>0.626</td>\n",
       "      <td>0.435</td>\n",
       "      <td>1.465</td>\n",
       "      <td>20</td>\n",
       "      <td>0.328</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TYP</td>\n",
       "      <td>33</td>\n",
       "      <td>3.629</td>\n",
       "      <td>0.497</td>\n",
       "      <td>2.598</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.445</td>\n",
       "      <td>1.920</td>\n",
       "      <td>66</td>\n",
       "      <td>0.742</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ASD</td>\n",
       "      <td>59</td>\n",
       "      <td>4.392</td>\n",
       "      <td>0.611</td>\n",
       "      <td>3.621</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.467</td>\n",
       "      <td>1.507</td>\n",
       "      <td>74</td>\n",
       "      <td>0.892</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>TYP</td>\n",
       "      <td>35</td>\n",
       "      <td>3.917</td>\n",
       "      <td>0.542</td>\n",
       "      <td>3.109</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.437</td>\n",
       "      <td>1.741</td>\n",
       "      <td>71</td>\n",
       "      <td>0.755</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ASD</td>\n",
       "      <td>62</td>\n",
       "      <td>2.483</td>\n",
       "      <td>0.510</td>\n",
       "      <td>2.275</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.552</td>\n",
       "      <td>1.987</td>\n",
       "      <td>55</td>\n",
       "      <td>0.671</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TYP</td>\n",
       "      <td>24</td>\n",
       "      <td>1.333</td>\n",
       "      <td>0.317</td>\n",
       "      <td>1.074</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.397</td>\n",
       "      <td>1.487</td>\n",
       "      <td>10</td>\n",
       "      <td>0.145</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ASD</td>\n",
       "      <td>65</td>\n",
       "      <td>1.389</td>\n",
       "      <td>0.317</td>\n",
       "      <td>1.125</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.254</td>\n",
       "      <td>1.144</td>\n",
       "      <td>15</td>\n",
       "      <td>0.197</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TYP</td>\n",
       "      <td>38</td>\n",
       "      <td>3.785</td>\n",
       "      <td>0.804</td>\n",
       "      <td>3.030</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.465</td>\n",
       "      <td>1.076</td>\n",
       "      <td>60</td>\n",
       "      <td>0.698</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ASD</td>\n",
       "      <td>53</td>\n",
       "      <td>1.878</td>\n",
       "      <td>0.421</td>\n",
       "      <td>1.524</td>\n",
       "      <td>0.497</td>\n",
       "      <td>0.562</td>\n",
       "      <td>2.209</td>\n",
       "      <td>20</td>\n",
       "      <td>0.308</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ASD</td>\n",
       "      <td>76</td>\n",
       "      <td>4.283</td>\n",
       "      <td>0.698</td>\n",
       "      <td>3.708</td>\n",
       "      <td>0.751</td>\n",
       "      <td>0.391</td>\n",
       "      <td>1.366</td>\n",
       "      <td>75</td>\n",
       "      <td>0.843</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>TYP</td>\n",
       "      <td>24</td>\n",
       "      <td>1.051</td>\n",
       "      <td>0.218</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.255</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.445</td>\n",
       "      <td>3</td>\n",
       "      <td>0.043</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ASD</td>\n",
       "      <td>64</td>\n",
       "      <td>1.444</td>\n",
       "      <td>0.269</td>\n",
       "      <td>1.344</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.512</td>\n",
       "      <td>3.007</td>\n",
       "      <td>37</td>\n",
       "      <td>0.597</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>TYP</td>\n",
       "      <td>32</td>\n",
       "      <td>4.735</td>\n",
       "      <td>0.813</td>\n",
       "      <td>3.419</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.321</td>\n",
       "      <td>1.289</td>\n",
       "      <td>65</td>\n",
       "      <td>0.915</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ASD</td>\n",
       "      <td>66</td>\n",
       "      <td>3.067</td>\n",
       "      <td>0.503</td>\n",
       "      <td>3.316</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.397</td>\n",
       "      <td>1.491</td>\n",
       "      <td>18</td>\n",
       "      <td>0.212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>TYP</td>\n",
       "      <td>33</td>\n",
       "      <td>2.630</td>\n",
       "      <td>0.371</td>\n",
       "      <td>2.254</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.479</td>\n",
       "      <td>2.042</td>\n",
       "      <td>43</td>\n",
       "      <td>0.531</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ASD</td>\n",
       "      <td>59</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>TYP</td>\n",
       "      <td>32</td>\n",
       "      <td>3.730</td>\n",
       "      <td>0.698</td>\n",
       "      <td>2.567</td>\n",
       "      <td>0.597</td>\n",
       "      <td>0.442</td>\n",
       "      <td>1.344</td>\n",
       "      <td>61</td>\n",
       "      <td>0.744</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>TYP</td>\n",
       "      <td>29</td>\n",
       "      <td>4.084</td>\n",
       "      <td>0.860</td>\n",
       "      <td>3.129</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.364</td>\n",
       "      <td>1.052</td>\n",
       "      <td>69</td>\n",
       "      <td>0.802</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>TYP</td>\n",
       "      <td>27</td>\n",
       "      <td>2.148</td>\n",
       "      <td>0.338</td>\n",
       "      <td>1.512</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.597</td>\n",
       "      <td>2.093</td>\n",
       "      <td>46</td>\n",
       "      <td>0.561</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>TYP</td>\n",
       "      <td>28</td>\n",
       "      <td>1.952</td>\n",
       "      <td>0.341</td>\n",
       "      <td>1.382</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.438</td>\n",
       "      <td>1.827</td>\n",
       "      <td>42</td>\n",
       "      <td>0.494</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>TYP</td>\n",
       "      <td>27</td>\n",
       "      <td>2.640</td>\n",
       "      <td>0.558</td>\n",
       "      <td>2.060</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.910</td>\n",
       "      <td>44</td>\n",
       "      <td>0.629</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>TYP</td>\n",
       "      <td>27</td>\n",
       "      <td>2.606</td>\n",
       "      <td>0.509</td>\n",
       "      <td>2.308</td>\n",
       "      <td>0.569</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.051</td>\n",
       "      <td>45</td>\n",
       "      <td>0.616</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>TYP</td>\n",
       "      <td>23</td>\n",
       "      <td>1.083</td>\n",
       "      <td>0.215</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.253</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.801</td>\n",
       "      <td>1</td>\n",
       "      <td>0.014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>TYP</td>\n",
       "      <td>21</td>\n",
       "      <td>1.581</td>\n",
       "      <td>0.361</td>\n",
       "      <td>1.359</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.358</td>\n",
       "      <td>1.278</td>\n",
       "      <td>18</td>\n",
       "      <td>0.243</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>TYP</td>\n",
       "      <td>21</td>\n",
       "      <td>1.096</td>\n",
       "      <td>0.224</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.733</td>\n",
       "      <td>2.632</td>\n",
       "      <td>4</td>\n",
       "      <td>0.059</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>TYP</td>\n",
       "      <td>44</td>\n",
       "      <td>5.448</td>\n",
       "      <td>1.049</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.393</td>\n",
       "      <td>1.041</td>\n",
       "      <td>76</td>\n",
       "      <td>0.874</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>TYP</td>\n",
       "      <td>43</td>\n",
       "      <td>3.742</td>\n",
       "      <td>0.648</td>\n",
       "      <td>2.982</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.494</td>\n",
       "      <td>1.639</td>\n",
       "      <td>55</td>\n",
       "      <td>0.733</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ASD  age_in_months  child_mlu  percent_child_parents_mlu  child_mluw  \\\n",
       "0   TYP             43      2.641                      0.398       2.604   \n",
       "1   ASD             51      1.105                      0.218       1.000   \n",
       "2   TYP             32      3.158                      0.449       2.719   \n",
       "3   ASD             44      2.394                      0.462       1.979   \n",
       "4   ASD             79      5.213                      0.841       4.023   \n",
       "5   TYP             37      4.358                      0.634       3.672   \n",
       "6   ASD             73      3.625                      0.639       3.410   \n",
       "7   TYP             35      7.855                      1.203       6.642   \n",
       "8   TYP             53      3.341                      0.745       3.183   \n",
       "9   TYP             38      3.976                      0.581       2.807   \n",
       "10  TYP             32      4.241                      0.730       3.753   \n",
       "11  ASD             43      2.568                      0.651       1.816   \n",
       "12  TYP             33      3.629                      0.497       2.598   \n",
       "13  ASD             59      4.392                      0.611       3.621   \n",
       "14  TYP             35      3.917                      0.542       3.109   \n",
       "15  ASD             62      2.483                      0.510       2.275   \n",
       "16  TYP             24      1.333                      0.317       1.074   \n",
       "17  ASD             65      1.389                      0.317       1.125   \n",
       "18  TYP             38      3.785                      0.804       3.030   \n",
       "19  ASD             53      1.878                      0.421       1.524   \n",
       "20  ASD             76      4.283                      0.698       3.708   \n",
       "21  TYP             24      1.051                      0.218       1.000   \n",
       "22  ASD             64      1.444                      0.269       1.344   \n",
       "23  TYP             32      4.735                      0.813       3.419   \n",
       "24  ASD             66      3.067                      0.503       3.316   \n",
       "25  TYP             33      2.630                      0.371       2.254   \n",
       "26  ASD             59      1.000                      0.198       0.000   \n",
       "27  TYP             32      3.730                      0.698       2.567   \n",
       "28  TYP             29      4.084                      0.860       3.129   \n",
       "29  TYP             27      2.148                      0.338       1.512   \n",
       "30  TYP             28      1.952                      0.341       1.382   \n",
       "31  TYP             27      2.640                      0.558       2.060   \n",
       "32  TYP             27      2.606                      0.509       2.308   \n",
       "33  TYP             23      1.083                      0.215       1.000   \n",
       "34  TYP             21      1.581                      0.361       1.359   \n",
       "35  TYP             21      1.096                      0.224       1.000   \n",
       "36  TYP             44      5.448                      1.049       4.000   \n",
       "37  TYP             43      3.742                      0.648       2.982   \n",
       "\n",
       "    percent_child_parents_mluw  child_ttr  percent_child_parents_ttr  \\\n",
       "0                        0.472      0.558                      2.215   \n",
       "1                        0.265      0.500                      2.556   \n",
       "2                        0.501      0.394                      1.218   \n",
       "3                        0.488      0.527                      1.905   \n",
       "4                        0.832      0.421                      0.972   \n",
       "5                        0.665      0.443                      1.496   \n",
       "6                        0.793      0.526                      2.072   \n",
       "7                        1.277      0.326                      0.834   \n",
       "8                        0.873      0.518                      1.161   \n",
       "9                        0.523      0.391                      1.597   \n",
       "10                       0.784      0.299                      0.777   \n",
       "11                       0.626      0.435                      1.465   \n",
       "12                       0.431      0.445                      1.920   \n",
       "13                       0.640      0.467                      1.507   \n",
       "14                       0.541      0.437                      1.741   \n",
       "15                       0.615      0.552                      1.987   \n",
       "16                       0.331      0.397                      1.487   \n",
       "17                       0.320      0.254                      1.144   \n",
       "18                       0.832      0.465                      1.076   \n",
       "19                       0.497      0.562                      2.209   \n",
       "20                       0.751      0.391                      1.366   \n",
       "21                       0.255      1.000                      4.445   \n",
       "22                       0.324      0.512                      3.007   \n",
       "23                       0.765      0.321                      1.289   \n",
       "24                       0.694      0.397                      1.491   \n",
       "25                       0.396      0.479                      2.042   \n",
       "26                       0.000      0.000                      0.000   \n",
       "27                       0.597      0.442                      1.344   \n",
       "28                       0.834      0.364                      1.052   \n",
       "29                       0.315      0.597                      2.093   \n",
       "30                       0.302      0.438                      1.827   \n",
       "31                       0.549      0.274                      0.910   \n",
       "32                       0.569      0.500                      1.051   \n",
       "33                       0.253      1.000                      3.801   \n",
       "34                       0.396      0.358                      1.278   \n",
       "35                       0.249      0.733                      2.632   \n",
       "36                       0.978      0.393                      1.041   \n",
       "37                       0.679      0.494                      1.639   \n",
       "\n",
       "    child_ipsyn  percent_child_parents_ipsyn  ...  pos_percent_sub#n  \\\n",
       "0            61                        0.701  ...              0.000   \n",
       "1             2                        0.026  ...              0.000   \n",
       "2            57                        0.606  ...              0.000   \n",
       "3            40                        0.519  ...              0.000   \n",
       "4            81                        0.931  ...              0.000   \n",
       "5            77                        0.885  ...              0.000   \n",
       "6            53                        0.654  ...              0.000   \n",
       "7            74                        0.881  ...              0.004   \n",
       "8            56                        0.767  ...              0.000   \n",
       "9            71                        0.826  ...              0.000   \n",
       "10           64                        0.865  ...              0.000   \n",
       "11           20                        0.328  ...              0.000   \n",
       "12           66                        0.742  ...              0.000   \n",
       "13           74                        0.892  ...              0.000   \n",
       "14           71                        0.755  ...              0.000   \n",
       "15           55                        0.671  ...              0.000   \n",
       "16           10                        0.145  ...              0.000   \n",
       "17           15                        0.197  ...              0.000   \n",
       "18           60                        0.698  ...              0.000   \n",
       "19           20                        0.308  ...              0.000   \n",
       "20           75                        0.843  ...              0.000   \n",
       "21            3                        0.043  ...              0.000   \n",
       "22           37                        0.597  ...              0.000   \n",
       "23           65                        0.915  ...              0.000   \n",
       "24           18                        0.212  ...              0.000   \n",
       "25           43                        0.531  ...              0.000   \n",
       "26            0                        0.000  ...              0.000   \n",
       "27           61                        0.744  ...              0.000   \n",
       "28           69                        0.802  ...              0.000   \n",
       "29           46                        0.561  ...              0.000   \n",
       "30           42                        0.494  ...              0.000   \n",
       "31           44                        0.629  ...              0.000   \n",
       "32           45                        0.616  ...              0.000   \n",
       "33            1                        0.014  ...              0.000   \n",
       "34           18                        0.243  ...              0.000   \n",
       "35            4                        0.059  ...              0.000   \n",
       "36           76                        0.874  ...              0.000   \n",
       "37           55                        0.733  ...              0.000   \n",
       "\n",
       "    pos_percent_n:pt  pos_percent_pro:refl  pos_percent_un#part  \\\n",
       "0              0.000                 0.000                0.000   \n",
       "1              0.000                 0.000                0.000   \n",
       "2              0.000                 0.000                0.000   \n",
       "3              0.000                 0.000                0.000   \n",
       "4              0.000                 0.000                0.000   \n",
       "5              0.000                 0.000                0.000   \n",
       "6              0.000                 0.000                0.000   \n",
       "7              0.000                 0.000                0.000   \n",
       "8              0.007                 0.003                0.003   \n",
       "9              0.000                 0.000                0.000   \n",
       "10             0.006                 0.000                0.000   \n",
       "11             0.000                 0.000                0.000   \n",
       "12             0.000                 0.000                0.000   \n",
       "13             0.000                 0.000                0.000   \n",
       "14             0.007                 0.002                0.000   \n",
       "15             0.000                 0.000                0.000   \n",
       "16             0.000                 0.000                0.000   \n",
       "17             0.000                 0.000                0.000   \n",
       "18             0.000                 0.000                0.000   \n",
       "19             0.000                 0.000                0.000   \n",
       "20             0.000                 0.000                0.000   \n",
       "21             0.000                 0.000                0.000   \n",
       "22             0.000                 0.000                0.000   \n",
       "23             0.002                 0.000                0.000   \n",
       "24             0.000                 0.000                0.000   \n",
       "25             0.000                 0.000                0.000   \n",
       "26             0.000                 0.000                0.000   \n",
       "27             0.000                 0.000                0.000   \n",
       "28             0.002                 0.000                0.000   \n",
       "29             0.000                 0.000                0.000   \n",
       "30             0.000                 0.000                0.000   \n",
       "31             0.000                 0.000                0.000   \n",
       "32             0.000                 0.000                0.000   \n",
       "33             0.000                 0.000                0.000   \n",
       "34             0.000                 0.000                0.000   \n",
       "35             0.000                 0.000                0.000   \n",
       "36             0.000                 0.000                0.000   \n",
       "37             0.000                 0.000                0.000   \n",
       "\n",
       "    pos_percent_L2  pos_percent_chi  pos_percent_pro:poss  pos_percent_n:let  \\\n",
       "0            0.000            0.000                 0.000              0.000   \n",
       "1            0.000            0.000                 0.000              0.000   \n",
       "2            0.000            0.000                 0.000              0.000   \n",
       "3            0.000            0.000                 0.000              0.000   \n",
       "4            0.000            0.000                 0.000              0.000   \n",
       "5            0.000            0.000                 0.000              0.000   \n",
       "6            0.000            0.000                 0.000              0.000   \n",
       "7            0.000            0.000                 0.000              0.000   \n",
       "8            0.000            0.000                 0.000              0.000   \n",
       "9            0.000            0.000                 0.000              0.000   \n",
       "10           0.009            0.002                 0.000              0.000   \n",
       "11           0.009            0.000                 0.000              0.000   \n",
       "12           0.000            0.000                 0.003              0.000   \n",
       "13           0.000            0.002                 0.000              0.000   \n",
       "14           0.000            0.000                 0.000              0.000   \n",
       "15           0.000            0.005                 0.000              0.000   \n",
       "16           0.000            0.005                 0.000              0.000   \n",
       "17           0.000            0.000                 0.000              0.000   \n",
       "18           0.000            0.003                 0.007              0.000   \n",
       "19           0.000            0.000                 0.000              0.000   \n",
       "20           0.000            0.000                 0.000              0.004   \n",
       "21           0.000            0.000                 0.000              0.000   \n",
       "22           0.000            0.000                 0.000              0.000   \n",
       "23           0.000            0.000                 0.004              0.000   \n",
       "24           0.000            0.000                 0.000              0.000   \n",
       "25           0.000            0.000                 0.000              0.000   \n",
       "26           0.000            0.000                 0.000              0.000   \n",
       "27           0.000            0.000                 0.000              0.000   \n",
       "28           0.000            0.000                 0.000              0.000   \n",
       "29           0.000            0.000                 0.000              0.000   \n",
       "30           0.042            0.000                 0.000              0.000   \n",
       "31           0.000            0.000                 0.000              0.000   \n",
       "32           0.000            0.000                 0.000              0.000   \n",
       "33           0.000            0.000                 0.000              0.000   \n",
       "34           0.000            0.000                 0.008              0.000   \n",
       "35           0.000            0.000                 0.000              0.000   \n",
       "36           0.000            0.000                 0.000              0.000   \n",
       "37           0.000            0.000                 0.000              0.000   \n",
       "\n",
       "    pos_percent_bab  pos_percent_sing  \n",
       "0             0.000             0.000  \n",
       "1             0.000             0.000  \n",
       "2             0.000             0.000  \n",
       "3             0.000             0.000  \n",
       "4             0.000             0.000  \n",
       "5             0.000             0.000  \n",
       "6             0.000             0.000  \n",
       "7             0.000             0.000  \n",
       "8             0.000             0.000  \n",
       "9             0.000             0.000  \n",
       "10            0.000             0.000  \n",
       "11            0.000             0.000  \n",
       "12            0.000             0.000  \n",
       "13            0.000             0.000  \n",
       "14            0.000             0.000  \n",
       "15            0.000             0.000  \n",
       "16            0.000             0.000  \n",
       "17            0.000             0.000  \n",
       "18            0.000             0.000  \n",
       "19            0.000             0.000  \n",
       "20            0.000             0.000  \n",
       "21            0.024             0.000  \n",
       "22            0.000             0.000  \n",
       "23            0.000             0.000  \n",
       "24            0.000             0.000  \n",
       "25            0.000             0.000  \n",
       "26            0.000             0.000  \n",
       "27            0.000             0.000  \n",
       "28            0.000             0.008  \n",
       "29            0.000             0.000  \n",
       "30            0.000             0.000  \n",
       "31            0.000             0.000  \n",
       "32            0.000             0.000  \n",
       "33            0.000             0.000  \n",
       "34            0.000             0.000  \n",
       "35            0.075             0.000  \n",
       "36            0.000             0.000  \n",
       "37            0.000             0.000  \n",
       "\n",
       "[38 rows x 71 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_eda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0e0b812a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ydata_profiling import ProfileReport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4102c6df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the Profiling Report\n",
    "profile = ProfileReport(\n",
    "    df_eda, title=\"Exploratory Data Analysis\", html={\"style\": {\"full_width\": True}}, sort=None\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "363bb944",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81908f9be0db449d9c7b2d5916528c0d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77e0a90642724c4784c396f2fe450680",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Render widgets:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f7942d3744c490cbf0c296e206fa078",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Tab(children=(Tab(children=(GridBox(children=(VBox(children=(GridspecLayout(children=(HTML(valu…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The Notebook Widgets Interface\n",
    "profile.to_widgets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f867589c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate our EDA in a HTML File\n",
    "profile.to_file(\"Nadig_EDA.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b05e0e4-2341-44f2-ab7e-c7e677554474",
   "metadata": {
    "id": "2b05e0e4-2341-44f2-ab7e-c7e677554474"
   },
   "source": [
    "## 6.2 - Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e8eca1-6694-4940-b070-c2c67566981c",
   "metadata": {
    "id": "71e8eca1-6694-4940-b070-c2c67566981c"
   },
   "source": [
    "*Check the distribution of variables taking into account their data types*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55a67b62-5296-4deb-ad14-68f4c6ab9ed6",
   "metadata": {
    "id": "55a67b62-5296-4deb-ad14-68f4c6ab9ed6"
   },
   "source": [
    "### 6.2.1 - Numeric Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4be8c28e-94fb-42aa-992b-3c5e65fbfdd8",
   "metadata": {
    "id": "4be8c28e-94fb-42aa-992b-3c5e65fbfdd8"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c44be4c7-c566-4bbc-9c5b-9fb010ef56fe",
   "metadata": {
    "id": "c44be4c7-c566-4bbc-9c5b-9fb010ef56fe"
   },
   "source": [
    "### 6.2.2 - Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f346d0c-609c-49be-84b2-940e85ac8ccb",
   "metadata": {
    "id": "7f346d0c-609c-49be-84b2-940e85ac8ccb"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6159ba9a-6c2b-4f91-ad83-fca8cd5c78cd",
   "metadata": {
    "id": "6159ba9a-6c2b-4f91-ad83-fca8cd5c78cd"
   },
   "source": [
    "### 6.2.3 - Time Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d697e251-9b62-4a87-82be-10ef02cca916",
   "metadata": {
    "id": "d697e251-9b62-4a87-82be-10ef02cca916"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10a9060a-d1f2-4c3a-b56a-f1d4d34f1c5c",
   "metadata": {
    "id": "10a9060a-d1f2-4c3a-b56a-f1d4d34f1c5c"
   },
   "source": [
    "## 6.3 - Bivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6abb6b5-fb46-4ef7-9cf7-5b7162cb637f",
   "metadata": {
    "id": "c6abb6b5-fb46-4ef7-9cf7-5b7162cb637f"
   },
   "source": [
    "*Check the correlation among variables*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61c0fdd5-b2be-43c4-a0a5-137d0451da43",
   "metadata": {
    "id": "61c0fdd5-b2be-43c4-a0a5-137d0451da43"
   },
   "source": [
    "### 6.3.1 - Numeric vs Numeric Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71e55f35-6926-43a4-9874-69dacb7397f8",
   "metadata": {
    "id": "71e55f35-6926-43a4-9874-69dacb7397f8"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec003fc-82aa-4cc8-a3d3-dbf0a4f0d377",
   "metadata": {
    "id": "1ec003fc-82aa-4cc8-a3d3-dbf0a4f0d377"
   },
   "source": [
    "### 6.3.2 - Categorical vs Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7e08eeb-55a7-47d4-bb8c-bf04f39499b6",
   "metadata": {
    "id": "d7e08eeb-55a7-47d4-bb8c-bf04f39499b6"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3a55500-c269-489b-9f24-1e8f9b19d73f",
   "metadata": {
    "id": "b3a55500-c269-489b-9f24-1e8f9b19d73f"
   },
   "source": [
    "### 6.3.3 - Numerical vs Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f39f519a-d32b-4434-aa73-a7495d420851",
   "metadata": {
    "id": "f39f519a-d32b-4434-aa73-a7495d420851"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6a33c4e-8268-4930-972a-b6e3a8486978",
   "metadata": {
    "id": "e6a33c4e-8268-4930-972a-b6e3a8486978"
   },
   "source": [
    "# 7 - Synthetic Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e6e82f1-9e8c-46e3-a4fa-45b18627453c",
   "metadata": {
    "id": "0e6e82f1-9e8c-46e3-a4fa-45b18627453c"
   },
   "source": [
    "*Do we need to create synthetic data?*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b8a72e5-8f5f-4afc-a55a-ebd95cee5199",
   "metadata": {
    "id": "4b8a72e5-8f5f-4afc-a55a-ebd95cee5199"
   },
   "source": [
    "*Is it possible to create synthetic data for this problem?*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "065170f8-b4ec-4f6e-a438-6dc48e96e71a",
   "metadata": {
    "id": "065170f8-b4ec-4f6e-a438-6dc48e96e71a"
   },
   "source": [
    "*What are the pros and cons of synthetic data?*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7bf2e78-a626-48b9-a44b-2d61f30b367f",
   "metadata": {
    "id": "b7bf2e78-a626-48b9-a44b-2d61f30b367f"
   },
   "source": [
    "*Techniques*:\n",
    "- oversampling\n",
    "- undersampling\n",
    "- SMOTE\n",
    "- model loss function weighting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f1a7042-bbcf-4d17-a5e6-a40b7694e417",
   "metadata": {
    "id": "9f1a7042-bbcf-4d17-a5e6-a40b7694e417"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "547d9300-5534-472b-acfd-f1abb50d7d6a",
   "metadata": {
    "id": "547d9300-5534-472b-acfd-f1abb50d7d6a"
   },
   "source": [
    "# 8 Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6471d30-7d80-4215-a91f-4e611fb4cf15",
   "metadata": {
    "id": "b6471d30-7d80-4215-a91f-4e611fb4cf15"
   },
   "source": [
    "## 8.1 - Restore Point"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60cd03bc-299f-4c4b-8355-680e654c9703",
   "metadata": {
    "id": "60cd03bc-299f-4c4b-8355-680e654c9703"
   },
   "source": [
    "*Create a checkpoint of the last dataframe from previous section*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65df4fd8-17bb-449f-b94b-49a3b04d1532",
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 237
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    "id": "65df4fd8-17bb-449f-b94b-49a3b04d1532",
    "outputId": "e6655992-38dc-4650-981c-04b2b21d966a"
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   "outputs": [
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       "    ASD  child_mlu  percent_child_parents_mlu  child_mluw  \\\n",
       "5   TYP      4.358                      0.634       3.672   \n",
       "16  TYP      1.333                      0.317       1.074   \n",
       "2   TYP      3.158                      0.449       2.719   \n",
       "\n",
       "    percent_child_parents_mluw  child_ttr  percent_child_parents_ttr  \\\n",
       "5                        0.665      0.443                      1.496   \n",
       "16                       0.331      0.397                      1.487   \n",
       "2                        0.501      0.394                      1.218   \n",
       "\n",
       "    child_ipsyn  percent_child_parents_ipsyn  child_num_words  ...  \\\n",
       "5            77                        0.885              332  ...   \n",
       "16           10                        0.145              205  ...   \n",
       "2            57                        0.606              233  ...   \n",
       "\n",
       "    pos_percent_sub#n  pos_percent_n:pt  pos_percent_pro:refl  \\\n",
       "5               0.000             0.000                 0.000   \n",
       "16              0.000             0.000                 0.000   \n",
       "2               0.000             0.000                 0.000   \n",
       "\n",
       "    pos_percent_un#part  pos_percent_L2  pos_percent_chi  \\\n",
       "5                 0.000           0.000            0.000   \n",
       "16                0.000           0.000            0.005   \n",
       "2                 0.000           0.000            0.000   \n",
       "\n",
       "    pos_percent_pro:poss  pos_percent_n:let  pos_percent_bab  pos_percent_sing  \n",
       "5                  0.000              0.000            0.000             0.000  \n",
       "16                 0.000              0.000            0.000             0.000  \n",
       "2                  0.000              0.000            0.000             0.000  \n",
       "\n",
       "[3 rows x 70 columns]"
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     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a restore point for the previous section dataframe\n",
    "df_prep = df_eda.copy()\n",
    "\n",
    "# check dataframe\n",
    "df_prep.sample(3)"
   ]
  },
  {
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   "execution_count": null,
   "id": "j80ro2i4tI1k",
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    },
    "id": "j80ro2i4tI1k",
    "outputId": "d425ba83-df9e-4377-cfa9-efe4e3cc8672"
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       "      <td>0.558</td>\n",
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       "      <td>1.105</td>\n",
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       "      <td>0.500</td>\n",
       "      <td>2.556</td>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>2.394</td>\n",
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       "      <td>1.979</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.527</td>\n",
       "      <td>1.905</td>\n",
       "      <td>40</td>\n",
       "      <td>0.519</td>\n",
       "      <td>165</td>\n",
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       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.213</td>\n",
       "      <td>0.841</td>\n",
       "      <td>4.023</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.972</td>\n",
       "      <td>81</td>\n",
       "      <td>0.931</td>\n",
       "      <td>462</td>\n",
       "      <td>0.918</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "<p>5 rows × 69 columns</p>\n",
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       "\n",
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       "          const element = document.querySelector('#df-36860d43-cf95-4bc2-befb-addfc4bafe84');\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "            + ' to learn more about interactive tables.';\n",
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       "  "
      ],
      "text/plain": [
       "   child_mlu  percent_child_parents_mlu  child_mluw  \\\n",
       "0      2.641                      0.398       2.604   \n",
       "1      1.105                      0.218       1.000   \n",
       "2      3.158                      0.449       2.719   \n",
       "3      2.394                      0.462       1.979   \n",
       "4      5.213                      0.841       4.023   \n",
       "\n",
       "   percent_child_parents_mluw  child_ttr  percent_child_parents_ttr  \\\n",
       "0                       0.472      0.558                      2.215   \n",
       "1                       0.265      0.500                      2.556   \n",
       "2                       0.501      0.394                      1.218   \n",
       "3                       0.488      0.527                      1.905   \n",
       "4                       0.832      0.421                      0.972   \n",
       "\n",
       "   child_ipsyn  percent_child_parents_ipsyn  child_num_words  \\\n",
       "0           61                        0.701              212   \n",
       "1            2                        0.026               21   \n",
       "2           57                        0.606              233   \n",
       "3           40                        0.519              165   \n",
       "4           81                        0.931              462   \n",
       "\n",
       "   perct_child_parents_words  ...  pos_percent_sub#n  pos_percent_n:pt  \\\n",
       "0                      0.155  ...              0.000             0.000   \n",
       "1                      0.015  ...              0.000             0.000   \n",
       "2                      0.238  ...              0.000             0.000   \n",
       "3                      0.193  ...              0.000             0.000   \n",
       "4                      0.918  ...              0.000             0.000   \n",
       "\n",
       "   pos_percent_pro:refl  pos_percent_un#part  pos_percent_L2  pos_percent_chi  \\\n",
       "0                 0.000                0.000           0.000            0.000   \n",
       "1                 0.000                0.000           0.000            0.000   \n",
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       "3                 0.000                0.000           0.000            0.000   \n",
       "4                 0.000                0.000           0.000            0.000   \n",
       "\n",
       "   pos_percent_pro:poss  pos_percent_n:let  pos_percent_bab  pos_percent_sing  \n",
       "0                 0.000              0.000            0.000             0.000  \n",
       "1                 0.000              0.000            0.000             0.000  \n",
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       "3                 0.000              0.000            0.000             0.000  \n",
       "4                 0.000              0.000            0.000             0.000  \n",
       "\n",
       "[5 rows x 69 columns]"
      ]
     },
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    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'y data'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "3    1\n",
       "4    1\n",
       "Name: ASD, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define X and y data\n",
    "X = df_prep.drop(columns=[\"ASD\"])\n",
    "y = df_prep[\"ASD\"]\n",
    "\n",
    "# map labels\n",
    "y = y.map({\"ASD\": 1, \"TYP\": 0})\n",
    "\n",
    "# check X and y\n",
    "display(\"X data\", X.head(), \"y data\", y.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ecba23-3fb6-4330-8e23-baa1b29af380",
   "metadata": {
    "id": "d3ecba23-3fb6-4330-8e23-baa1b29af380"
   },
   "source": [
    "## 8.2 - Train-validation-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "CWQFGJbptFlZ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 847
    },
    "id": "CWQFGJbptFlZ",
    "outputId": "a28e71da-5a42-42a9-cde7-2d4de3677b29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y stratification: 0.34%\n",
      "y_train stratification: 0.33%\n",
      "y_test stratification: 0.38%\n",
      "\n",
      "X_train proportion: 78.95% of whole dataset\n",
      "y_train proportion: 78.95% of whole dataset\n",
      "\n",
      "X_test proportion: 21.05% of whole dataset\n",
      "y_test proportion: 21.05% of whole dataset\n",
      "\n"
     ]
    },
    {
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       "type": "string"
      },
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       "'X_train'"
      ]
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    },
    {
     "data": {
      "text/plain": [
       "(30, 69)"
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       "      <th>child_mlu</th>\n",
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       "      <th>...</th>\n",
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       "      <td>3.067</td>\n",
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       "      <td>3.316</td>\n",
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       "      <td>2.640</td>\n",
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       "      <td>2.060</td>\n",
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       "      <td>44</td>\n",
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       "   child_mlu  percent_child_parents_mlu  child_mluw  \\\n",
       "0      3.067                      0.503       3.316   \n",
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       "2      2.640                      0.558       2.060   \n",
       "\n",
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       "0           18                        0.212               94   \n",
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       "2           44                        0.629              380   \n",
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       "1                      0.217  ...              0.000             0.000   \n",
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       "\n",
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       "0                 0.000                0.000           0.000            0.000   \n",
       "1                 0.000                0.000           0.000            0.000   \n",
       "2                 0.000                0.000           0.000            0.000   \n",
       "\n",
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       "      <th>child_mluw</th>\n",
       "      <th>percent_child_parents_mluw</th>\n",
       "      <th>child_ttr</th>\n",
       "      <th>percent_child_parents_ttr</th>\n",
       "      <th>child_ipsyn</th>\n",
       "      <th>percent_child_parents_ipsyn</th>\n",
       "      <th>child_num_words</th>\n",
       "      <th>perct_child_parents_words</th>\n",
       "      <th>...</th>\n",
       "      <th>pos_percent_sub#n</th>\n",
       "      <th>pos_percent_n:pt</th>\n",
       "      <th>pos_percent_pro:refl</th>\n",
       "      <th>pos_percent_un#part</th>\n",
       "      <th>pos_percent_L2</th>\n",
       "      <th>pos_percent_chi</th>\n",
       "      <th>pos_percent_pro:poss</th>\n",
       "      <th>pos_percent_n:let</th>\n",
       "      <th>pos_percent_bab</th>\n",
       "      <th>pos_percent_sing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.568</td>\n",
       "      <td>0.651</td>\n",
       "      <td>1.816</td>\n",
       "      <td>0.626</td>\n",
       "      <td>0.435</td>\n",
       "      <td>1.465</td>\n",
       "      <td>20</td>\n",
       "      <td>0.328</td>\n",
       "      <td>115</td>\n",
       "      <td>0.221</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.855</td>\n",
       "      <td>1.203</td>\n",
       "      <td>6.642</td>\n",
       "      <td>1.277</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.834</td>\n",
       "      <td>74</td>\n",
       "      <td>0.881</td>\n",
       "      <td>534</td>\n",
       "      <td>0.947</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.735</td>\n",
       "      <td>0.813</td>\n",
       "      <td>3.419</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.321</td>\n",
       "      <td>1.289</td>\n",
       "      <td>65</td>\n",
       "      <td>0.915</td>\n",
       "      <td>425</td>\n",
       "      <td>0.532</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 69 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ebc86b11-5ead-476e-bc98-d4e34f7dcfea')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-ebc86b11-5ead-476e-bc98-d4e34f7dcfea button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-ebc86b11-5ead-476e-bc98-d4e34f7dcfea');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   child_mlu  percent_child_parents_mlu  child_mluw  \\\n",
       "0      2.568                      0.651       1.816   \n",
       "1      7.855                      1.203       6.642   \n",
       "2      4.735                      0.813       3.419   \n",
       "\n",
       "   percent_child_parents_mluw  child_ttr  percent_child_parents_ttr  \\\n",
       "0                       0.626      0.435                      1.465   \n",
       "1                       1.277      0.326                      0.834   \n",
       "2                       0.765      0.321                      1.289   \n",
       "\n",
       "   child_ipsyn  percent_child_parents_ipsyn  child_num_words  \\\n",
       "0           20                        0.328              115   \n",
       "1           74                        0.881              534   \n",
       "2           65                        0.915              425   \n",
       "\n",
       "   perct_child_parents_words  ...  pos_percent_sub#n  pos_percent_n:pt  \\\n",
       "0                      0.221  ...              0.000             0.000   \n",
       "1                      0.947  ...              0.004             0.000   \n",
       "2                      0.532  ...              0.000             0.002   \n",
       "\n",
       "   pos_percent_pro:refl  pos_percent_un#part  pos_percent_L2  pos_percent_chi  \\\n",
       "0                 0.000                0.000           0.009            0.000   \n",
       "1                 0.000                0.000           0.000            0.000   \n",
       "2                 0.000                0.000           0.000            0.000   \n",
       "\n",
       "   pos_percent_pro:poss  pos_percent_n:let  pos_percent_bab  pos_percent_sing  \n",
       "0                 0.000              0.000            0.000             0.000  \n",
       "1                 0.000              0.000            0.000             0.000  \n",
       "2                 0.004              0.000            0.000             0.000  \n",
       "\n",
       "[3 rows x 69 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'y_test'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(8,)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    0\n",
       "Name: ASD, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define ratio of test split ratio \n",
    "test_ratio = 0.2 \n",
    "\n",
    "# Train-Test split for the whole dataset\n",
    "# X: attributes\n",
    "# y: labels\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=test_ratio, random_state=seed, \n",
    "                                                    shuffle=True, stratify=y)\n",
    "\n",
    "# reset index after train test split\n",
    "X_train = X_train.reset_index(drop=True)\n",
    "y_train = y_train.reset_index(drop=True)\n",
    "X_test = X_test.reset_index(drop=True)\n",
    "y_test = y_test.reset_index(drop=True)\n",
    "\n",
    "# check dataframes\n",
    "print(\n",
    "    f\"Y stratification: {y.mean():.2f}%\\n\"\n",
    "    f\"y_train stratification: {y_train.mean():.2f}%\\n\"\n",
    "    f\"y_test stratification: {y_test.mean():.2f}%\\n\\n\"\n",
    "    f\"X_train proportion: {(len(X_train) / len(X))*100:.2f}% of whole dataset\\n\"\n",
    "    f\"y_train proportion: {(len(y_train) / len(y))*100:.2f}% of whole dataset\\n\\n\"\n",
    "    f\"X_test proportion: {(len(X_test) / len(X))*100:.2f}% of whole dataset\\n\"    \n",
    "    f\"y_test proportion: {(len(y_test) / len(y))*100:.2f}% of whole dataset\\n\"\n",
    ")\n",
    "\n",
    "# check dataframes\n",
    "display(\n",
    "    \"X_train\", X_train.shape, X_train.head(3), \n",
    "    \"y_train\", y_train.shape, y_train.head(3), \n",
    "    \"X_test\", X_test.shape, X_test.head(3), \n",
    "    \"y_test\", y_test.shape, y_test.head(3)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06d95381-60f7-432b-84e3-5bcf1a07f2ca",
   "metadata": {
    "id": "06d95381-60f7-432b-84e3-5bcf1a07f2ca"
   },
   "source": [
    "## 8.3 - Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9093081-b0a3-44e3-a473-209e856e5a38",
   "metadata": {
    "id": "a9093081-b0a3-44e3-a473-209e856e5a38"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "_E9cx-3cSONi",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 554
    },
    "id": "_E9cx-3cSONi",
    "outputId": "80ba0bc8-f7bf-4752-9a51-be9b6df82651"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'X_train'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-c23226d1-3017-4b07-b25b-5b1022357555\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>child_mlu</th>\n",
       "      <th>percent_child_parents_mlu</th>\n",
       "      <th>child_mluw</th>\n",
       "      <th>percent_child_parents_mluw</th>\n",
       "      <th>child_ttr</th>\n",
       "      <th>percent_child_parents_ttr</th>\n",
       "      <th>child_ipsyn</th>\n",
       "      <th>percent_child_parents_ipsyn</th>\n",
       "      <th>child_num_words</th>\n",
       "      <th>perct_child_parents_words</th>\n",
       "      <th>...</th>\n",
       "      <th>pos_percent_sub#n</th>\n",
       "      <th>pos_percent_n:pt</th>\n",
       "      <th>pos_percent_pro:refl</th>\n",
       "      <th>pos_percent_un#part</th>\n",
       "      <th>pos_percent_L2</th>\n",
       "      <th>pos_percent_chi</th>\n",
       "      <th>pos_percent_pro:poss</th>\n",
       "      <th>pos_percent_n:let</th>\n",
       "      <th>pos_percent_bab</th>\n",
       "      <th>pos_percent_sing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.067</td>\n",
       "      <td>0.503</td>\n",
       "      <td>3.316</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.397</td>\n",
       "      <td>1.491</td>\n",
       "      <td>18</td>\n",
       "      <td>0.212</td>\n",
       "      <td>94</td>\n",
       "      <td>0.095</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.630</td>\n",
       "      <td>0.371</td>\n",
       "      <td>2.254</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.479</td>\n",
       "      <td>2.042</td>\n",
       "      <td>43</td>\n",
       "      <td>0.531</td>\n",
       "      <td>234</td>\n",
       "      <td>0.217</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.640</td>\n",
       "      <td>0.558</td>\n",
       "      <td>2.060</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.910</td>\n",
       "      <td>44</td>\n",
       "      <td>0.629</td>\n",
       "      <td>380</td>\n",
       "      <td>0.483</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.629</td>\n",
       "      <td>0.497</td>\n",
       "      <td>2.598</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.445</td>\n",
       "      <td>1.920</td>\n",
       "      <td>66</td>\n",
       "      <td>0.742</td>\n",
       "      <td>372</td>\n",
       "      <td>0.266</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.333</td>\n",
       "      <td>0.317</td>\n",
       "      <td>1.074</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.397</td>\n",
       "      <td>1.487</td>\n",
       "      <td>10</td>\n",
       "      <td>0.145</td>\n",
       "      <td>205</td>\n",
       "      <td>0.197</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 69 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c23226d1-3017-4b07-b25b-5b1022357555')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "   child_mlu  percent_child_parents_mlu  child_mluw  \\\n",
       "0      3.067                      0.503       3.316   \n",
       "1      2.630                      0.371       2.254   \n",
       "2      2.640                      0.558       2.060   \n",
       "3      3.629                      0.497       2.598   \n",
       "4      1.333                      0.317       1.074   \n",
       "\n",
       "   percent_child_parents_mluw  child_ttr  percent_child_parents_ttr  \\\n",
       "0                       0.694      0.397                      1.491   \n",
       "1                       0.396      0.479                      2.042   \n",
       "2                       0.549      0.274                      0.910   \n",
       "3                       0.431      0.445                      1.920   \n",
       "4                       0.331      0.397                      1.487   \n",
       "\n",
       "   child_ipsyn  percent_child_parents_ipsyn  child_num_words  \\\n",
       "0           18                        0.212               94   \n",
       "1           43                        0.531              234   \n",
       "2           44                        0.629              380   \n",
       "3           66                        0.742              372   \n",
       "4           10                        0.145              205   \n",
       "\n",
       "   perct_child_parents_words  ...  pos_percent_sub#n  pos_percent_n:pt  \\\n",
       "0                      0.095  ...              0.000             0.000   \n",
       "1                      0.217  ...              0.000             0.000   \n",
       "2                      0.483  ...              0.000             0.000   \n",
       "3                      0.266  ...              0.000             0.000   \n",
       "4                      0.197  ...              0.000             0.000   \n",
       "\n",
       "   pos_percent_pro:refl  pos_percent_un#part  pos_percent_L2  pos_percent_chi  \\\n",
       "0                 0.000                0.000           0.000            0.000   \n",
       "1                 0.000                0.000           0.000            0.000   \n",
       "2                 0.000                0.000           0.000            0.000   \n",
       "3                 0.000                0.000           0.000            0.000   \n",
       "4                 0.000                0.000           0.000            0.005   \n",
       "\n",
       "   pos_percent_pro:poss  pos_percent_n:let  pos_percent_bab  pos_percent_sing  \n",
       "0                 0.000              0.000            0.000             0.000  \n",
       "1                 0.000              0.000            0.000             0.000  \n",
       "2                 0.000              0.000            0.000             0.000  \n",
       "3                 0.003              0.000            0.000             0.000  \n",
       "4                 0.000              0.000            0.000             0.000  \n",
       "\n",
       "[5 rows x 69 columns]"
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       "      <th></th>\n",
       "      <th>child_mlu</th>\n",
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       "      <td>-0.614</td>\n",
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       "      <td>0.013</td>\n",
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       "      <td>-0.259</td>\n",
       "      <td>0.127</td>\n",
       "      <td>-1.090</td>\n",
       "      <td>-0.936</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.287</td>\n",
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       "   child_mlu  percent_child_parents_mlu  child_mluw  \\\n",
       "0      0.156                      0.010       0.915   \n",
       "1     -0.183                     -0.614      -0.077   \n",
       "2     -0.176                      0.268      -0.259   \n",
       "3      0.594                     -0.020       0.244   \n",
       "4     -1.194                     -0.871      -1.180   \n",
       "\n",
       "   percent_child_parents_mluw  child_ttr  percent_child_parents_ttr  \\\n",
       "0                       0.764     -0.428                     -0.274   \n",
       "1                      -0.541      0.013                      0.354   \n",
       "2                       0.127     -1.090                     -0.936   \n",
       "3                      -0.388     -0.168                      0.215   \n",
       "4                      -0.829     -0.430                     -0.279   \n",
       "\n",
       "   child_ipsyn  percent_child_parents_ipsyn  child_num_words  \\\n",
       "0       -1.029                       -1.107           -1.027   \n",
       "1       -0.048                       -0.040           -0.016   \n",
       "2       -0.009                        0.287            1.037   \n",
       "3        0.854                        0.665            0.980   \n",
       "4       -1.343                       -1.331           -0.226   \n",
       "\n",
       "   perct_child_parents_words  ...  pos_percent_sub#n  pos_percent_n:pt  \\\n",
       "0                     -0.804  ...              0.000            -0.333   \n",
       "1                     -0.351  ...              0.000            -0.333   \n",
       "2                      0.641  ...              0.000            -0.333   \n",
       "3                     -0.169  ...              0.000            -0.333   \n",
       "4                     -0.426  ...              0.000            -0.333   \n",
       "\n",
       "   pos_percent_pro:refl  pos_percent_un#part  pos_percent_L2  pos_percent_chi  \\\n",
       "0                -0.262               -0.186          -0.220           -0.420   \n",
       "1                -0.262               -0.186          -0.220           -0.420   \n",
       "2                -0.262               -0.186          -0.220           -0.420   \n",
       "3                -0.262               -0.186          -0.220           -0.420   \n",
       "4                -0.262               -0.186          -0.220            3.135   \n",
       "\n",
       "   pos_percent_pro:poss  pos_percent_n:let  pos_percent_bab  pos_percent_sing  \n",
       "0                -0.244              0.000           -0.237             0.000  \n",
       "1                -0.244              0.000           -0.237             0.000  \n",
       "2                -0.244              0.000           -0.237             0.000  \n",
       "3                 1.828              0.000           -0.237             0.000  \n",
       "4                -0.244              0.000           -0.237             0.000  \n",
       "\n",
       "[5 rows x 69 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define scaler object\n",
    "ss = StandardScaler(copy=True, with_mean=True, with_std=True)\n",
    "\n",
    "# fit to data and get scaled dataframe\n",
    "X_train_scaled = pd.DataFrame(data=ss.fit_transform(X_train), columns=X_train.columns)\n",
    "\n",
    "# inspect values\n",
    "display(\"X_train\", X_train.head(), \"X_train_scaled\", X_train_scaled.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "PTnOepxgTKce",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 742
    },
    "id": "PTnOepxgTKce",
    "outputId": "2fda0eba-4a1d-4109-a820-d6b4d5016869"
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>child_mlu</th>\n",
       "      <th>percent_child_parents_mlu</th>\n",
       "      <th>child_mluw</th>\n",
       "      <th>percent_child_parents_mluw</th>\n",
       "      <th>child_ttr</th>\n",
       "      <th>percent_child_parents_ttr</th>\n",
       "      <th>child_ipsyn</th>\n",
       "      <th>percent_child_parents_ipsyn</th>\n",
       "      <th>child_num_words</th>\n",
       "      <th>perct_child_parents_words</th>\n",
       "      <th>...</th>\n",
       "      <th>pos_percent_sub#n</th>\n",
       "      <th>pos_percent_n:pt</th>\n",
       "      <th>pos_percent_pro:refl</th>\n",
       "      <th>pos_percent_un#part</th>\n",
       "      <th>pos_percent_L2</th>\n",
       "      <th>pos_percent_chi</th>\n",
       "      <th>pos_percent_pro:poss</th>\n",
       "      <th>pos_percent_n:let</th>\n",
       "      <th>pos_percent_bab</th>\n",
       "      <th>pos_percent_sing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>...</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.866</td>\n",
       "      <td>0.501</td>\n",
       "      <td>2.337</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.476</td>\n",
       "      <td>1.731</td>\n",
       "      <td>44.233</td>\n",
       "      <td>0.543</td>\n",
       "      <td>236.267</td>\n",
       "      <td>0.311</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.306</td>\n",
       "      <td>0.215</td>\n",
       "      <td>1.088</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.893</td>\n",
       "      <td>25.919</td>\n",
       "      <td>0.304</td>\n",
       "      <td>140.931</td>\n",
       "      <td>0.273</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>0.012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.571</td>\n",
       "      <td>0.322</td>\n",
       "      <td>1.353</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.397</td>\n",
       "      <td>1.148</td>\n",
       "      <td>22.750</td>\n",
       "      <td>0.282</td>\n",
       "      <td>139.250</td>\n",
       "      <td>0.153</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.853</td>\n",
       "      <td>0.506</td>\n",
       "      <td>2.437</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.455</td>\n",
       "      <td>1.552</td>\n",
       "      <td>49.500</td>\n",
       "      <td>0.623</td>\n",
       "      <td>220.000</td>\n",
       "      <td>0.224</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.774</td>\n",
       "      <td>0.638</td>\n",
       "      <td>3.165</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.517</td>\n",
       "      <td>2.028</td>\n",
       "      <td>63.250</td>\n",
       "      <td>0.752</td>\n",
       "      <td>362.750</td>\n",
       "      <td>0.415</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.448</td>\n",
       "      <td>1.049</td>\n",
       "      <td>4.023</td>\n",
       "      <td>0.978</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.445</td>\n",
       "      <td>81.000</td>\n",
       "      <td>0.931</td>\n",
       "      <td>462.000</td>\n",
       "      <td>1.089</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 69 columns</p>\n",
       "</div>\n",
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      ],
      "text/plain": [
       "       child_mlu  percent_child_parents_mlu  child_mluw  \\\n",
       "count     30.000                     30.000      30.000   \n",
       "mean       2.866                      0.501       2.337   \n",
       "std        1.306                      0.215       1.088   \n",
       "min        1.000                      0.198       0.000   \n",
       "25%        1.571                      0.322       1.353   \n",
       "50%        2.853                      0.506       2.437   \n",
       "75%        3.774                      0.638       3.165   \n",
       "max        5.448                      1.049       4.023   \n",
       "\n",
       "       percent_child_parents_mluw  child_ttr  percent_child_parents_ttr  \\\n",
       "count                      30.000     30.000                     30.000   \n",
       "mean                        0.520      0.476                      1.731   \n",
       "std                         0.232      0.189                      0.893   \n",
       "min                         0.000      0.000                      0.000   \n",
       "25%                         0.321      0.397                      1.148   \n",
       "50%                         0.532      0.455                      1.552   \n",
       "75%                         0.675      0.517                      2.028   \n",
       "max                         0.978      1.000                      4.445   \n",
       "\n",
       "       child_ipsyn  percent_child_parents_ipsyn  child_num_words  \\\n",
       "count       30.000                       30.000           30.000   \n",
       "mean        44.233                        0.543          236.267   \n",
       "std         25.919                        0.304          140.931   \n",
       "min          0.000                        0.000            3.000   \n",
       "25%         22.750                        0.282          139.250   \n",
       "50%         49.500                        0.623          220.000   \n",
       "75%         63.250                        0.752          362.750   \n",
       "max         81.000                        0.931          462.000   \n",
       "\n",
       "       perct_child_parents_words  ...  pos_percent_sub#n  pos_percent_n:pt  \\\n",
       "count                     30.000  ...             30.000            30.000   \n",
       "mean                       0.311  ...              0.000             0.001   \n",
       "std                        0.273  ...              0.000             0.002   \n",
       "min                        0.012  ...              0.000             0.000   \n",
       "25%                        0.153  ...              0.000             0.000   \n",
       "50%                        0.224  ...              0.000             0.000   \n",
       "75%                        0.415  ...              0.000             0.000   \n",
       "max                        1.089  ...              0.000             0.007   \n",
       "\n",
       "       pos_percent_pro:refl  pos_percent_un#part  pos_percent_L2  \\\n",
       "count                30.000               30.000          30.000   \n",
       "mean                  0.000                0.000           0.002   \n",
       "std                   0.001                0.001           0.008   \n",
       "min                   0.000                0.000           0.000   \n",
       "25%                   0.000                0.000           0.000   \n",
       "50%                   0.000                0.000           0.000   \n",
       "75%                   0.000                0.000           0.000   \n",
       "max                   0.003                0.003           0.042   \n",
       "\n",
       "       pos_percent_chi  pos_percent_pro:poss  pos_percent_n:let  \\\n",
       "count           30.000                30.000             30.000   \n",
       "mean             0.001                 0.000              0.000   \n",
       "std              0.001                 0.001              0.000   \n",
       "min              0.000                 0.000              0.000   \n",
       "25%              0.000                 0.000              0.000   \n",
       "50%              0.000                 0.000              0.000   \n",
       "75%              0.000                 0.000              0.000   \n",
       "max              0.005                 0.007              0.000   \n",
       "\n",
       "       pos_percent_bab  pos_percent_sing  \n",
       "count           30.000            30.000  \n",
       "mean             0.003             0.000  \n",
       "std              0.014             0.000  \n",
       "min              0.000             0.000  \n",
       "25%              0.000             0.000  \n",
       "50%              0.000             0.000  \n",
       "75%              0.000             0.000  \n",
       "max              0.075             0.000  \n",
       "\n",
       "[8 rows x 69 columns]"
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       "      <th></th>\n",
       "      <th>child_mlu</th>\n",
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       "      <td>0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>1.017</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.017</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.453</td>\n",
       "      <td>-1.430</td>\n",
       "      <td>-2.185</td>\n",
       "      <td>-2.279</td>\n",
       "      <td>-2.564</td>\n",
       "      <td>-1.973</td>\n",
       "      <td>-1.736</td>\n",
       "      <td>-1.815</td>\n",
       "      <td>-1.683</td>\n",
       "      <td>-1.114</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>-0.262</td>\n",
       "      <td>-0.186</td>\n",
       "      <td>-0.220</td>\n",
       "      <td>-0.420</td>\n",
       "      <td>-0.244</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.237</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.008</td>\n",
       "      <td>-0.844</td>\n",
       "      <td>-0.920</td>\n",
       "      <td>-0.870</td>\n",
       "      <td>-0.430</td>\n",
       "      <td>-0.664</td>\n",
       "      <td>-0.843</td>\n",
       "      <td>-0.871</td>\n",
       "      <td>-0.700</td>\n",
       "      <td>-0.588</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>-0.262</td>\n",
       "      <td>-0.186</td>\n",
       "      <td>-0.220</td>\n",
       "      <td>-0.420</td>\n",
       "      <td>-0.244</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.237</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.053</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>-0.204</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.266</td>\n",
       "      <td>-0.117</td>\n",
       "      <td>-0.322</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>-0.262</td>\n",
       "      <td>-0.186</td>\n",
       "      <td>-0.220</td>\n",
       "      <td>-0.420</td>\n",
       "      <td>-0.244</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.237</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.707</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.389</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.333</td>\n",
       "      <td>-0.262</td>\n",
       "      <td>-0.186</td>\n",
       "      <td>-0.220</td>\n",
       "      <td>-0.420</td>\n",
       "      <td>-0.244</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.237</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.011</td>\n",
       "      <td>2.586</td>\n",
       "      <td>1.576</td>\n",
       "      <td>2.010</td>\n",
       "      <td>2.817</td>\n",
       "      <td>3.092</td>\n",
       "      <td>1.443</td>\n",
       "      <td>1.298</td>\n",
       "      <td>1.629</td>\n",
       "      <td>2.900</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.102</td>\n",
       "      <td>4.416</td>\n",
       "      <td>5.385</td>\n",
       "      <td>5.276</td>\n",
       "      <td>3.135</td>\n",
       "      <td>4.999</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.113</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 69 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1a7127b2-c459-4292-a450-b1df7c666a5a')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-1a7127b2-c459-4292-a450-b1df7c666a5a button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-1a7127b2-c459-4292-a450-b1df7c666a5a');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "       child_mlu  percent_child_parents_mlu  child_mluw  \\\n",
       "count     30.000                     30.000      30.000   \n",
       "mean      -0.000                     -0.000       0.000   \n",
       "std        1.017                      1.017       1.017   \n",
       "min       -1.453                     -1.430      -2.185   \n",
       "25%       -1.008                     -0.844      -0.920   \n",
       "50%       -0.010                      0.023       0.094   \n",
       "75%        0.707                      0.646       0.774   \n",
       "max        2.011                      2.586       1.576   \n",
       "\n",
       "       percent_child_parents_mluw  child_ttr  percent_child_parents_ttr  \\\n",
       "count                      30.000     30.000                     30.000   \n",
       "mean                       -0.000     -0.000                     -0.000   \n",
       "std                         1.017      1.017                      1.017   \n",
       "min                        -2.279     -2.564                     -1.973   \n",
       "25%                        -0.870     -0.430                     -0.664   \n",
       "50%                         0.053     -0.115                     -0.204   \n",
       "75%                         0.681      0.216                      0.338   \n",
       "max                         2.010      2.817                      3.092   \n",
       "\n",
       "       child_ipsyn  percent_child_parents_ipsyn  child_num_words  \\\n",
       "count       30.000                       30.000           30.000   \n",
       "mean        -0.000                        0.000           -0.000   \n",
       "std          1.017                        1.017            1.017   \n",
       "min         -1.736                       -1.815           -1.683   \n",
       "25%         -0.843                       -0.871           -0.700   \n",
       "50%          0.207                        0.266           -0.117   \n",
       "75%          0.746                        0.701            0.913   \n",
       "max          1.443                        1.298            1.629   \n",
       "\n",
       "       perct_child_parents_words  ...  pos_percent_sub#n  pos_percent_n:pt  \\\n",
       "count                     30.000  ...             30.000            30.000   \n",
       "mean                      -0.000  ...              0.000             0.000   \n",
       "std                        1.017  ...              0.000             1.017   \n",
       "min                       -1.114  ...              0.000            -0.333   \n",
       "25%                       -0.588  ...              0.000            -0.333   \n",
       "50%                       -0.322  ...              0.000            -0.333   \n",
       "75%                        0.389  ...              0.000            -0.333   \n",
       "max                        2.900  ...              0.000             3.102   \n",
       "\n",
       "       pos_percent_pro:refl  pos_percent_un#part  pos_percent_L2  \\\n",
       "count                30.000               30.000          30.000   \n",
       "mean                 -0.000               -0.000          -0.000   \n",
       "std                   1.017                1.017           1.017   \n",
       "min                  -0.262               -0.186          -0.220   \n",
       "25%                  -0.262               -0.186          -0.220   \n",
       "50%                  -0.262               -0.186          -0.220   \n",
       "75%                  -0.262               -0.186          -0.220   \n",
       "max                   4.416                5.385           5.276   \n",
       "\n",
       "       pos_percent_chi  pos_percent_pro:poss  pos_percent_n:let  \\\n",
       "count           30.000                30.000             30.000   \n",
       "mean            -0.000                 0.000              0.000   \n",
       "std              1.017                 1.017              0.000   \n",
       "min             -0.420                -0.244              0.000   \n",
       "25%             -0.420                -0.244              0.000   \n",
       "50%             -0.420                -0.244              0.000   \n",
       "75%             -0.420                -0.244              0.000   \n",
       "max              3.135                 4.999              0.000   \n",
       "\n",
       "       pos_percent_bab  pos_percent_sing  \n",
       "count           30.000            30.000  \n",
       "mean             0.000             0.000  \n",
       "std              1.017             0.000  \n",
       "min             -0.237             0.000  \n",
       "25%             -0.237             0.000  \n",
       "50%             -0.237             0.000  \n",
       "75%             -0.237             0.000  \n",
       "max              5.113             0.000  \n",
       "\n",
       "[8 rows x 69 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inspect statistics\n",
    "display(\"X_train\", X_train.describe(), \"X_train_scaled\", X_train_scaled.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c36cf823-ee69-4afa-a430-dc809bd263b6",
   "metadata": {
    "id": "c36cf823-ee69-4afa-a430-dc809bd263b6"
   },
   "source": [
    "## 8.4 - Discretizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fec98c8-db17-4abc-a24e-0b57726b6711",
   "metadata": {
    "id": "2fec98c8-db17-4abc-a24e-0b57726b6711"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69006d8f-a1eb-4ae6-aa01-f60bb4ecfd1d",
   "metadata": {
    "id": "69006d8f-a1eb-4ae6-aa01-f60bb4ecfd1d"
   },
   "source": [
    "## 8.5 - Categorical encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b596036e-34f3-4d44-85a2-2b01a0d196d7",
   "metadata": {
    "id": "b596036e-34f3-4d44-85a2-2b01a0d196d7"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "803cccee-e385-4233-96dc-000a3d700055",
   "metadata": {
    "id": "803cccee-e385-4233-96dc-000a3d700055"
   },
   "source": [
    "## 8.6 - Time-cyclic variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb31afca-dd5d-4ba5-a9a5-bfda6581d119",
   "metadata": {
    "id": "cb31afca-dd5d-4ba5-a9a5-bfda6581d119"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be7d3971-e833-4d77-826d-5b5f1e9577e1",
   "metadata": {
    "id": "be7d3971-e833-4d77-826d-5b5f1e9577e1"
   },
   "source": [
    "## 8.7 - Response Variable Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edde216a-bdd1-40c8-b845-bfa071c717d5",
   "metadata": {
    "id": "edde216a-bdd1-40c8-b845-bfa071c717d5"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b6c368b-f121-4435-af4a-35443eaefaea",
   "metadata": {
    "id": "5b6c368b-f121-4435-af4a-35443eaefaea"
   },
   "source": [
    "## 8.8 - Cyclic time variables transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cb0b67f-ab13-4ea2-a50c-6e1dbd5484c5",
   "metadata": {
    "id": "1cb0b67f-ab13-4ea2-a50c-6e1dbd5484c5"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "588e7283-4f58-4f67-8df7-16bb365b1d52",
   "metadata": {
    "id": "588e7283-4f58-4f67-8df7-16bb365b1d52"
   },
   "source": [
    "## 8.9 - sklearn.pipelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "240fc558-49ec-4039-9cad-ceb3807eb396",
   "metadata": {
    "id": "240fc558-49ec-4039-9cad-ceb3807eb396"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a14f2e77-fd3d-4646-947b-cd94f7f35c62",
   "metadata": {
    "id": "a14f2e77-fd3d-4646-947b-cd94f7f35c62"
   },
   "source": [
    "## 8.10 - Final check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "770fb098-fd43-4fd3-9f20-479f6486d583",
   "metadata": {
    "id": "770fb098-fd43-4fd3-9f20-479f6486d583"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ceb412f-4110-4f90-909e-1278e7eb3772",
   "metadata": {
    "id": "5ceb412f-4110-4f90-909e-1278e7eb3772"
   },
   "source": [
    "# 9 - Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c97dd7e0-4f43-46a4-968c-c921b5bb75f6",
   "metadata": {
    "id": "c97dd7e0-4f43-46a4-968c-c921b5bb75f6"
   },
   "source": [
    "*What will be the model output?* \n",
    "- Label or probability?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "631635f9-1cc5-4b41-8733-a71bc8cb1c9b",
   "metadata": {
    "id": "631635f9-1cc5-4b41-8733-a71bc8cb1c9b"
   },
   "source": [
    "*What will be the main model metric? And the auxiliary metrics?*\n",
    "- Accuracy\n",
    "- Recall\n",
    "- Precision\n",
    "- F1-score\n",
    "- ROC-AUC\n",
    "- Precision-recall AUC\n",
    "- Cross Entropy (log-loss)\n",
    "- confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c99876b-6daf-4bd2-be4d-181eec1ede1f",
   "metadata": {
    "id": "7c99876b-6daf-4bd2-be4d-181eec1ede1f"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d947c95-2cec-46ab-babc-314ec66a3a9f",
   "metadata": {
    "id": "3d947c95-2cec-46ab-babc-314ec66a3a9f"
   },
   "source": [
    "# 10 - Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2418583-4477-4e72-8f50-6daa24fbd076",
   "metadata": {
    "id": "c2418583-4477-4e72-8f50-6daa24fbd076"
   },
   "source": [
    "*Select features for modelling*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a735c8-3104-40ed-aae5-e6920188099d",
   "metadata": {
    "id": "c2a735c8-3104-40ed-aae5-e6920188099d"
   },
   "source": [
    "## 10.1 - Feature coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "285382b0-e60c-4f09-bab0-37d57dac1ff1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "285382b0-e60c-4f09-bab0-37d57dac1ff1",
    "outputId": "37fb2e9e-d2bb-4de3-ba1d-377e2a8ac06d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000, n_jobs=-1)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instanciate LR\n",
    "lr = LogisticRegression(n_jobs=-1, max_iter=1000)\n",
    "\n",
    "# fit model to data\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "VJiGfLKkcqda",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 894
    },
    "id": "VJiGfLKkcqda",
    "outputId": "7b2c3a1a-10e1-4070-b339-ec794b38820e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a dataframe with feature importances\n",
    "df_f_coef = pd.DataFrame(data=lr.coef_, columns=X_train.columns).T\n",
    "# reset index and rename column\n",
    "df_f_coef = df_f_coef.reset_index()\n",
    "df_f_coef = df_f_coef.rename(columns={\"index\":\"features\", 0:\"feature_coef\"})\n",
    "# create a new column with absolute value of coef\n",
    "df_f_coef[\"abs_coef\"] = df_f_coef[\"feature_coef\"].apply(lambda x: abs(x))\n",
    "# define column names\n",
    "df_f_coef = df_f_coef.sort_values(by=\"feature_coef\", ascending=False, ignore_index=True)\n",
    "\n",
    "# define figure size\n",
    "fig = plt.figure(figsize=(10, 15))\n",
    "\n",
    "# plot\n",
    "sns.barplot(data=df_f_coef, x=\"feature_coef\", y=\"features\")\n",
    "# define details\n",
    "plt.title(\"Feature importance according to Logistic Regression\", loc=\"center\")\n",
    "plt.axvline(-0.1, linestyle=\":\", color=\"black\")\n",
    "plt.axvline(0.1, linestyle=\":\", color=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Z0ZFLSoBd-lM",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z0ZFLSoBd-lM",
    "outputId": "38d174f5-e050-4f46-d9ad-af9f8a3fd2e6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['child_mluw',\n",
       " 'pos_percent_nan',\n",
       " 'perct_child_parents_utterances',\n",
       " 'percent_child_parents_ttr',\n",
       " 'child_ttr']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get features whose abs coef is > 0.1\n",
    "f_selected_lr = df_f_coef.loc[df_f_coef[\"abs_coef\"] > 0.1, \"features\"].tolist()\n",
    "f_selected_lr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aeb1a4f-aeab-4bd0-919f-18f2c049e547",
   "metadata": {
    "id": "8aeb1a4f-aeab-4bd0-919f-18f2c049e547"
   },
   "source": [
    "## 10.2 - Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdefa8b2-93a3-4089-a56d-c84a5901fa2d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cdefa8b2-93a3-4089-a56d-c84a5901fa2d",
    "outputId": "d89ceb6d-9e82-433b-edc9-e5244b031700"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_jobs=-1)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define model\n",
    "rf = RandomForestClassifier(n_estimators=100, n_jobs=-1, class_weight=None)\n",
    "\n",
    "# fit model to data\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "KnvnV66_admB",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 894
    },
    "id": "KnvnV66_admB",
    "outputId": "d901343b-50aa-4a3d-e461-cbb56fd2e97f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a dataframe with feature importances\n",
    "df_f_importance = pd.DataFrame(data=rf.feature_importances_, index=X_train.columns)\n",
    "# reset index and rename column\n",
    "df_f_importance = df_f_importance.reset_index()\n",
    "df_f_importance = df_f_importance.rename(columns={\"index\":\"features\", 0:\"feature_importance\"})\n",
    "# define column names\n",
    "df_f_importance = df_f_importance.sort_values(by=\"feature_importance\", ascending=False)\n",
    "\n",
    "# define figure size\n",
    "fig = plt.figure(figsize=(10, 15))\n",
    "\n",
    "# plot\n",
    "sns.barplot(data=df_f_importance, x=\"feature_importance\", y=\"features\")\n",
    "# define details\n",
    "plt.title(\"Feature importance according to Random Forest\", loc=\"center\")\n",
    "plt.axvline(0.03, linestyle=\":\", color=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5hczqSXFfIR1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5hczqSXFfIR1",
    "outputId": "a678a71e-847a-44f4-8e6b-ce3ee9a7fb7f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pos_percent_det:num',\n",
       " 'child_ttr',\n",
       " 'perct_child_parents_words',\n",
       " 'pos_percent_?',\n",
       " 'child_mlu',\n",
       " 'child_num_unique_words',\n",
       " 'perct_child_parents_utterances',\n",
       " 'perct_child_parents_tokens']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get features whose feature_importance is > 0.03\n",
    "f_selected_rf = df_f_importance.loc[df_f_importance[\"feature_importance\"] > 0.03, \"features\"].tolist()\n",
    "f_selected_rf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cea71bbf-55c8-48d8-b502-957ae0a89ded",
   "metadata": {
    "id": "cea71bbf-55c8-48d8-b502-957ae0a89ded"
   },
   "source": [
    "## 10.3 - Buruta algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8afCenPLX8Cd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8afCenPLX8Cd",
    "outputId": "5f80eaac-55e7-4b2b-8d20-6f33f608be07"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting Boruta\n",
      "  Downloading Boruta-0.3-py3-none-any.whl (56 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.6/56.6 KB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.8/dist-packages (from Boruta) (1.7.3)\n",
      "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.8/dist-packages (from Boruta) (1.21.6)\n",
      "Requirement already satisfied: scikit-learn>=0.17.1 in /usr/local/lib/python3.8/dist-packages (from Boruta) (1.0.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.17.1->Boruta) (3.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.17.1->Boruta) (1.2.0)\n",
      "Installing collected packages: Boruta\n",
      "Successfully installed Boruta-0.3\n"
     ]
    }
   ],
   "source": [
    "# install boruta\n",
    "!pip install Boruta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcde15d8-5020-434b-82dd-b74f3bd49c32",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dcde15d8-5020-434b-82dd-b74f3bd49c32",
    "outputId": "690b6a4f-71ab-4a8c-cd5b-db5c4a5f1a20"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*************************************************\n",
      "\n",
      "Number of selected features: 0\n",
      "\n",
      "*************************************************\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# import required libraries\n",
    "from boruta import BorutaPy\n",
    "\n",
    "# define model\n",
    "rf = RandomForestClassifier(n_estimators=100, n_jobs=-1, class_weight=None)\n",
    "\n",
    "# define Boruta feature selection method\n",
    "feat_selector = BorutaPy(rf, n_estimators='auto', verbose=0, random_state=seed)\n",
    "\n",
    "# find all relevant features - 5 features should be selected\n",
    "feat_selector.fit(X_train.values, y_train.values)\n",
    "\n",
    "# get number of selected features\n",
    "print(\n",
    "    f\"\\n{'*'*49}\\n\\n\"\n",
    "    f\"Number of selected features: {feat_selector.n_features_}\"\n",
    "    f\"\\n\\n{'*'*49}\\n\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hY6CtouO0pI7",
   "metadata": {
    "id": "hY6CtouO0pI7"
   },
   "source": [
    "## 10.4 - Mutual Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "vE00YvHP0n6A",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 879
    },
    "id": "vE00YvHP0n6A",
    "outputId": "b3409ccc-2652-4f5c-bbe1-755805c931b5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate mutual information between features and target\n",
    "mutual_info = mutual_info_classif(X_train, y_train)\n",
    "\n",
    "# create a series from mutual information\n",
    "mutual_info_series = pd.Series(mutual_info)\n",
    "# add column labels as index\n",
    "mutual_info_series.index=X_train.columns\n",
    "# sort in descending order\n",
    "mutual_info_series = mutual_info_series.sort_values(ascending=False)\n",
    "\n",
    "# define figure size\n",
    "fig = plt.figure(figsize=(10, 15))\n",
    "\n",
    "# plot\n",
    "sns.barplot(y=mutual_info_series.index, x=mutual_info_series)\n",
    "# define details\n",
    "plt.title(\"Mutual information per feature\", loc=\"center\")\n",
    "plt.axvline(0.08, linestyle=\":\", color=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "uXopF_wYgKjg",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uXopF_wYgKjg",
    "outputId": "06ea9d7c-567f-4593-8fe8-1692a96c06c0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pos_percent_det:num',\n",
       " 'pos_percent_pro:exist',\n",
       " 'child_utterances',\n",
       " 'perct_child_parents_utterances',\n",
       " 'pos_percent_adv:tem',\n",
       " 'pos_percent_sing',\n",
       " 'child_ttr',\n",
       " 'pos_percent_?',\n",
       " 'child_num_words',\n",
       " 'pos_percent_mid#n',\n",
       " 'pos_percent_pro:poss',\n",
       " 'pos_percent_pro:int']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get features whose mutual_info_series > 0.075\n",
    "f_selected_mi = mutual_info_series[mutual_info_series > 0.08].index.tolist()\n",
    "f_selected_mi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "XN0nV9M7iTwn",
   "metadata": {
    "id": "XN0nV9M7iTwn"
   },
   "source": [
    "## 10.5 - Final feaures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "rNy64-8AiXSP",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rNy64-8AiXSP",
    "outputId": "9ab0f313-b4f5-4fd1-de10-f4856c515fbe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*************************************************\n",
      "\n",
      "Number of feature with OR statement between feature selection methods: 19\n",
      "\n",
      "*************************************************\n",
      "\n",
      "Number of feature with AND statement between feature selection methods: 2\n",
      "Selected features are: ['child_ttr', 'perct_child_parents_utterances']\n",
      "\n",
      "*************************************************\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# define feature with OR statement\n",
    "f_or = list((set(f_selected_mi + f_selected_rf + f_selected_lr)))\n",
    "# print report\n",
    "print(\n",
    "    f'\\n{\"*\"*49}\\n\\n'\n",
    "    f'Number of feature with OR statement between feature selection methods: {len(f_or)}\\n'\n",
    ")\n",
    "\n",
    "# define feature with OR statement\n",
    "f_and = list(set(f_selected_mi) & set(f_selected_rf) & set(f_selected_lr))\n",
    "# print report\n",
    "print(\n",
    "    f'{\"*\"*49}\\n\\n'\n",
    "    f'Number of feature with AND statement between feature selection methods: {len(f_and)}\\n'\n",
    "    f'Selected features are: {f_and}'\n",
    "    f'\\n\\n{\"*\"*49}\\n'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Pyludt2OIf8P",
   "metadata": {
    "id": "Pyludt2OIf8P"
   },
   "source": [
    "# 11 - Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2883f546-fadd-4a99-be81-5b6876dd454f",
   "metadata": {
    "id": "2883f546-fadd-4a99-be81-5b6876dd454f"
   },
   "source": [
    "## 11.1 - Dummy model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f6236d6-bb22-4505-ad3b-2dfbefaf1ad6",
   "metadata": {
    "id": "7f6236d6-bb22-4505-ad3b-2dfbefaf1ad6"
   },
   "source": [
    "*The easiest model without any coding*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f781061-9519-4ab5-9519-ec2821e94278",
   "metadata": {
    "id": "5f781061-9519-4ab5-9519-ec2821e94278"
   },
   "source": [
    "## 11.2 - Baseline model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d0c39ac-4691-4729-aacd-f881ae933225",
   "metadata": {
    "id": "6d0c39ac-4691-4729-aacd-f881ae933225"
   },
   "source": [
    "*The fastest model to build*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92df00d1-cfd4-423b-bc67-518b5db4e62f",
   "metadata": {
    "id": "92df00d1-cfd4-423b-bc67-518b5db4e62f"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab1f97b-de15-41a3-b779-2047cab5fcb6",
   "metadata": {
    "id": "0ab1f97b-de15-41a3-b779-2047cab5fcb6"
   },
   "source": [
    "## 11.3 - Best model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2001d4b8-947b-4fe9-aa6a-54d3fa6ea77a",
   "metadata": {
    "id": "2001d4b8-947b-4fe9-aa6a-54d3fa6ea77a"
   },
   "source": [
    "*The best model of all*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14adb39c-05ea-4bf8-8306-db33cb12562f",
   "metadata": {
    "id": "14adb39c-05ea-4bf8-8306-db33cb12562f"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be1b21e-5616-4337-bbc7-e8cccf4cdf65",
   "metadata": {
    "id": "3be1b21e-5616-4337-bbc7-e8cccf4cdf65"
   },
   "source": [
    "# 12 - Hyper-parameter tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3a6998a-17b0-4a18-95ca-cf8858760224",
   "metadata": {
    "id": "b3a6998a-17b0-4a18-95ca-cf8858760224"
   },
   "source": [
    "*Find out the best hyper-parameters for the ML models*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "443ba924-01fa-4827-834b-a071b2eab686",
   "metadata": {
    "id": "443ba924-01fa-4827-834b-a071b2eab686"
   },
   "source": [
    "*What strategy to use*\n",
    "- Grid Search\n",
    "- Random search\n",
    "- Bayesian search\n",
    "- Optuna"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "661330c4-6161-4274-b64e-2ee28c2598a2",
   "metadata": {
    "id": "661330c4-6161-4274-b64e-2ee28c2598a2"
   },
   "source": [
    "*How ot keep track of model performances?*\n",
    "- mlflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cddfb773-f38e-4aa0-b009-f5da9ad55ee7",
   "metadata": {
    "id": "cddfb773-f38e-4aa0-b009-f5da9ad55ee7"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3b1a043-43a4-42d5-9ad9-2d7a01e2f7df",
   "metadata": {
    "id": "b3b1a043-43a4-42d5-9ad9-2d7a01e2f7df"
   },
   "source": [
    "# 13 - Error Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "241ac66b-4015-4fa2-a2f4-c8f4567b6b74",
   "metadata": {
    "id": "241ac66b-4015-4fa2-a2f4-c8f4567b6b74"
   },
   "source": [
    "*Perform error analysis to check model performance*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65162d91-bd44-4df5-96e2-b7185e8aabc9",
   "metadata": {
    "id": "65162d91-bd44-4df5-96e2-b7185e8aabc9"
   },
   "source": [
    "## 13.1 - Cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa816705-ff8f-4be8-8170-9c842194c95f",
   "metadata": {
    "id": "aa816705-ff8f-4be8-8170-9c842194c95f"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "LG6XHx27rt3h",
   "metadata": {
    "id": "LG6XHx27rt3h"
   },
   "outputs": [],
   "source": [
    "def get_class_metrics(model, y_true, y_pred, y_pred_proba):\n",
    "    \"\"\"Get basic classification metrics.\n",
    "    \n",
    "    Args\n",
    "        model: the machine learning model\n",
    "        y_true: true label for the samples\n",
    "        y_pred: predicted classes for the samples (model.predict)\n",
    "        y_pred_proba: predicted probability for the samples (model.predict_proba)\n",
    "\n",
    "    Return\n",
    "        tuple with accuracy, precision, recall, f1, roc-auc, precision-recall-auc scores\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    # get threshold dependent metrics for the given model\n",
    "    accuracy = accuracy_score(y_true, y_pred)\n",
    "    precision = precision_score(y_true, y_pred, zero_division=0)\n",
    "    recall = recall_score(y_true, y_pred)\n",
    "    f1 = f1_score(y_true, y_pred)\n",
    "\n",
    "    # get probability dependent metrics for the given model\n",
    "    roc_auc = roc_auc_score(y_true, y_pred_proba)\n",
    "    precision_, recall_, thresholds = precision_recall_curve(y_true, y_pred_proba)\n",
    "    precision_recall_auc = auc(recall_, precision_)\n",
    "    log_loss_ = log_loss(y_true, y_pred_proba)\n",
    "\n",
    "    return accuracy, precision, recall, f1, roc_auc, precision_recall_auc, log_loss_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "lBSBXs8TrWDV",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lBSBXs8TrWDV",
    "outputId": "a0cca536-d1e2-42f3-d1a5-0db4929ed491"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of leave 2 out splits: 435\n",
      "Number of leave 1 out splits: 30\n"
     ]
    }
   ],
   "source": [
    "# instanciate Leave 2 Out object\n",
    "lpo = LeavePOut(2)\n",
    "\n",
    "# print information\n",
    "print(f'Number of leave 2 out splits: {len([True for _ in lpo.split(X_train_scaled)])}')\n",
    "\n",
    "# instanciate Leave P Out object\n",
    "lpo = LeavePOut(1)\n",
    "\n",
    "# print information\n",
    "print(f'Number of leave 1 out splits: {len([True for _ in lpo.split(X_train_scaled)])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67qfHiIfT_YD",
   "metadata": {
    "id": "67qfHiIfT_YD"
   },
   "outputs": [],
   "source": [
    "# # define dict to store metrics\n",
    "# df_metrics = pd.DataFrame()\n",
    "\n",
    "# # define possible features\n",
    "# f_selected_mi\n",
    "# f_selected_rf\n",
    "# f_selected_lr\n",
    "# f_or = list((set(f_selected_mi + f_selected_rf + f_selected_lr)))\n",
    "# f_and = list(set(f_selected_mi) & set(f_selected_rf) & set(f_selected_lr))\n",
    "\n",
    "# # define default models to test\n",
    "# dummy = DummyClassifier(strategy=\"most_frequent\", random_state=seed)\n",
    "# lr = LogisticRegression(n_jobs=-1, max_iter=1000, random_state=seed)\n",
    "# svm = SVC(probability=True, max_iter=1000, random_state=seed)\n",
    "# bayes = BernoulliNB()\n",
    "# knn = KNeighborsClassifier(n_jobs=-1)\n",
    "# rf = RandomForestClassifier(n_jobs=-1, random_state=seed)\n",
    "# lgbm = LGBMClassifier(n_jobs=-1, random_state=seed)\n",
    "# catboost = CatBoostClassifier(random_seed=seed, verbose=False)\n",
    "\n",
    "# # iterate over possible feature combinations\n",
    "# for selected_features, selected_features_label in zip(\n",
    "#     # available features selected\n",
    "#     [f_selected_mi, f_selected_rf  , f_selected_lr , f_or, f_and],\n",
    "#     # features selected labels\n",
    "#     [\"Mutual Info\", \"Random Forest\", \"Logistic Reg\",  \"OR\", \"AND\"]\n",
    "#     ):\n",
    "\n",
    "#     # filter training dataframe given the selected features\n",
    "#     X_train_scaled_f_selected = X_train_scaled[selected_features]\n",
    "\n",
    "#     # sanity check\n",
    "#     assert X_train_scaled_f_selected.shape[1] == len(selected_features), \"Error of feature selection filtering\"\n",
    "\n",
    "#     # instanciate Leave P Out object\n",
    "#     lpo = LeavePOut(2)\n",
    "\n",
    "#     # iterate over models and labels\n",
    "#     for ml_model, ml_label in zip(\n",
    "#         # available models will be the \"model\" iterator\n",
    "#         [ dummy ,  lr  ,  svm  ,  bayes  ,  knn  ,  rf  ,  lgbm , catboost ],\n",
    "#         # available labels will be \"label\" iterator\n",
    "#         [\"Dummy\", \"lr\" , \"svm\" , \"bayes\" , \"knn\" , \"rf\" , \"lgbm\", \"catboost\" ] \n",
    "#     ):\n",
    "\n",
    "#         # iterate over synthetic data generation models and their labels\n",
    "#         for synthetic_data_model, data_label in zip(\n",
    "#             # available models will be the \"model\" iterator\n",
    "#             [  None  , SMOTE(random_state=0, n_jobs=-1), ADASYN(random_state=0, n_jobs=-1), TomekLinks(n_jobs=-1), NearMiss(n_jobs=-1), RandomOverSampler(random_state=seed)],\n",
    "#             # available labels will be \"label\" iterator\n",
    "#             [\"Normal\",       \"SMOTE\"                   ,       \"ADASYN\"                   ,       \"Tomek\"        ,      \"NearMiss\"    ,         \"RandomOverSampling\"        ]\n",
    "#         ):\n",
    "\n",
    "#             # print report\n",
    "#             print(\n",
    "#                 f'Performing Leave2Out validation for {ml_label.upper()} model on {data_label.upper()} data with {selected_features_label.upper()} features...'\n",
    "#             )\n",
    "\n",
    "#             # get metrics for leave 2 out validation\n",
    "#             metrics_p_out = {\n",
    "#                 \"accuracy\": [],\n",
    "#                 \"precision\": [],\n",
    "#                 \"recall\": [],\n",
    "#                 \"f1\": [],\n",
    "#                 \"roc_auc\": [], \n",
    "#                 \"pr_auc\": []\n",
    "#                 }\n",
    "\n",
    "#             # iterate over leave 2 out splits\n",
    "#             for train_index, val_index in lpo.split(X_train_scaled_f_selected):\n",
    "#                 # sanity check\n",
    "#                 assert len(set(train_index).intersection(set(val_index))) == 0, \"Overlapping train and validation index!\"\n",
    "\n",
    "#                 # ===============================================================\n",
    "#                 # for the two validation samples, \n",
    "#                 # we will always use splits that have 1 ASD and another 1 non-ASD\n",
    "#                 # ---> sum of y values in validation must be 1\n",
    "#                 # ---> otherwise both are ASD or both are non-ASD\n",
    "#                 # ===============================================================\n",
    "\n",
    "#                 # check if all are ASD or all are non-ASD\n",
    "#                 if y_train[val_index].sum() != 1: \n",
    "#                     # skip to the next iteration\n",
    "#                     continue\n",
    "#                 # one is ASD and the other is non-ASD\n",
    "#                 else:\n",
    "#                     # get train and validation samples\n",
    "#                     X_train_p = X_train_scaled_f_selected.iloc[train_index, :]\n",
    "#                     X_val_p   = X_train_scaled_f_selected.iloc[ val_index , :]\n",
    "#                     y_train_p = y_train.iloc[train_index]\n",
    "#                     y_val_p   = y_train.iloc[ val_index ]\n",
    "\n",
    "#                 # check if we will use synthetic data\n",
    "#                 if data_label == \"Normal\":\n",
    "#                     # keep data as it is\n",
    "#                     X_train_modelling, y_train_modelling = X_train_p.copy(), y_train_p.copy()\n",
    "#                 # we will not use normal data\n",
    "#                 else:\n",
    "#                     # create modelling data\n",
    "#                     X_train_modelling, y_train_modelling = synthetic_data_model.fit_resample(X_train_p.copy(), y_train_p.copy())\n",
    "\n",
    "#                 # fit model to data\n",
    "#                 ml_model.fit(X_train_modelling, y_train_modelling)\n",
    "\n",
    "#                 # get classification metrics\n",
    "#                 accuracy, precision, recall, f1, roc_auc, pr_auc = get_class_metrics(\n",
    "#                     ml_model, \n",
    "#                     y_val_p, \n",
    "#                     ml_model.predict(X_val_p), \n",
    "#                     ml_model.predict_proba(X_val_p)[:, 1]\n",
    "#                     )\n",
    "                \n",
    "#                 # get metrics for the given model\n",
    "#                 metrics_p_out[\"accuracy\"].append(accuracy)\n",
    "#                 metrics_p_out[\"precision\"].append(precision)\n",
    "#                 metrics_p_out[\"recall\"].append(recall)\n",
    "#                 metrics_p_out[\"f1\"].append(f1)\n",
    "#                 metrics_p_out[\"roc_auc\"].append(roc_auc)\n",
    "#                 metrics_p_out[\"pr_auc\"].append(pr_auc)\n",
    "\n",
    "#             # create a dataframe with iteration metrics\n",
    "#             df_ = pd.DataFrame({\n",
    "#                 \"model\": ml_label,\n",
    "#                 \"data\": data_label,\n",
    "#                 \"features\": selected_features_label,\n",
    "#                 \"pr_auc\": np.mean(metrics_p_out[\"pr_auc\"]),\n",
    "#                 \"roc_auc\": np.mean(metrics_p_out[\"roc_auc\"]),\n",
    "#                 \"f1\": np.mean(metrics_p_out[\"f1\"]),\n",
    "#                 \"recall\": np.mean(metrics_p_out[\"recall\"]),\n",
    "#                 \"precision\": np.mean(metrics_p_out[\"precision\"]),\n",
    "#                 \"accuracy\": np.mean(metrics_p_out[\"accuracy\"])\n",
    "#             }, index=[0])\n",
    "\n",
    "#             # add metrics to df_metricvalues to \n",
    "#             df_metrics = pd.concat(objs=[df_metrics, df_], ignore_index=True)\n",
    "\n",
    "# # order values by best performance metrics\n",
    "# df_metrics = df_metrics.sort_values(\n",
    "#     by=[\"pr_auc\", \"f1\", \"roc_auc\", \"recall\", \"precision\", \"accuracy\"], \n",
    "#     ascending=[False, False, False, False, False, False], \n",
    "#     ignore_index=True\n",
    "# )\n",
    "\n",
    "# # save results to html\n",
    "# df_metrics.to_html(\n",
    "#     buf=\"metrics_experimentation_asd_predictor.html\", \n",
    "#     max_rows=df_metrics.shape[0], max_cols=25, justify=\"center\"\n",
    "#     )\n",
    "\n",
    "# # check results\n",
    "# pd.read_html(\"metrics_experimentation_asd_predictor.html\")[0].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "VPpgiFXiA5WM",
   "metadata": {
    "id": "VPpgiFXiA5WM"
   },
   "outputs": [],
   "source": [
    "def explore_features_models_synthetic_data(\n",
    "    X_train, \n",
    "    y_train, \n",
    "    zip_features_label, \n",
    "    zip_model_label, \n",
    "    zip_synthetic_data_label\n",
    "):\n",
    "\n",
    "    # unpack zips\n",
    "    zip_features_label = [*zip_features_label]\n",
    "    zip_model_label = [*zip_model_label]\n",
    "    zip_synthetic_data_label = [*zip_synthetic_data_label]\n",
    "\n",
    "    # define dict to store metrics\n",
    "    df_metrics = pd.DataFrame()\n",
    "\n",
    "    # iterate over possible feature combinations\n",
    "    for selected_features, selected_features_label in zip_features_label:\n",
    "\n",
    "        # filter training dataframe given the selected features\n",
    "        X_train_f_selected = X_train[selected_features]\n",
    "\n",
    "        # sanity check\n",
    "        assert X_train_f_selected.shape[1] == len(selected_features), \"Error of feature selection filtering\"\n",
    "\n",
    "        # instanciate Leave P Out object\n",
    "        lpo = LeavePOut(2)\n",
    "\n",
    "        # iterate over models and labels\n",
    "        for ml_model, ml_label in zip_model_label:\n",
    "\n",
    "            # iterate over synthetic data generation models and their labels\n",
    "            for synthetic_data_model, data_label in zip_synthetic_data_label:\n",
    "\n",
    "                # print report\n",
    "                print(\n",
    "                    f'Performing Leave2Out validation for {ml_label.upper()} model on {data_label.upper()} data with {selected_features_label.upper()} features...'\n",
    "                )\n",
    "\n",
    "                # get metrics for leave 2 out validation\n",
    "                metrics_p_out = {\n",
    "                    \"pr_auc\": [],\n",
    "                    \"roc_auc\": [], \n",
    "                    \"log_loss\": [],\n",
    "                    \"f1\": [],\n",
    "                    \"recall\": [],\n",
    "                    \"precision\": [],\n",
    "                    \"accuracy\": []\n",
    "                    }\n",
    "\n",
    "                # iterate over leave 2 out splits\n",
    "                for train_index, val_index in lpo.split(X_train_f_selected):\n",
    "                    # sanity check\n",
    "                    assert len(set(train_index).intersection(set(val_index))) == 0, \"Overlapping train and validation index!\"\n",
    "\n",
    "                    # ===============================================================\n",
    "                    # for the two validation samples, \n",
    "                    # we will always use splits that have 1 ASD and another 1 non-ASD\n",
    "                    # ---> sum of y values in validation must be 1\n",
    "                    # ---> otherwise both are ASD or both are non-ASD\n",
    "                    # ===============================================================\n",
    "\n",
    "                    # check if all are ASD or all are non-ASD\n",
    "                    if y_train[val_index].sum() != 1: \n",
    "                        # skip to the next iteration\n",
    "                        continue\n",
    "                    # one is ASD and the other is non-ASD\n",
    "                    else:\n",
    "                        # get train and validation samples\n",
    "                        X_train_p = X_train_f_selected.iloc[train_index, :]\n",
    "                        X_val_p   = X_train_f_selected.iloc[ val_index , :]\n",
    "                        y_train_p = y_train.iloc[train_index]\n",
    "                        y_val_p   = y_train.iloc[ val_index ]\n",
    "\n",
    "                    # check if we will use synthetic data\n",
    "                    if data_label == \"Normal\":\n",
    "                        # keep data as it is\n",
    "                        X_train_modelling, y_train_modelling = X_train_p.copy(), y_train_p.copy()\n",
    "                    # we will not use normal data\n",
    "                    else:\n",
    "                        # create modelling data\n",
    "                        X_train_modelling, y_train_modelling = synthetic_data_model.fit_resample(X_train_p.copy(), y_train_p.copy())\n",
    "\n",
    "                    # fit model to data\n",
    "                    ml_model.fit(X_train_modelling, y_train_modelling)\n",
    "\n",
    "                    # get classification metrics\n",
    "                    accuracy, precision, recall, f1, roc_auc, pr_auc, log_loss = get_class_metrics(\n",
    "                        ml_model, \n",
    "                        y_val_p, \n",
    "                        ml_model.predict(X_val_p), \n",
    "                        ml_model.predict_proba(X_val_p)[:, 1]\n",
    "                        )\n",
    "\n",
    "                    # get metrics for the given model\n",
    "                    metrics_p_out[\"accuracy\"].append(accuracy)\n",
    "                    metrics_p_out[\"precision\"].append(precision)\n",
    "                    metrics_p_out[\"recall\"].append(recall)\n",
    "                    metrics_p_out[\"f1\"].append(f1)\n",
    "                    metrics_p_out[\"roc_auc\"].append(roc_auc)\n",
    "                    metrics_p_out[\"pr_auc\"].append(pr_auc)\n",
    "                    metrics_p_out[\"log_loss\"].append(log_loss)\n",
    "\n",
    "                # create a dataframe with iteration metrics\n",
    "                df_ = pd.DataFrame({\n",
    "                    \"model\": ml_label,\n",
    "                    \"data\": data_label,\n",
    "                    \"features\": selected_features_label,\n",
    "                    \"pr_auc_mean\": np.mean(metrics_p_out[\"pr_auc\"]),\n",
    "                    \"roc_auc_mean\": np.mean(metrics_p_out[\"roc_auc\"]),\n",
    "                    \"log_loss_mean\": np.mean(metrics_p_out[\"log_loss\"]),\n",
    "                    \"f1_mean\": np.mean(metrics_p_out[\"f1\"]),\n",
    "                    \"recall_mean\": np.mean(metrics_p_out[\"recall\"]),\n",
    "                    \"precision_mean\": np.mean(metrics_p_out[\"precision\"]),\n",
    "                    \"accuracy_mean\": np.mean(metrics_p_out[\"accuracy\"]),\n",
    "                    \"pr_auc_std\": np.std(metrics_p_out[\"pr_auc\"], ddof=1),\n",
    "                    \"roc_auc_std\": np.std(metrics_p_out[\"roc_auc\"], ddof=1),\n",
    "                    \"log_loss_std\": np.std(metrics_p_out[\"log_loss\"], ddof=1),\n",
    "                    \"f1_std\": np.std(metrics_p_out[\"f1\"], ddof=1),\n",
    "                    \"recall_std\": np.std(metrics_p_out[\"recall\"], ddof=1),\n",
    "                    \"precision_std\": np.std(metrics_p_out[\"precision\"], ddof=1),\n",
    "                    \"accuracy_std\": np.std(metrics_p_out[\"accuracy\"], ddof=1)\n",
    "                }, index=[0])\n",
    "\n",
    "                # add metrics to df_metricvalues to \n",
    "                df_metrics = pd.concat(objs=[df_metrics, df_], ignore_index=True)\n",
    "\n",
    "    # order values by best performance metrics\n",
    "    df_metrics = df_metrics.sort_values(\n",
    "        by=[\"pr_auc_mean\", \"roc_auc_mean\", \"log_loss_mean\", \"f1_mean\", \"recall_mean\", \"precision_mean\", \"accuracy_mean\"], \n",
    "        ascending=[False, False, False, False, False, False, False], \n",
    "        ignore_index=True\n",
    "    )\n",
    "\n",
    "    return df_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "WZJF9fuWpzLk",
   "metadata": {
    "id": "WZJF9fuWpzLk"
   },
   "outputs": [],
   "source": [
    "# # define possible features\n",
    "# f_selected_mi\n",
    "# f_selected_rf\n",
    "# f_selected_lr\n",
    "# f_or = list((set(f_selected_mi + f_selected_rf + f_selected_lr)))\n",
    "# f_and = list(set(f_selected_mi) & set(f_selected_rf) & set(f_selected_lr))\n",
    "\n",
    "# # define possible features to test\n",
    "# zip_features_label = zip(\n",
    "#     # available features selected\n",
    "#     [f_selected_mi, f_selected_rf  , f_selected_lr , f_or, f_and],\n",
    "#     # features selected labels\n",
    "#     [\"Mutual Info\", \"Random Forest\", \"Logistic Reg\",  \"OR\", \"AND\"]\n",
    "#     )\n",
    "\n",
    "# # define possible models to test\n",
    "# dummy = DummyClassifier(strategy=\"most_frequent\", random_state=seed)\n",
    "# lr = LogisticRegression(n_jobs=-1, max_iter=1000, random_state=seed)\n",
    "# svm = SVC(probability=True, max_iter=1000, random_state=seed)\n",
    "# bayes = BernoulliNB()\n",
    "# knn = KNeighborsClassifier(n_jobs=-1)\n",
    "# rf = RandomForestClassifier(n_jobs=-1, random_state=seed)\n",
    "# lgbm = LGBMClassifier(n_jobs=-1, random_state=seed)\n",
    "# catboost = CatBoostClassifier(random_seed=seed, verbose=False)\n",
    "\n",
    "# zip_model_label = zip(\n",
    "#         # available models will be the \"model\" iterator\n",
    "#         [ dummy ,  lr  ,  svm  ,  bayes  ,  knn  ,  rf  ,  lgbm , catboost ],\n",
    "#         # available labels will be \"label\" iterator\n",
    "#         [\"Dummy\", \"lr\" , \"svm\" , \"bayes\" , \"knn\" , \"rf\" , \"lgbm\", \"catboost\" ] \n",
    "#     )\n",
    "\n",
    "# # define possible synthetic data generation techniques\n",
    "# zip_synthetic_data_label = zip(\n",
    "#             # available models will be the \"model\" iterator\n",
    "#             [  None  , SMOTE(random_state=0, n_jobs=-1), ADASYN(random_state=0, n_jobs=-1), TomekLinks(n_jobs=-1), NearMiss(n_jobs=-1), RandomOverSampler(random_state=seed)],\n",
    "#             # available labels will be \"label\" iterator\n",
    "#             [\"Normal\",       \"SMOTE\"                   ,       \"ADASYN\"                   ,       \"Tomek\"        ,      \"NearMiss\"    ,         \"RandomOverSampling\"        ]\n",
    "#         )\n",
    "\n",
    "# # make experiments\n",
    "# df_metrics = explore_features_models_synthetic_data(\n",
    "#     X_train=X_train_scaled, \n",
    "#     y_train=y_train, \n",
    "#     zip_features_label=zip_features_label, \n",
    "#     zip_model_label=zip_model_label, \n",
    "#     zip_synthetic_data_label=zip_synthetic_data_label\n",
    "#     )\n",
    "\n",
    "# # save results to html\n",
    "# df_metrics.to_html(\n",
    "#     buf=\"metrics_experimentation_asd_predictor.html\", \n",
    "#     max_rows=df_metrics.shape[0], max_cols=25, justify=\"center\"\n",
    "#     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eXZfG8lrCynW",
   "metadata": {
    "id": "eXZfG8lrCynW"
   },
   "outputs": [],
   "source": [
    "# # make experiments \n",
    "# df_metrics = explore_features_models_synthetic_data(\n",
    "#     X_train=X_train_scaled, \n",
    "#     y_train=y_train, \n",
    "#     zip_features_label=zip([f_selected_mi, f_or, f_and], [\"Mutual Info\", \"OR\", \"AND\"]), \n",
    "#     zip_model_label=zip([ DummyClassifier(strategy=\"most_frequent\", random_state=seed) ,  KNeighborsClassifier(n_jobs=-1)], [\"Dummy\", \"knn\"]), \n",
    "#     zip_synthetic_data_label=zip([  None  , ADASYN(random_state=0, n_jobs=-1), NearMiss(n_jobs=-1)], [\"Normal\", \"ADASYN\",\"NearMiss\"])\n",
    "#     )\n",
    "\n",
    "# # inspect results\n",
    "# df_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7011ae20-ebac-4cbd-8e9b-5de9064d5e0b",
   "metadata": {
    "id": "7011ae20-ebac-4cbd-8e9b-5de9064d5e0b"
   },
   "source": [
    "## 13.2 - Overfitting vs Wellfitting vs Underfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "mqE_7_7DNDyI",
   "metadata": {
    "id": "mqE_7_7DNDyI"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Va-hzxia89AL",
   "metadata": {
    "id": "Va-hzxia89AL"
   },
   "outputs": [],
   "source": [
    "def explore_stratified_cv(\n",
    "    X_train, \n",
    "    y_train, \n",
    "    zip_features_label, \n",
    "    zip_model_label, \n",
    "    zip_synthetic_data_label\n",
    "):\n",
    "\n",
    "    # unpack zips\n",
    "    zip_features_label = [*zip_features_label]\n",
    "    zip_model_label = [*zip_model_label]\n",
    "    zip_synthetic_data_label = [*zip_synthetic_data_label]\n",
    "\n",
    "    # define dict to store metrics\n",
    "    df_metrics = pd.DataFrame()\n",
    "\n",
    "    # iterate over possible feature combinations\n",
    "    for selected_features, selected_features_label in zip_features_label:\n",
    "\n",
    "        # filter training dataframe given the selected features\n",
    "        X_train_f_selected = X_train[selected_features]\n",
    "\n",
    "        # sanity check\n",
    "        assert X_train_f_selected.shape[1] == len(selected_features), \"Error of feature selection filtering\"\n",
    "\n",
    "        # instanciate Leave P Out object\n",
    "        lpo = LeavePOut(2)\n",
    "\n",
    "        # iterate over models and labels\n",
    "        for ml_model, ml_label in zip_model_label:\n",
    "\n",
    "            # iterate over synthetic data generation models and their labels\n",
    "            for synthetic_data_model, data_label in zip_synthetic_data_label:\n",
    "\n",
    "                # print report\n",
    "                print(\n",
    "                    f'Performing Leave2Out validation for {ml_label.upper()} model on {data_label.upper()} data with {selected_features_label.upper()} features...'\n",
    "                )\n",
    "\n",
    "                # check if we will use synthetic data\n",
    "                if data_label == \"Normal\":\n",
    "                    # keep data as it is\n",
    "                    X_train_modelling, y_train_modelling = X_train_f_selected.copy(), y_train.copy()\n",
    "                # we will not use normal data\n",
    "                else:\n",
    "                    # create modelling data\n",
    "                    X_train_modelling, y_train_modelling = synthetic_data_model.fit_resample(X_train_f_selected.copy(), y_train.copy())\n",
    "\n",
    "                # define metrics\n",
    "                metrics = [\"roc_auc\", \"f1\", \"recall\", \"precision\", \"accuracy\", \"neg_log_loss\"]\n",
    "\n",
    "                # cross validate model\n",
    "                cv_data = cross_validate(\n",
    "                    estimator=ml_model, \n",
    "                    X=X_train_modelling, \n",
    "                    y=y_train_modelling, \n",
    "                    # groups=None, \n",
    "                    scoring=metrics, \n",
    "                    cv=StratifiedKFold(n_splits=10, shuffle=True, random_state=seed), \n",
    "                    return_train_score=True,\n",
    "                    n_jobs=-1, \n",
    "                    verbose=0\n",
    "                    )\n",
    "\n",
    "                # iterate over metrics\n",
    "                for metric in metrics:\n",
    "                    # create a new key for division of test and train metric\n",
    "                    cv_data[f\"test_divided_train_{metric}\"] = cv_data[f\"test_{metric}\"] / cv_data[f\"train_{metric}\"]\n",
    "\n",
    "                # create a dict to store mean and std for k-fold metrics\n",
    "                cv_data_agg = {}\n",
    "\n",
    "                # iterate over cv_data dict\n",
    "                for k, v in cv_data.items():\n",
    "                    # check if key is not time\n",
    "                    if not k.endswith(\"_time\"):\n",
    "                        # create a mean and std for the given metric\n",
    "                        cv_data_agg[f\"mean_{k}\"] = np.mean(v)\n",
    "                        cv_data_agg[f\"std_{k}\"] = np.std(v, ddof=1)\n",
    "\n",
    "                # add metadata info to dict\n",
    "                cv_data_agg[\"model\"]=ml_label\n",
    "                cv_data_agg[\"data\"]=data_label\n",
    "                cv_data_agg[\"features\"]=selected_features_label\n",
    "\n",
    "                # create a dataframe with iteration metrics\n",
    "                df_ = pd.DataFrame(cv_data_agg, index=[0])\n",
    "\n",
    "                # add metrics to df_metricvalues to \n",
    "                df_metrics = pd.concat(objs=[df_metrics, df_], ignore_index=True)\n",
    "\n",
    "    # reorder columns\n",
    "    df_metrics = df_metrics[[\n",
    "        'model', 'data', 'features', \n",
    "\n",
    "        'mean_test_divided_train_roc_auc',\n",
    "        'mean_test_divided_train_f1', \n",
    "        'mean_test_divided_train_recall',      \n",
    "        'mean_test_divided_train_precision',\n",
    "        'mean_test_divided_train_accuracy',\n",
    "        'mean_test_divided_train_neg_log_loss',\n",
    "\n",
    "        'mean_test_roc_auc', 'std_test_roc_auc',\n",
    "        'mean_train_roc_auc', 'std_train_roc_auc', \n",
    "        'mean_test_f1', 'std_test_f1', \n",
    "        'mean_train_f1', 'std_train_f1', \n",
    "        'mean_test_recall','std_test_recall', \n",
    "        'mean_train_recall', 'std_train_recall',\n",
    "        'mean_test_precision', 'std_test_precision', \n",
    "        'mean_train_precision', 'std_train_precision', \n",
    "        'mean_test_accuracy', 'std_test_accuracy',\n",
    "        'mean_train_accuracy', 'std_train_accuracy', \n",
    "        'mean_test_neg_log_loss', 'std_test_neg_log_loss', \n",
    "        'mean_train_neg_log_loss', 'std_train_neg_log_loss',        \n",
    "\n",
    "        'std_test_divided_train_roc_auc', \n",
    "        'std_test_divided_train_f1', \n",
    "        'std_test_divided_train_recall',\n",
    "        'std_test_divided_train_precision',\n",
    "        'std_test_divided_train_accuracy',\n",
    "        'std_test_divided_train_neg_log_loss'        \n",
    "       ]]\n",
    "\n",
    "    # order values by best performance metrics\n",
    "    df_metrics = df_metrics.sort_values(\n",
    "        by=[\"mean_test_divided_train_roc_auc\", \"mean_test_divided_train_neg_log_loss\", \"mean_test_divided_train_f1\"],\n",
    "        ascending=[False, False, False], \n",
    "        ignore_index=True\n",
    "    )\n",
    "\n",
    "    return df_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fN8DBpbGAU5u",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fN8DBpbGAU5u",
    "outputId": "23d0bde6-dcb2-4e04-da1e-50e3b2c4aafb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing Leave2Out validation for DUMMY model on NORMAL data with OR features...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "invalid value encountered in true_divide\n",
      "invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing Leave2Out validation for DUMMY model on SMOTE data with OR features...\n",
      "Performing Leave2Out validation for DUMMY model on ADASYN data with OR features...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing Leave2Out validation for LR model on NORMAL data with OR features...\n",
      "Performing Leave2Out validation for LR model on SMOTE data with OR features...\n",
      "Performing Leave2Out validation for LR model on ADASYN data with OR features...\n",
      "Performing Leave2Out validation for BAYES model on NORMAL data with OR features...\n",
      "Performing Leave2Out validation for BAYES model on SMOTE data with OR features...\n",
      "Performing Leave2Out validation for BAYES model on ADASYN data with OR features...\n",
      "Performing Leave2Out validation for KNN model on NORMAL data with OR features...\n",
      "Performing Leave2Out validation for KNN model on SMOTE data with OR features...\n",
      "Performing Leave2Out validation for KNN model on ADASYN data with OR features...\n",
      "Performing Leave2Out validation for DUMMY model on NORMAL data with AND features...\n",
      "Performing Leave2Out validation for DUMMY model on SMOTE data with AND features...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "invalid value encountered in true_divide\n",
      "invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing Leave2Out validation for DUMMY model on ADASYN data with AND features...\n",
      "Performing Leave2Out validation for LR model on NORMAL data with AND features...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing Leave2Out validation for LR model on SMOTE data with AND features...\n",
      "Performing Leave2Out validation for LR model on ADASYN data with AND features...\n",
      "Performing Leave2Out validation for BAYES model on NORMAL data with AND features...\n",
      "Performing Leave2Out validation for BAYES model on SMOTE data with AND features...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing Leave2Out validation for BAYES model on ADASYN data with AND features...\n",
      "Performing Leave2Out validation for KNN model on NORMAL data with AND features...\n",
      "Performing Leave2Out validation for KNN model on SMOTE data with AND features...\n",
      "Performing Leave2Out validation for KNN model on ADASYN data with AND features...\n"
     ]
    }
   ],
   "source": [
    "# define possible features\n",
    "f_selected_mi\n",
    "f_selected_rf\n",
    "f_selected_lr\n",
    "f_or = list((set(f_selected_mi + f_selected_rf + f_selected_lr)))\n",
    "f_and = list(set(f_selected_mi) & set(f_selected_rf) & set(f_selected_lr))\n",
    "\n",
    "# define possible features to test\n",
    "zip_features_label = zip(\n",
    "    # available features selected\n",
    "    [f_selected_mi, f_selected_rf  , f_selected_lr , f_or, f_and],\n",
    "    # features selected labels\n",
    "    [\"Mutual Info\", \"Random Forest\", \"Logistic Reg\",  \"OR\", \"AND\"]\n",
    "    )\n",
    "\n",
    "# define possible models to test\n",
    "dummy = DummyClassifier(strategy=\"most_frequent\", random_state=seed)\n",
    "lr = LogisticRegression(n_jobs=-1, max_iter=1000, random_state=seed)\n",
    "svm = SVC(probability=True, max_iter=1000, random_state=seed)\n",
    "bayes = BernoulliNB()\n",
    "knn = KNeighborsClassifier(n_jobs=-1)\n",
    "rf = RandomForestClassifier(n_jobs=-1, random_state=seed)\n",
    "lgbm = LGBMClassifier(n_jobs=-1, random_state=seed)\n",
    "catboost = CatBoostClassifier(random_seed=seed, verbose=False)\n",
    "\n",
    "zip_model_label = zip(\n",
    "        # available models will be the \"model\" iterator\n",
    "        [ dummy ,  lr  ,  svm  ,  bayes  ,  knn  ,  rf  ,  lgbm , catboost ],\n",
    "        # available labels will be \"label\" iterator\n",
    "        [\"Dummy\", \"lr\" , \"svm\" , \"bayes\" , \"knn\" , \"rf\" , \"lgbm\", \"catboost\" ] \n",
    "    )\n",
    "\n",
    "# define possible synthetic data generation techniques\n",
    "zip_synthetic_data_label = zip(\n",
    "            # available models will be the \"model\" iterator\n",
    "            [  None  , SMOTE(random_state=0, n_jobs=-1), ADASYN(random_state=0, n_jobs=-1), TomekLinks(n_jobs=-1), NearMiss(n_jobs=-1), RandomOverSampler(random_state=seed)],\n",
    "            # available labels will be \"label\" iterator\n",
    "            [\"Normal\",       \"SMOTE\"                   ,       \"ADASYN\"                   ,       \"Tomek\"        ,      \"NearMiss\"    ,         \"RandomOverSampling\"        ]\n",
    "        )\n",
    "\n",
    "# make experiments\n",
    "df_metrics = explore_stratified_cv(\n",
    "    X_train=X_train_scaled, \n",
    "    y_train=y_train, \n",
    "    zip_features_label=zip_features_label, \n",
    "    zip_model_label=zip_model_label, \n",
    "    zip_synthetic_data_label=zip_synthetic_data_label\n",
    "    )\n",
    "\n",
    "# save results to html\n",
    "df_metrics.to_html(\n",
    "    buf=\"teste.html\", \n",
    "    max_rows=df_metrics.shape[0], max_cols=100, justify=\"center\"\n",
    "    )    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "GFUmAXiFBnpC",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 802
    },
    "id": "GFUmAXiFBnpC",
    "outputId": "15c8ed94-5905-4d61-886a-bdce24af9ae3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-114fa338-c894-4847-ad4e-e86de840a54c\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>data</th>\n",
       "      <th>features</th>\n",
       "      <th>mean_test_divided_train_f1</th>\n",
       "      <th>mean_test_f1</th>\n",
       "      <th>std_test_f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lr</td>\n",
       "      <td>Normal</td>\n",
       "      <td>AND</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bayes</td>\n",
       "      <td>Normal</td>\n",
       "      <td>AND</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lr</td>\n",
       "      <td>ADASYN</td>\n",
       "      <td>AND</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bayes</td>\n",
       "      <td>ADASYN</td>\n",
       "      <td>AND</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dummy</td>\n",
       "      <td>ADASYN</td>\n",
       "      <td>AND</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dummy</td>\n",
       "      <td>Normal</td>\n",
       "      <td>OR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dummy</td>\n",
       "      <td>Normal</td>\n",
       "      <td>AND</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dummy</td>\n",
       "      <td>SMOTE</td>\n",
       "      <td>OR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dummy</td>\n",
       "      <td>ADASYN</td>\n",
       "      <td>OR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dummy</td>\n",
       "      <td>SMOTE</td>\n",
       "      <td>AND</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>bayes</td>\n",
       "      <td>SMOTE</td>\n",
       "      <td>AND</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bayes</td>\n",
       "      <td>ADASYN</td>\n",
       "      <td>OR</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>knn</td>\n",
       "      <td>ADASYN</td>\n",
       "      <td>AND</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>bayes</td>\n",
       "      <td>SMOTE</td>\n",
       "      <td>OR</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>knn</td>\n",
       "      <td>ADASYN</td>\n",
       "      <td>OR</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>knn</td>\n",
       "      <td>SMOTE</td>\n",
       "      <td>AND</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>lr</td>\n",
       "      <td>SMOTE</td>\n",
       "      <td>AND</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.680</td>\n",
       "      <td>0.296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>lr</td>\n",
       "      <td>SMOTE</td>\n",
       "      <td>OR</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>bayes</td>\n",
       "      <td>Normal</td>\n",
       "      <td>OR</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.567</td>\n",
       "      <td>0.417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>knn</td>\n",
       "      <td>Normal</td>\n",
       "      <td>AND</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>knn</td>\n",
       "      <td>SMOTE</td>\n",
       "      <td>OR</td>\n",
       "      <td>0.787</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>lr</td>\n",
       "      <td>ADASYN</td>\n",
       "      <td>OR</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>knn</td>\n",
       "      <td>Normal</td>\n",
       "      <td>OR</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>lr</td>\n",
       "      <td>Normal</td>\n",
       "      <td>OR</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-114fa338-c894-4847-ad4e-e86de840a54c')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
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       "      fill: #1967D2;\n",
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       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-114fa338-c894-4847-ad4e-e86de840a54c button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-114fa338-c894-4847-ad4e-e86de840a54c');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "    model    data features  mean_test_divided_train_f1  mean_test_f1  \\\n",
       "0      lr  Normal      AND                       0.800         0.400   \n",
       "1   bayes  Normal      AND                         NaN         0.233   \n",
       "2      lr  ADASYN      AND                       0.954         0.600   \n",
       "3   bayes  ADASYN      AND                       0.972         0.647   \n",
       "4   Dummy  ADASYN      AND                         NaN         0.600   \n",
       "5   Dummy  Normal       OR                         NaN         0.000   \n",
       "6   Dummy  Normal      AND                         NaN         0.000   \n",
       "7   Dummy   SMOTE       OR                         NaN         0.000   \n",
       "8   Dummy  ADASYN       OR                         NaN         0.000   \n",
       "9   Dummy   SMOTE      AND                         NaN         0.000   \n",
       "10  bayes   SMOTE      AND                       0.987         0.707   \n",
       "11  bayes  ADASYN       OR                       0.890         0.643   \n",
       "12    knn  ADASYN      AND                       0.724         0.592   \n",
       "13  bayes   SMOTE       OR                       0.972         0.697   \n",
       "14    knn  ADASYN       OR                       0.935         0.780   \n",
       "15    knn   SMOTE      AND                       0.802         0.617   \n",
       "16     lr   SMOTE      AND                       0.975         0.680   \n",
       "17     lr   SMOTE       OR                       0.896         0.847   \n",
       "18  bayes  Normal       OR                       0.896         0.567   \n",
       "19    knn  Normal      AND                       0.892         0.633   \n",
       "20    knn   SMOTE       OR                       0.787         0.657   \n",
       "21     lr  ADASYN       OR                       0.882         0.827   \n",
       "22    knn  Normal       OR                       0.752         0.483   \n",
       "23     lr  Normal       OR                       0.545         0.433   \n",
       "\n",
       "    std_test_f1  \n",
       "0         0.516  \n",
       "1         0.387  \n",
       "2         0.274  \n",
       "3         0.347  \n",
       "4         0.211  \n",
       "5         0.000  \n",
       "6         0.000  \n",
       "7         0.000  \n",
       "8         0.000  \n",
       "9         0.000  \n",
       "10        0.311  \n",
       "11        0.377  \n",
       "12        0.365  \n",
       "13        0.320  \n",
       "14        0.191  \n",
       "15        0.385  \n",
       "16        0.296  \n",
       "17        0.199  \n",
       "18        0.417  \n",
       "19        0.457  \n",
       "20        0.330  \n",
       "21        0.192  \n",
       "22        0.448  \n",
       "23        0.473  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_metrics[[\"model\", \"data\", \"features\", \"mean_test_divided_train_f1\", \"mean_test_f1\", \"std_test_f1\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d74b92-80f8-4d91-a8a2-c4c8fc2ce58f",
   "metadata": {
    "id": "41d74b92-80f8-4d91-a8a2-c4c8fc2ce58f"
   },
   "source": [
    "## 13.3 - Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f9f1003-94fb-491d-b419-33f71b153989",
   "metadata": {
    "id": "0f9f1003-94fb-491d-b419-33f71b153989"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e624fe66-08df-4aaa-a51b-8cf8d3846cdc",
   "metadata": {
    "id": "e624fe66-08df-4aaa-a51b-8cf8d3846cdc"
   },
   "source": [
    "## 13.4 - Learning Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ERH-extTxcr2",
   "metadata": {
    "id": "ERH-extTxcr2"
   },
   "outputs": [],
   "source": [
    "# define training dataset -> X_train_scaled with mutual information features\n",
    "# X_train_final = X_train_scaled[f_selected_mi]\n",
    "# X_train_final = X_train_scaled[f_or]\n",
    "X_train_final = X_train_scaled[f_and]\n",
    "\n",
    "# define model to generate synthetic data\n",
    "data_model = SMOTE(random_state=0, n_jobs=-1)\n",
    "# data_model = RandomOverSampler(random_state=seed)\n",
    "\n",
    "# generate synthetic data\n",
    "X_train_modelling, y_train_modelling = data_model.fit_resample(\n",
    "    X_train_final, y_train\n",
    "    )\n",
    "\n",
    "# define ML model\n",
    "# ml_model = CatBoostClassifier(random_seed=seed, verbose=False)\n",
    "# ml_model = KNeighborsClassifier(n_jobs=-1)\n",
    "# ml_model = RandomForestClassifier(n_jobs=-1, random_state=seed)\n",
    "# ml_model = LogisticRegression(n_jobs=-1, max_iter=1000, random_state=seed, C=0.1)\n",
    "ml_model = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "rHnjm-68xb6A",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rHnjm-68xb6A",
    "outputId": "3aa730cc-3305-4b35-bab3-d636639da876"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STRATIFIED CROSS VALIDATION:\n",
      "\n",
      "\ttest_f1: 0.71 +-0.31\n",
      "\ttrain_f1: 0.73 +-0.03\n",
      "\ttest_recall: 0.80 +-0.35\n",
      "\ttrain_recall: 0.80 +-0.04\n",
      "\ttest_precision: 0.65 +-0.31\n",
      "\ttrain_precision: 0.67 +-0.02\n",
      "\ttest_divided_train_f1: 0.99 +-0.45\n",
      "\ttest_divided_train_recall: 1.02 +-0.46\n",
      "\ttest_divided_train_precision: 0.99 +-0.49\n"
     ]
    }
   ],
   "source": [
    "# define metrics\n",
    "metrics = [\"f1\", \"recall\", \"precision\"]\n",
    "\n",
    "# cross validate model\n",
    "cv_data = cross_validate(\n",
    "    estimator=ml_model, \n",
    "    X=X_train_modelling, \n",
    "    y=y_train_modelling, \n",
    "    groups=None, \n",
    "    scoring=metrics, \n",
    "    cv=StratifiedKFold(n_splits=10, shuffle=True, random_state=seed), \n",
    "    return_train_score=True,\n",
    "    n_jobs=-1, \n",
    "    verbose=0\n",
    "    )\n",
    "\n",
    "# iterate over metrics\n",
    "for metric in metrics:\n",
    "    # create a new key for division of test and train metric\n",
    "    cv_data[f\"test_divided_train_{metric}\"] = cv_data[f\"test_{metric}\"] / cv_data[f\"train_{metric}\"]\n",
    "\n",
    "# print report\n",
    "print(\n",
    "    f'STRATIFIED CROSS VALIDATION:\\n'\n",
    ")\n",
    "\n",
    "# iterate over cv_data\n",
    "for k, v in cv_data.items():\n",
    "    # check if key is time or metric\n",
    "    if not k.endswith(\"_time\"):\n",
    "        # print metric\n",
    "        print(\n",
    "            f\"\\t{k}: {np.mean(v):.2f} +-{np.std(v, ddof=1):.2f}\"\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac5ed1de-8bbe-4716-9680-40cdceff555f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "ac5ed1de-8bbe-4716-9680-40cdceff555f",
    "outputId": "f719cfa3-e470-4230-a591-ae4200a25c2e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check learning curves\n",
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "    estimator=LogisticRegression(n_jobs=-1, max_iter=1000, random_state=seed, C=0.1), \n",
    "    X=X_train_modelling, y=y_train_modelling, \n",
    "    train_sizes=np.linspace(0.1, 1.0, 10), \n",
    "    cv=StratifiedKFold(n_splits=10, shuffle=True, random_state=seed),\n",
    "    scoring=\"f1\", n_jobs=-1, shuffle=True, random_state=seed\n",
    "    )\n",
    "\n",
    "# Take the mean of cross-validated train scores and validation scores\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "\n",
    "# plot learning curves\n",
    "sns.lineplot(x = train_sizes, y = train_scores_mean, \n",
    "             estimator='mean', ci=95, n_boot=1000, label = \"Training\")\n",
    "sns.lineplot(x = train_sizes, y = test_scores_mean, \n",
    "             estimator='mean', ci=95, n_boot=1000, label = \"Validating\")\n",
    "# define plot details\n",
    "plt.ylabel('PERFORMANCE METRICS')\n",
    "plt.xlabel('TRAINING SET SIZE')\n",
    "plt.title('LEARNING CURVES')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e1e4a0e-10e7-415c-b4cc-86db0fa3821c",
   "metadata": {
    "id": "4e1e4a0e-10e7-415c-b4cc-86db0fa3821c"
   },
   "source": [
    "## 13.5 - Model Calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7df6a7fc-98e3-4ab5-b03a-4304958de00e",
   "metadata": {
    "id": "7df6a7fc-98e3-4ab5-b03a-4304958de00e"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b3ebc08-9b00-4b62-b2a0-790f7621bd5d",
   "metadata": {
    "id": "2b3ebc08-9b00-4b62-b2a0-790f7621bd5d"
   },
   "source": [
    "# 14 - Model Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f624f4-e0fb-45a1-a53c-1b302d0f9f60",
   "metadata": {
    "id": "29f624f4-e0fb-45a1-a53c-1b302d0f9f60"
   },
   "source": [
    "*Evaluate model performance*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b0ef560-c352-44ea-990f-2bb098dd8e89",
   "metadata": {
    "id": "9b0ef560-c352-44ea-990f-2bb098dd8e89"
   },
   "source": [
    "## 14.1 - Training performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff7b4358-9824-4526-a05d-2dce0b7bb783",
   "metadata": {
    "id": "ff7b4358-9824-4526-a05d-2dce0b7bb783"
   },
   "source": [
    "*Evaluate model performance on training dataset*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d072a34-4285-4ac4-b118-1e132a6d9628",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0d072a34-4285-4ac4-b118-1e132a6d9628",
    "outputId": "bbf93fbc-07a6-4c74-f5dd-46f66354485a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BernoulliNB()"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define training dataset -> X_train_scaled with mutual information features\n",
    "X_train_final = X_train_scaled[f_and]\n",
    "\n",
    "# define model to generate synthetic data\n",
    "data_model = SMOTE(random_state=0, n_jobs=-1)\n",
    "\n",
    "# generate synthetic data\n",
    "X_train_modelling, y_train_modelling = data_model.fit_resample(\n",
    "    X_train_final, y_train\n",
    "    )\n",
    "\n",
    "# define ML model\n",
    "ml_model = BernoulliNB()\n",
    "\n",
    "# fit model to data\n",
    "ml_model.fit(X_train_modelling, y_train_modelling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22r2FU7SHuhc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 229
    },
    "id": "22r2FU7SHuhc",
    "outputId": "ddef867c-8f66-4630-ab24-9e7814769ef3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Attribute `coef_` was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26).\n",
      "Attribute `intercept_` was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x165.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute SHAP values\n",
    "explainer = shap.Explainer(ml_model, X_train_modelling)\n",
    "shap_values = explainer(X_train_modelling)\n",
    "\n",
    "# plot shap values for features\n",
    "shap.plots.beeswarm(shap_values, order=shap_values.abs.mean(0), max_display=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38hhOsZTKUbU",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 252
    },
    "id": "38hhOsZTKUbU",
    "outputId": "91f2aba8-1df6-4e49-a752-06fbff506d63"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot heatmap\n",
    "shap.plots.heatmap(shap_values[:1000], max_display=10, feature_values=shap.Explanation.abs.mean(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "748ed4f0-b4b7-4a43-89d7-af4b0c7e31f7",
   "metadata": {
    "id": "748ed4f0-b4b7-4a43-89d7-af4b0c7e31f7"
   },
   "source": [
    "## 14.2 - Validation performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca3e6fb1-1a36-4bae-a6c5-3d028101dfff",
   "metadata": {
    "id": "ca3e6fb1-1a36-4bae-a6c5-3d028101dfff"
   },
   "source": [
    "*Evaluate model performance on validation dataset*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "465d7120-8148-48e4-ac20-57d632a601b9",
   "metadata": {
    "id": "465d7120-8148-48e4-ac20-57d632a601b9"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "452ba38b-08d8-419f-9841-66dff895ba43",
   "metadata": {
    "id": "452ba38b-08d8-419f-9841-66dff895ba43"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "383066ac-0be1-4629-8eb2-20ca4eac315b",
   "metadata": {
    "id": "383066ac-0be1-4629-8eb2-20ca4eac315b"
   },
   "source": [
    "## 14.1 - Test performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3be767f-fde3-433f-88c5-72a7855c0738",
   "metadata": {
    "id": "a3be767f-fde3-433f-88c5-72a7855c0738"
   },
   "source": [
    "*Evaluate model performance on test dataset*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e7744b6-5091-4356-9231-3035d052a5ab",
   "metadata": {
    "id": "7e7744b6-5091-4356-9231-3035d052a5ab"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cea5884f-d96e-4f1f-940d-f3fe1fd2d289",
   "metadata": {
    "id": "cea5884f-d96e-4f1f-940d-f3fe1fd2d289"
   },
   "source": [
    "# 15 - Model diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "706efcd9-8110-4325-bfc5-60e10177b9fa",
   "metadata": {
    "id": "706efcd9-8110-4325-bfc5-60e10177b9fa"
   },
   "source": [
    "## 15.1 - Feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0f4e185-b234-4f32-b18a-b08ac00f522e",
   "metadata": {
    "id": "c0f4e185-b234-4f32-b18a-b08ac00f522e"
   },
   "source": [
    "*Check the importance of features for model prediciton*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e1b74f5-03d3-4d36-ba57-d87b5cf045fd",
   "metadata": {
    "id": "1e1b74f5-03d3-4d36-ba57-d87b5cf045fd"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89d52e7e-92ad-4adb-8799-9432e196e9a4",
   "metadata": {
    "id": "89d52e7e-92ad-4adb-8799-9432e196e9a4"
   },
   "source": [
    "## 15.2 - Bias analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "654d5a3a-2f73-4789-a8d9-1c14148da64d",
   "metadata": {
    "id": "654d5a3a-2f73-4789-a8d9-1c14148da64d"
   },
   "source": [
    "*Check for bias on model predictions*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84c16f0a-06a0-4c61-b28d-1a83a585f30d",
   "metadata": {
    "id": "84c16f0a-06a0-4c61-b28d-1a83a585f30d"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "409035aa-8576-46bc-b1aa-a6b114f52a56",
   "metadata": {
    "id": "409035aa-8576-46bc-b1aa-a6b114f52a56"
   },
   "source": [
    "## 15.3 - Uncertainty analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ca8fc6-b17d-4cb7-acaf-1ef7840843af",
   "metadata": {
    "id": "73ca8fc6-b17d-4cb7-acaf-1ef7840843af"
   },
   "source": [
    "*Check for uncertainty on model predictions*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f1f86fa-2476-4db7-8295-38132ba3568e",
   "metadata": {
    "id": "1f1f86fa-2476-4db7-8295-38132ba3568e"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78a671d7-7efb-45a9-9174-76d5e92ed18e",
   "metadata": {
    "id": "78a671d7-7efb-45a9-9174-76d5e92ed18e"
   },
   "source": [
    "## 15.4 - SHAP values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e45fe97-793e-4c79-9c52-9b1720d37154",
   "metadata": {
    "id": "8e45fe97-793e-4c79-9c52-9b1720d37154"
   },
   "source": [
    "*Inspect shap values for model prediction*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6b47d25-1fa6-42af-9b36-d6c1bb4a2fc5",
   "metadata": {
    "id": "d6b47d25-1fa6-42af-9b36-d6c1bb4a2fc5"
   },
   "outputs": [],
   "source": [
    "# # TO-DO\n",
    "\n",
    "# # explain the GAM model with SHAP\n",
    "# explainer_ebm = shap.Explainer(model_ebm.predict, X100)\n",
    "# shap_values_ebm = explainer_ebm(X)\n",
    "\n",
    "# shap.plots.beeswarm(shap_values_ebm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81275df4-e65e-4a4d-a695-40094519ab32",
   "metadata": {
    "id": "81275df4-e65e-4a4d-a695-40094519ab32"
   },
   "source": [
    "# 16 - Prepare model for production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5ffef10-8ced-40e2-b6e5-1085403a0104",
   "metadata": {
    "id": "b5ffef10-8ced-40e2-b6e5-1085403a0104"
   },
   "outputs": [],
   "source": [
    "# TO-DO"
   ]
  }
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