{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "understanding-messaging",
   "metadata": {},
   "source": [
    "# Predict BMI\n",
    "\n",
    "This script shows a real world example using BPt to study the relationship between BMI and the brain. The data used in this notebook cannot be made public as it is from the ABCD Study, which requires a data use agreement in order to use the data.\n",
    "\n",
    "This notebook covers a number of different topics:\n",
    "\n",
    "- Preparing Data\n",
    "- Evaluating a single pipeline\n",
    "- Considering different options for how to use a test set\n",
    "- Introduce and use the Evaluate input option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "illegal-account",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import BPt as bp\n",
    "import numpy as np\n",
    "\n",
    "# Don't show sklearn convergence warnings\n",
    "from warnings import simplefilter\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "simplefilter(\"ignore\", category=ConvergenceWarning)\n",
    "\n",
    "# Display tables up to five decimals\n",
    "pd.options.display.float_format = \"{:,.5f}\".format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "approximate-swift",
   "metadata": {},
   "source": [
    "## Preparing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "swiss-external",
   "metadata": {},
   "source": [
    "We will first load in the underlying dataset for this project which has been saved as a csv. It contains multi-modal change in ROI data from two timepoints of the ABCD Study (difference from follow up and baseline).\n",
    "\n",
    "This saved dataset doesn't include the real family ids, but an interesting piece of the ABCD study derived data is that there are a number of subjects from the same family. We will handle that in this example (granted with a fake family structure which we will generate below) by ensuring that for any cross-validation split, members of the same family stay in the same training or testing fold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "smart-defensive",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Unnamed: 0',\n",
       " 'src_subject_id',\n",
       " 'b_averaged_puberty',\n",
       " 'b_agemos',\n",
       " 'b_sex',\n",
       " 'b_race_ethnicity_categories',\n",
       " 'b_demo_highest_education_categories',\n",
       " 'b_site_id_l',\n",
       " 'b_subjects_no_missing_data',\n",
       " 'bmi_keep']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel('data/structure_base.xlsx')\n",
    "list(data)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chinese-thirty",
   "metadata": {},
   "source": [
    "This dataset contains a number of columns we don't need. We will use the next cell to both group variables of interest together, and then select only the relvant columns to keep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "chubby-length",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9724, 668)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our target variable\n",
    "targets = ['b_bmi']\n",
    "\n",
    "# Columns with different traditional 'co-variates'.\n",
    "covars = ['b_sex',  'b_demo_highest_education_categories','b_race_ethnicity_categories',\n",
    "          'b_agemos', 'b_mri_info_deviceserialnumber']\n",
    "\n",
    "# Let's also note which of these are categorical\n",
    "cat_covars = ['b_mri_info_deviceserialnumber',\n",
    "              'b_demo_highest_education_categories',\n",
    "              'b_race_ethnicity_categories',\n",
    "              'b_sex']\n",
    "\n",
    "# These variables are any which we might want to use\n",
    "# but not directly as input features! E.g., we\n",
    "# might want to use them to inform choice of cross-validation.\n",
    "non_input = ['b_rel_family_id']\n",
    "\n",
    "# The different imaging features\n",
    "thick = [d for d in list(data) if 'thick' in d]\n",
    "area = [d for d in list(data) if 'smri_area_cort' in d]\n",
    "subcort = [d for d in list(data) if 'smri_vol' in d]\n",
    "dti_fa = [d for d in list(data) if 'dmri_dti_full_fa_' in d]\n",
    "dti_md = [d for d in list(data) if 'dmri_dti_full_md_' in d]\n",
    "brain = thick + area + subcort + dti_fa + dti_md\n",
    "\n",
    "# All to keep\n",
    "to_keep = brain + targets + covars + non_input\n",
    "\n",
    "data = data[to_keep]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "editorial-vehicle",
   "metadata": {},
   "source": [
    "Now let's convert from a pandas DataFrame to a BPt Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "mobile-berlin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9724, 668)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = bp.Dataset(data)\n",
    "\n",
    "# This is optional, to print some extra statements.\n",
    "data.verbose = 1\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "macro-matrix",
   "metadata": {},
   "source": [
    "Next, we perform some actions specific to the Dataset class. These include specifying which columns are 'target' and 'non input', with any we don't set to one these roles treated as the default role, 'data'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "random-commissioner",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting NaN threshold to: 0.5\n",
      "Dropped 8 Rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"float: left; padding: 10px;\">\n",
       "        <h3>Data</h3><div>\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b_agemos</th>\n",
       "      <th>b_demo_highest_education_categories</th>\n",
       "      <th>b_mri_info_deviceserialnumber</th>\n",
       "      <th>b_race_ethnicity_categories</th>\n",
       "      <th>b_sex</th>\n",
       "      <th>dmri_dti_full_fa_subcort_aseg_accumbens_area_lh</th>\n",
       "      <th>dmri_dti_full_fa_subcort_aseg_accumbens_area_rh</th>\n",
       "      <th>dmri_dti_full_fa_subcort_aseg_amygdala_lh</th>\n",
       "      <th>dmri_dti_full_fa_subcort_aseg_amygdala_rh</th>\n",
       "      <th>dmri_dti_full_fa_subcort_aseg_caudate_lh</th>\n",
       "      <th>...</th>\n",
       "      <th>smri_vol_scs_subcorticalgv</th>\n",
       "      <th>smri_vol_scs_suprateialv</th>\n",
       "      <th>smri_vol_scs_tplh</th>\n",
       "      <th>smri_vol_scs_tprh</th>\n",
       "      <th>smri_vol_scs_vedclh</th>\n",
       "      <th>smri_vol_scs_vedcrh</th>\n",
       "      <th>smri_vol_scs_wholeb</th>\n",
       "      <th>smri_vol_scs_wmhint</th>\n",
       "      <th>smri_vol_scs_wmhintlh</th>\n",
       "      <th>smri_vol_scs_wmhintrh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>124</td>\n",
       "      <td>2</td>\n",
       "      <td>HASHe4f6957a</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.17195</td>\n",
       "      <td>0.17389</td>\n",
       "      <td>0.19923</td>\n",
       "      <td>0.16654</td>\n",
       "      <td>0.15518</td>\n",
       "      <td>...</td>\n",
       "      <td>52,440.00000</td>\n",
       "      <td>935,475.83514</td>\n",
       "      <td>6,220.90000</td>\n",
       "      <td>5,787.40000</td>\n",
       "      <td>3,554.80000</td>\n",
       "      <td>3,427.90000</td>\n",
       "      <td>1,045,923.63514</td>\n",
       "      <td>478.40000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122</td>\n",
       "      <td>5</td>\n",
       "      <td>HASH1314a204</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.16897</td>\n",
       "      <td>0.16477</td>\n",
       "      <td>0.20000</td>\n",
       "      <td>0.17713</td>\n",
       "      <td>0.15848</td>\n",
       "      <td>...</td>\n",
       "      <td>62,550.00000</td>\n",
       "      <td>1,078,644.86257</td>\n",
       "      <td>8,222.60000</td>\n",
       "      <td>7,571.40000</td>\n",
       "      <td>3,872.50000</td>\n",
       "      <td>3,837.40000</td>\n",
       "      <td>1,197,394.06258</td>\n",
       "      <td>769.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114</td>\n",
       "      <td>3</td>\n",
       "      <td>HASH69f406fa</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22881</td>\n",
       "      <td>0.25250</td>\n",
       "      <td>0.23221</td>\n",
       "      <td>0.22298</td>\n",
       "      <td>0.22545</td>\n",
       "      <td>...</td>\n",
       "      <td>60,695.00000</td>\n",
       "      <td>1,133,471.50460</td>\n",
       "      <td>7,040.50000</td>\n",
       "      <td>6,752.90000</td>\n",
       "      <td>4,588.80000</td>\n",
       "      <td>4,631.50000</td>\n",
       "      <td>1,291,126.70460</td>\n",
       "      <td>1,114.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>130</td>\n",
       "      <td>5</td>\n",
       "      <td>HASH1314a204</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.16737</td>\n",
       "      <td>0.18840</td>\n",
       "      <td>0.15135</td>\n",
       "      <td>0.16215</td>\n",
       "      <td>0.18848</td>\n",
       "      <td>...</td>\n",
       "      <td>65,614.00000</td>\n",
       "      <td>1,055,580.45187</td>\n",
       "      <td>8,365.70000</td>\n",
       "      <td>7,656.40000</td>\n",
       "      <td>4,600.10000</td>\n",
       "      <td>4,920.60000</td>\n",
       "      <td>1,189,841.05187</td>\n",
       "      <td>1,788.50000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>115</td>\n",
       "      <td>5</td>\n",
       "      <td>HASHc3bf3d9c</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.18902</td>\n",
       "      <td>0.21375</td>\n",
       "      <td>0.23945</td>\n",
       "      <td>0.20581</td>\n",
       "      <td>0.20100</td>\n",
       "      <td>...</td>\n",
       "      <td>59,174.00000</td>\n",
       "      <td>1,010,567.33270</td>\n",
       "      <td>6,577.90000</td>\n",
       "      <td>6,612.70000</td>\n",
       "      <td>3,434.30000</td>\n",
       "      <td>3,942.60000</td>\n",
       "      <td>1,144,069.73270</td>\n",
       "      <td>1,036.30000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 666 columns</p>\n",
       "</div></div>\n",
       "<div style=\"float: left; padding: 10px;\">\n",
       "        <h3>Target</h3><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>b_bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.17507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.45090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.43703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.38701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.59670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></div>\n",
       "<div style=\"float: left; padding: 10px;\">\n",
       "        <h3>Non Input</h3><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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b_rel_family_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></div>\n"
      ],
      "text/plain": [
       "   smri_thick_cort_destrieux_g_and_s_frontomargin_lh  \\\n",
       "0                                            2.34300   \n",
       "1                                            2.74300   \n",
       "2                                            2.45400   \n",
       "3                                            2.96600   \n",
       "4                                            2.48500   \n",
       "\n",
       "   smri_thick_cort_destrieux_g_and_s_occipital_inf_lh  \\\n",
       "0                                            2.43700    \n",
       "1                                            2.31000    \n",
       "2                                            2.80100    \n",
       "3                                            2.60500    \n",
       "4                                            2.68100    \n",
       "\n",
       "   smri_thick_cort_destrieux_g_and_s_paracentral_lh  \\\n",
       "0                                           2.41200   \n",
       "1                                           2.86600   \n",
       "2                                           2.70300   \n",
       "3                                           3.19700   \n",
       "4                                           2.65500   \n",
       "\n",
       "   smri_thick_cort_destrieux_g_and_s_subcentral_lh  \\\n",
       "0                                          2.81300   \n",
       "1                                          3.33100   \n",
       "2                                          2.45700   \n",
       "3                                          3.09100   \n",
       "4                                          2.60800   \n",
       "\n",
       "   smri_thick_cort_destrieux_g_and_s_transv_frontopol_lh  \\\n",
       "0                                            2.69700       \n",
       "1                                            3.07600       \n",
       "2                                            2.62100       \n",
       "3                                            3.25200       \n",
       "4                                            3.05000       \n",
       "\n",
       "   smri_thick_cort_destrieux_g_and_s_cingul_ant_lh  \\\n",
       "0                                          2.92300   \n",
       "1                                          3.24900   \n",
       "2                                          3.17200   \n",
       "3                                          3.12600   \n",
       "4                                          3.23100   \n",
       "\n",
       "   smri_thick_cort_destrieux_g_and_s_cingul_mid_ant_lh  \\\n",
       "0                                            2.91300     \n",
       "1                                            3.23000     \n",
       "2                                            3.13800     \n",
       "3                                            3.24100     \n",
       "4                                            2.95600     \n",
       "\n",
       "   smri_thick_cort_destrieux_g_and_s_cingul_mid_post_lh  \\\n",
       "0                                            2.75700      \n",
       "1                                            3.08100      \n",
       "2                                            2.84600      \n",
       "3                                            3.05700      \n",
       "4                                            2.84400      \n",
       "\n",
       "   smri_thick_cort_destrieux_g_cingul_post_dorsal_lh  \\\n",
       "0                                            3.18000   \n",
       "1                                            3.49000   \n",
       "2                                            3.43000   \n",
       "3                                            3.44800   \n",
       "4                                            3.15200   \n",
       "\n",
       "   smri_thick_cort_destrieux_g_cingul_post_ventral_lh  ...  \\\n",
       "0                                            2.40700   ...   \n",
       "1                                            2.38600   ...   \n",
       "2                                            2.90700   ...   \n",
       "3                                            3.21400   ...   \n",
       "4                                            2.41400   ...   \n",
       "\n",
       "   dmri_dti_full_md_subcort_aseg_amygdala_rh  \\\n",
       "0                                    0.67408   \n",
       "1                                    0.66106   \n",
       "2                                    0.73185   \n",
       "3                                    0.67879   \n",
       "4                                    0.71735   \n",
       "\n",
       "   dmri_dti_full_md_subcort_aseg_accumbens_area_rh  \\\n",
       "0                                          0.64020   \n",
       "1                                          0.63493   \n",
       "2                                          0.66539   \n",
       "3                                          0.61966   \n",
       "4                                          0.72282   \n",
       "\n",
       "   dmri_dti_full_md_subcort_aseg_ventraldc_rh    b_bmi  b_sex  \\\n",
       "0                                     0.48485 15.17507      1   \n",
       "1                                     0.51481 16.45090      2   \n",
       "2                                     0.55961 24.43703      1   \n",
       "3                                     0.50990 17.38701      2   \n",
       "4                                     0.56224 17.59670      2   \n",
       "\n",
       "   b_demo_highest_education_categories  b_race_ethnicity_categories  b_agemos  \\\n",
       "0                                    2                            2       124   \n",
       "1                                    5                            1       122   \n",
       "2                                    3                            1       114   \n",
       "3                                    5                            1       130   \n",
       "4                                    5                            1       115   \n",
       "\n",
       "   b_mri_info_deviceserialnumber  b_rel_family_id  \n",
       "0                   HASHe4f6957a             2257  \n",
       "1                   HASH1314a204            11328  \n",
       "2                   HASH69f406fa             7607  \n",
       "3                   HASH1314a204            11324  \n",
       "4                   HASHc3bf3d9c             7608  \n",
       "\n",
       "[5 rows x 668 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set's targets to target role\n",
    "data = data.set_role(targets, 'target')\n",
    "\n",
    "# Set non input to non input role\n",
    "data = data.set_role(non_input, 'non input')\n",
    "\n",
    "# Drop any missing values in the target variable\n",
    "data = data.drop_subjects_by_nan(scope='target')\n",
    "\n",
    "# We can optional add the categories we made as scopes!\n",
    "data.add_scope(covars, 'covars', inplace=True)\n",
    "data.add_scope(cat_covars, 'cat covars', inplace=True)\n",
    "data.add_scope(thick, 'thick', inplace=True)\n",
    "data.add_scope(area, 'area', inplace=True)\n",
    "data.add_scope(subcort, 'subcort', inplace=True)\n",
    "data.add_scope(dti_fa, 'dti_fa', inplace=True)\n",
    "data.add_scope(dti_md, 'dti_md', inplace=True)\n",
    "data.add_scope(brain, 'brain', inplace=True )\n",
    "\n",
    "# Drop all NaN from any column\n",
    "# Though BPt can generally handle NaN data fine,\n",
    "# it makes certain pieces easier for this example as we don't have to worry\n",
    "# about imputation.\n",
    "data = data.dropna()\n",
    "\n",
    "# Just show the first few rows\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "civilian-display",
   "metadata": {},
   "source": [
    "The scopes we defined are nice, as it lets use check columns, or compose different scopes together. For example\n",
    "we can check the scope we set 'cat covars' as composed with another variable as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "unusual-rental",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['b_demo_highest_education_categories',\n",
       " 'b_mri_info_deviceserialnumber',\n",
       " 'b_race_ethnicity_categories',\n",
       " 'b_rel_family_id',\n",
       " 'b_sex']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.get_cols([cat_covars, 'b_rel_family_id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "renewable-correction",
   "metadata": {},
   "source": [
    "These are notably the columns we want to make sure are categorical, so lets ordinalize them, then plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "homeless-bahamas",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b_demo_highest_education_categories: 9478 rows\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b_mri_info_deviceserialnumber: 9478 rows\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b_race_ethnicity_categories: 9478 rows\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b_rel_family_id: 9478 rows\n",
      "b_sex: 9478 rows\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sage/anaconda3/envs/bpt/lib/python3.9/site-packages/BPt/dataset/helpers.py:167: UserWarning: Skipping plot: b_rel_family_id as >= categories!\n",
      "  warnings.warn(as_str)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAF+CAYAAACidPAUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVpUlEQVR4nO3deZRtZX3m8e/DLFzCqMikTBpDDCEM7UTSjoEQNZ20MbAwaIwDatIh2q2gBoMmsdW00SzSC8EYY8QxxqXBARFwjAHvJaAMIYxGAUGggQshhuHXf+y3yKGounUu1Lmn3qrvZ62zap9377P376117nPfes8+e6eqkCT1Y6NpFyBJWj8GtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwa2HJck1SZ497ToeiiRvTPL+RdzfHUn2assfTPJHi7jvk5P8wWLtT30zuLUsJflKkn9PsjbJ7UnWJDkuyeYz21TVn1TVy8bc14LbVdWqqrpqEWp/SZJvzNr3MVX1toe7by0PBreWs9+pqq2BnYHXAUcAn0+SxTxIkk0Wc3/SQgxuLYaDk1yS5P8l+askW6xr4yQ7Jjk9ya1Jbkny9SQbtXW7JPlUkh8luTrJ/2jt2yf5QZLnteerklyR5OiFiquqO6vqK8DzgacAv9z28YdJPtyWt0jy4SQ3t7q+nWSnJH8M/DxwUpsKOaltX0lek+Ry4PKRtn1GDr1jkjPbqP+rSR7bttujbXt/4M+M6pP8FHAy8JR2vFvb+gdMvSR5eev/LUk+m2SXkXWV5Jgkl7e+/MVi/2el6TK4tRiOAg4F9gYeD7x5ge1fB/wAeCSwE/BGoFp4/z1wIbAr8Czg2CSHVtUtwEuBU5M8Cvgz4IKq+tC4RVbVvwKrGYJ4thcD2wC7AzsAxwB3VdWbgK8zjN5XVdXvjLzmvwFPAvad55BHAW8DdgQuAE4bo8ZL27G/1Y637extkjwTeDvwQoa/Jr4HfGzWZs8FDgb2a9sdutCx1Q+DW4vhpKr6fgvXPwaOXGD7uxkC57FVdXdVfb2Gq50dDDyyqt5aVf/R5otPZZjioKq+BHwSOAs4HHjlQ6j1OmD7eWraAdinqu6tqjVVdfsC+3p7Vd1SVXfNs/5zVfW1qvox8CaGUfTuD6Hm2Y4CPlBV57d9H9/2vcfINv+7qm5t/1mdA+y/CMfVEmFwazF8f2T5e8Au823YvAu4AvhSkquSHNfaHwvs0v68v7VNE7yRYVQ+4xTgicAHq+rmh1DrrsAtc7T/DXAG8LEk1yV5Z5JNF9jX98ddX1V3tOMu9LsZxy4Mv+fRfd/M0LcZPxxZ/jdg1SIcV0uEwa3FMDqKfAzDqHZeVbW2ql5XVXsxzDu/NsmzGILu6qraduSxdVUdDpBkY4bg/hDw6lnzyQtqo90DGaY+Ztd0d1WdWFX7Ak9lmGqYmT+f79rHC10T+f7fS5JVDCP964A7W/OWI9s+ej32ex3Df3Iz+96K4a+Faxd4nZYJg1uL4TVJdkuyPcOUwMfXtXGS5ybZp31gdhtwL3AfcB6wNskbkjwiycZJnpjk4PbSNzKE2ksZRu0famG+Tkm2TPJfgc+0Y3x+jm2ekeRn2v5uZ5g6ua+tvgHYa6HjzOHwJIck2Yxhrvsf25TSjxhC9kWtjy9l+Hxgxg3Abu11c/ko8FtJ9m+nN/4JcG5VXfMQalSHDG4tho8AXwKuAq4EFvriyeOALwN3AN8C/m9VnVNV9zKMdPcHrgZuAt4PbJPkQOC1wNFtu3cwhPhxD979/U5KspYhCN8DfAo4rKrum2PbRwN/yxDalwJfZZg+AXgv8IJ21syfL9C3UR8B3sIwRXIg8KKRdS8H/hfDFMdPA/8wsu5s4GLgh0lumr3Tqvoy8AetP9czhP4R61GXOhfvgCNJfXHELUmdMbg1ERmuA3LHHI8vTLs2qXdOlUhSZ5bUNRYOO+yw+uIXvzjtMiRpKZj3MgVLaqrkppse9AG6JGmWJRXckqSFGdyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdWVI3Utjq0XvWE37zxGmXIUmLYs27jn44L+/jetySpIUZ3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqzMSCO8kHktyY5KJJHUOSVqJJjrg/CBw2wf1L0oo0seCuqq8Bt0xq/5K0Uk19jjvJK5KsTrL6nn9bO+1yJGnJm3pwV9UpVXVQVR20yZZbT7scSVryph7ckqT1Y3BLUmcmeTrgR4FvAT+Z5AdJfntSx5KklWSTSe24qo6c1L4laSVzqkSSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1Jn1ju4k2yXZL9JFCNJWthYwZ3kK0l+Isn2wPnAqUnePdnSJElzGXfEvU1V3Q78GvChqnoS8OzJlSVJms+4wb1Jkp2BFwKnT7AeSdICxg3utwJnAFdW1beT7AVcPrmyJEnz2WScjarqk8AnR55fBfz3SRUlSZrfuB9OPj7JWUkuas/3S/LmyZYmSZrLuFMlpwLHA3cDVNV3gCMmVZQkaX5jTZUAW1bVeUlG2+5Z7GJ+arcdWP2uoxd7t5K0rIw74r4pyd5AASR5AXD9xKqSJM1r3BH3a4BTgCckuRa4GnjRxKqSJM1r3LNKrgKenWQrYKOqWjvZsiRJ81lncCd5UVV9OMlrZ7UDUFV+7V2SNrCFRtxbtZ9bT7oQSdJ41hncVfW+JBsDt1fVn22gmiRJ67DgWSVVdS9w5AaoRZI0hnHPKvlmkpOAjwN3zjRW1fkTqUqSNK9xg3v/9vOtI20FPHNRq5EkLWjc0wGfMelCJEnjGfciU9skeXeS1e3xf5JsM+niJEkPNu5X3j8ArGW4kcILgduBv5pUUZKk+Y07x713VY1ef/vEJBdMoB5J0gLGHXHfleSQmSdJngbcNZmSJEnrMu6I+1XAX7d57QC3AC+ZVFGSpPmNe1bJBcDPJvmJ9vz2SRYlSZrfWME9z0WmbgPWtFCXJG0g485xHwQcA+zaHq8EDgNOTfL6CdUmSZrDuHPcuwEHVNUdAEneAnwO+AVgDfDOyZQnSZpt3BH3o4Afjzy/G9ipqu6a1S5JmrBxR9ynAecm+Ux7/jzgI+2OOJdMpDJJ0pzGPavkbUm+ADytNR1TVavb8lETqUySNKdxp0oAtmC4ocJ7ge8l2XNCNUmS1mHci0y9BXgDcHxr2hT48KSKkiTNb9wR968Cz6fdRKGqrsP7UErSVIwb3P9RVcVw8wTah5KSpCkYN7g/keR9wLZJXg58GXj/5MqSJM1n3LNK/jTJcxiuw/2TwAlVdeZEK5MkzWnca5W8o6reAJw5R5skaQMad6rkOXO0/dJiFiJJGs86R9xJXgW8GtgryXdGVm0NfHOShUmS5pbhZJF5Vg43TtgOeDtw3MiqtVV1y2IXs9+uj6jTX7nPYu9WkgB4zAnfnXYJ6yPzrVjniLuqbmO47vaRAEkexfANylVJVlXVvy5mlZKkhY37zcnnJbkcuBr4KnAN8IUJ1iVJmse4H07+EfBk4F+qak/gWcA/TqwqSdK8xg3uu6vqZmCjJBtV1TkMd8WRJG1g416P+9Ykq4CvAacluZF23RJJ0oa10OmA+wA7Ab8C3AX8PsP1tx8L/O7Eq5MkPchCUyXvYbgG951VdV9V3VNVfw18GvjDSRcnSXqwhYJ7p6p60ImPrW2PiVQkSVqnhYJ723Wse8Qi1iFJGtNCwb26Xcb1AZK8DFgzmZIkSeuy0FklxwKfTnIU/xnUBwGbMdwVR5K0gS30lfcbgKcmeQbwxNb8uao6e+KVSZLmNO6NFM4BzplwLZKkMYz7zUlJ0hJhcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM5MLLiT7J7knCSXJLk4ye9N6liStJJsMsF93wO8rqrOT7I1sCbJmVV1yQSPKUnL3sRG3FV1fVWd35bXApcCu07qeJK0UmyQOe4kewA/B5w7x7pXJFmdZPUtd967IcqRpK5NPLiTrAI+BRxbVbfPXl9Vp1TVQVV10PZbbTzpciSpexMN7iSbMoT2aVX1d5M8liStFJM8qyTAXwKXVtW7J3UcSVppJjnifhrwm8Azk1zQHodP8HiStCJM7HTAqvoGkEntX5JWKr85KUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdWaTaRcwarOdf5rHnLB62mVI0pLmiFuSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOpKqmXcP9kqwFLpt2HVOwI3DTtIuYgpXab1i5fbff47upqg6ba8WSupECcFlVHTTtIja0JKvt98qyUvtuvxeHUyWS1BmDW5I6s9SC+5RpFzAl9nvlWal9t9+LYEl9OClJWthSG3FLkhZgcEtSZ5ZEcCc5LMllSa5Icty063m4knwgyY1JLhpp2z7JmUkubz+3a+1J8uet799JcsDIa17ctr88yYun0Zf1kWT3JOckuSTJxUl+r7WvhL5vkeS8JBe2vp/Y2vdMcm7r48eTbNbaN2/Pr2jr9xjZ1/Gt/bIkh06pS+slycZJ/inJ6e35Sun3NUm+m+SCJKtb2+Tf71U11QewMXAlsBewGXAhsO+063qYffoF4ADgopG2dwLHteXjgHe05cOBLwABngyc29q3B65qP7dry9tNu28L9Htn4IC2vDXwL8C+K6TvAVa15U2Bc1ufPgEc0dpPBl7Vll8NnNyWjwA+3pb3bf8GNgf2bP82Np52/8bo/2uBjwCnt+crpd/XADvOapv4+30pdPwpwBkjz48Hjp92XYvQrz1mBfdlwM5teWeGLxsBvA84cvZ2wJHA+0baH7BdDw/gM8BzVlrfgS2B84EnMXxbbpPWfv97HTgDeEpb3qRtl9nv/9HtluoD2A04C3gmcHrrx7Lvd6tzruCe+Pt9KUyV7Ap8f+T5D1rbcrNTVV3fln8I7NSW5+t/17+X9ifwzzGMPFdE39t0wQXAjcCZDKPGW6vqnrbJaD/u72NbfxuwA332/T3A64H72vMdWBn9BijgS0nWJHlFa5v4+32pfeV9RaiqSrJsz8NMsgr4FHBsVd2e5P51y7nvVXUvsH+SbYFPA0+YbkWTl+S5wI1VtSbJ06dczjQcUlXXJnkUcGaSfx5dOan3+1IYcV8L7D7yfLfWttzckGRngPbzxtY+X/+7/L0k2ZQhtE+rqr9rzSui7zOq6lbgHIYpgm2TzAyQRvtxfx/b+m2Am+mv708Dnp/kGuBjDNMl72X59xuAqrq2/byR4T/r/8IGeL8vheD+NvC49in0ZgwfWHx2yjVNwmeBmU+LX8ww/zvTfnT7xPnJwG3tz6wzgF9Msl37VPoXW9uSlWFo/ZfApVX17pFVK6Hvj2wjbZI8gmFu/1KGAH9B22x232d+Jy8Azq5hgvOzwBHt7Is9gccB522QTjwEVXV8Ve1WVXsw/Ns9u6qOYpn3GyDJVkm2nllmeJ9exIZ4v097cr9Nxh/OcAbClcCbpl3PIvTno8D1wN0M81W/zTCPdxZwOfBlYPu2bYC/aH3/LnDQyH5eClzRHr817X6N0e9DGOb8vgNc0B6Hr5C+7wf8U+v7RcAJrX0vhgC6AvgksHlr36I9v6Kt32tkX29qv5PLgF+adt/W43fwdP7zrJJl3+/Wxwvb4+KZ7NoQ73e/8i5JnVkKUyWSpPVgcEtSZwxuSeqMwS1JnTG4JakzBreWnSSPTvKxJFe2ryJ/PsnjF3H/T0/y1MXan7S+DG4tK+1LQJ8GvlJVe1fVgQwXMNpp3a9cL08HDG5NjcGt5eYZwN1VdfJMQ1VdCHwjybuSXNSun/wbcP/o+fSZbZOclOQlbfmaJCcmOb+95gnt4lnHAL/frsH880l+ve33wiRf25Cd1crkRaa03DwRWDNH+68B+wM/C+wIfHvMkL2pqg5I8mrgf1bVy5KcDNxRVX8KkOS7wKE1XGxo28XohLQujri1UhwCfLSq7q2qG4CvAgeP8bqZC2WtYbjG+ly+CXwwycsZbgwiTZTBreXmYuDA9dj+Hh7472CLWet/3H7eyzx/oVbVMcCbGa7wtibJDutxfGm9Gdxabs4GNh+5qD1J9gNuBX6j3ezgkQy3lzsP+B6wb7sq3bbAs8Y4xlqGW7PN7H/vqjq3qk4AfsQDL9EpLTrnuLWsVFUl+VXgPUneAPw7w+2ljgVWMVzJrYDXV9UPAZJ8guGKflczXOFvIX8P/G2SXwF+l+GDyscxXP3trHYMaWK8OqAkdcapEknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOvP/AZv7Fi6pQmBZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data.ordinalize([cat_covars, 'b_rel_family_id'])\n",
    "\n",
    "# Then plot just the categorical variables\n",
    "data.plot(scope='category', subjects='all', decode_values=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "municipal-worcester",
   "metadata": {},
   "source": [
    "Let's plot the target variables as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "thousand-nickname",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b_bmi: 9478 rows\n"
     ]
    },
    {
     "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": [
    "data.plot('target')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jewish-short",
   "metadata": {},
   "source": [
    "Okay, we note that there are some extreme outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "numeric-helping",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 1 Rows\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "b_bmi   53.90845\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.filter_outliers_by_std(n_std=10, scope='target')\n",
    "data['target'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "afraid-specialist",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b_bmi: 9477 rows\n"
     ]
    },
    {
     "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": [
    "data.plot('target')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "comparable-market",
   "metadata": {},
   "source": [
    "Okay this maximum seem much more reasonable. Let's also assume that there may be some extreme values present in the input data as well, and that these represet corrupted data that we therefore want to drop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "public-office",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 62 Rows\n",
      "Dropped 14 Rows\n"
     ]
    }
   ],
   "source": [
    "# Repeat it twice, to deal with outliers at multiple scales\n",
    "data = data.filter_outliers_by_std(n_std=10, scope='float')\n",
    "data = data.filter_outliers_by_std(n_std=10, scope='float')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "verified-spray",
   "metadata": {},
   "source": [
    "Next, we consider splitting up out data with a global train and test split. This can be useful in some instances. Note that we also define a cv strategy which says to perform the train test split keeping members of the same family in the same fold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "urban-edinburgh",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing test split on: 9401 subjects.\n",
      "random_state: 5\n",
      "Test split size: 0.2\n",
      "\n",
      "Performed train/test split\n",
      "Train size: 7481\n",
      "Test size:  1920\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        <h3>Data</h3><div>\n",
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       "      <th></th>\n",
       "      <th>b_agemos</th>\n",
       "      <th>b_demo_highest_education_categories</th>\n",
       "      <th>b_mri_info_deviceserialnumber</th>\n",
       "      <th>b_race_ethnicity_categories</th>\n",
       "      <th>b_sex</th>\n",
       "      <th>dmri_dti_full_fa_subcort_aseg_accumbens_area_lh</th>\n",
       "      <th>dmri_dti_full_fa_subcort_aseg_accumbens_area_rh</th>\n",
       "      <th>dmri_dti_full_fa_subcort_aseg_amygdala_lh</th>\n",
       "      <th>dmri_dti_full_fa_subcort_aseg_amygdala_rh</th>\n",
       "      <th>dmri_dti_full_fa_subcort_aseg_caudate_lh</th>\n",
       "      <th>...</th>\n",
       "      <th>smri_vol_scs_subcorticalgv</th>\n",
       "      <th>smri_vol_scs_suprateialv</th>\n",
       "      <th>smri_vol_scs_tplh</th>\n",
       "      <th>smri_vol_scs_tprh</th>\n",
       "      <th>smri_vol_scs_vedclh</th>\n",
       "      <th>smri_vol_scs_vedcrh</th>\n",
       "      <th>smri_vol_scs_wholeb</th>\n",
       "      <th>smri_vol_scs_wmhint</th>\n",
       "      <th>smri_vol_scs_wmhintlh</th>\n",
       "      <th>smri_vol_scs_wmhintrh</th>\n",
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       "      <td>0.20000</td>\n",
       "      <td>0.17713</td>\n",
       "      <td>0.15848</td>\n",
       "      <td>...</td>\n",
       "      <td>62,550.00000</td>\n",
       "      <td>1,078,644.86257</td>\n",
       "      <td>8,222.60000</td>\n",
       "      <td>7,571.40000</td>\n",
       "      <td>3,872.50000</td>\n",
       "      <td>3,837.40000</td>\n",
       "      <td>1,197,394.06258</td>\n",
       "      <td>769.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
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       "      <th style=\"background: RGBA(249, 121, 93, .25)\">2</th>\n",
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       "      <td>0.23221</td>\n",
       "      <td>0.22298</td>\n",
       "      <td>0.22545</td>\n",
       "      <td>...</td>\n",
       "      <td>60,695.00000</td>\n",
       "      <td>1,133,471.50460</td>\n",
       "      <td>7,040.50000</td>\n",
       "      <td>6,752.90000</td>\n",
       "      <td>4,588.80000</td>\n",
       "      <td>4,631.50000</td>\n",
       "      <td>1,291,126.70460</td>\n",
       "      <td>1,114.70000</td>\n",
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       "      <td>0.18840</td>\n",
       "      <td>0.15135</td>\n",
       "      <td>0.16215</td>\n",
       "      <td>0.18848</td>\n",
       "      <td>...</td>\n",
       "      <td>65,614.00000</td>\n",
       "      <td>1,055,580.45187</td>\n",
       "      <td>8,365.70000</td>\n",
       "      <td>7,656.40000</td>\n",
       "      <td>4,600.10000</td>\n",
       "      <td>4,920.60000</td>\n",
       "      <td>1,189,841.05187</td>\n",
       "      <td>1,788.50000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">4</th>\n",
       "      <td>115</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.18902</td>\n",
       "      <td>0.21375</td>\n",
       "      <td>0.23945</td>\n",
       "      <td>0.20581</td>\n",
       "      <td>0.20100</td>\n",
       "      <td>...</td>\n",
       "      <td>59,174.00000</td>\n",
       "      <td>1,010,567.33270</td>\n",
       "      <td>6,577.90000</td>\n",
       "      <td>6,612.70000</td>\n",
       "      <td>3,434.30000</td>\n",
       "      <td>3,942.60000</td>\n",
       "      <td>1,144,069.73270</td>\n",
       "      <td>1,036.30000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(249, 121, 93, .25)\">9717</th>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.16392</td>\n",
       "      <td>0.17222</td>\n",
       "      <td>0.19906</td>\n",
       "      <td>0.17829</td>\n",
       "      <td>0.15375</td>\n",
       "      <td>...</td>\n",
       "      <td>47,422.00000</td>\n",
       "      <td>893,537.35606</td>\n",
       "      <td>6,066.30000</td>\n",
       "      <td>5,342.00000</td>\n",
       "      <td>2,808.40000</td>\n",
       "      <td>3,469.60000</td>\n",
       "      <td>999,770.35606</td>\n",
       "      <td>1,152.40000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(249, 121, 93, .25)\">9720</th>\n",
       "      <td>129</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.23469</td>\n",
       "      <td>0.25338</td>\n",
       "      <td>0.25971</td>\n",
       "      <td>0.26321</td>\n",
       "      <td>0.22641</td>\n",
       "      <td>...</td>\n",
       "      <td>59,348.00000</td>\n",
       "      <td>1,070,071.00812</td>\n",
       "      <td>7,036.60000</td>\n",
       "      <td>6,669.10000</td>\n",
       "      <td>3,817.40000</td>\n",
       "      <td>4,103.40000</td>\n",
       "      <td>1,198,435.10812</td>\n",
       "      <td>794.90000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(249, 121, 93, .25)\">9721</th>\n",
       "      <td>108</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15469</td>\n",
       "      <td>0.20223</td>\n",
       "      <td>0.17145</td>\n",
       "      <td>0.15300</td>\n",
       "      <td>0.15900</td>\n",
       "      <td>...</td>\n",
       "      <td>63,328.00000</td>\n",
       "      <td>1,093,051.50897</td>\n",
       "      <td>8,123.20000</td>\n",
       "      <td>7,346.20000</td>\n",
       "      <td>3,992.80000</td>\n",
       "      <td>4,219.80000</td>\n",
       "      <td>1,213,030.80897</td>\n",
       "      <td>805.50000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">9722</th>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.16530</td>\n",
       "      <td>0.22484</td>\n",
       "      <td>0.19524</td>\n",
       "      <td>0.16084</td>\n",
       "      <td>0.16671</td>\n",
       "      <td>...</td>\n",
       "      <td>57,037.00000</td>\n",
       "      <td>997,648.58273</td>\n",
       "      <td>6,692.50000</td>\n",
       "      <td>6,437.90000</td>\n",
       "      <td>3,833.40000</td>\n",
       "      <td>3,887.70000</td>\n",
       "      <td>1,127,133.48273</td>\n",
       "      <td>1,477.90000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">9723</th>\n",
       "      <td>113</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.15050</td>\n",
       "      <td>0.16066</td>\n",
       "      <td>0.16982</td>\n",
       "      <td>0.16484</td>\n",
       "      <td>0.15355</td>\n",
       "      <td>...</td>\n",
       "      <td>61,090.00000</td>\n",
       "      <td>989,701.56266</td>\n",
       "      <td>7,113.50000</td>\n",
       "      <td>6,835.30000</td>\n",
       "      <td>4,029.60000</td>\n",
       "      <td>3,826.00000</td>\n",
       "      <td>1,134,202.76266</td>\n",
       "      <td>2,304.80000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9401 rows × 666 columns</p>\n",
       "<p style=\"margin-top: .35em\"><span style=\"background: RGBA(176, 224, 230, .25)\">7481 rows × 666 columns - Train Set </span></p><p style=\"margin-top: .35em\"><span style=\"background: RGBA(249, 121, 93, .25)\">1920 rows × 666 columns - Test Set </span></p></div></div>\n",
       "<div style=\"float: left; padding: 10px;\">\n",
       "        <h3>Target</h3><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>b_bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">0</th>\n",
       "      <td>15.17507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">1</th>\n",
       "      <td>16.45090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(249, 121, 93, .25)\">2</th>\n",
       "      <td>24.43703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">3</th>\n",
       "      <td>17.38701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">4</th>\n",
       "      <td>17.59670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(249, 121, 93, .25)\">9717</th>\n",
       "      <td>16.26895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(249, 121, 93, .25)\">9720</th>\n",
       "      <td>31.95732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(249, 121, 93, .25)\">9721</th>\n",
       "      <td>14.19771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">9722</th>\n",
       "      <td>24.42694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">9723</th>\n",
       "      <td>18.25486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9401 rows × 1 columns</p>\n",
       "<p style=\"margin-top: .35em\"><span style=\"background: RGBA(176, 224, 230, .25)\">7481 rows × 1 columns - Train Set </span></p><p style=\"margin-top: .35em\"><span style=\"background: RGBA(249, 121, 93, .25)\">1920 rows × 1 columns - Test Set </span></p></div></div>\n",
       "<div style=\"float: left; padding: 10px;\">\n",
       "        <h3>Non Input</h3><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>b_rel_family_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">0</th>\n",
       "      <td>1589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">1</th>\n",
       "      <td>7867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(249, 121, 93, .25)\">2</th>\n",
       "      <td>5174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">3</th>\n",
       "      <td>7864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">4</th>\n",
       "      <td>5175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(249, 121, 93, .25)\">9717</th>\n",
       "      <td>8070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(249, 121, 93, .25)\">9720</th>\n",
       "      <td>1814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(249, 121, 93, .25)\">9721</th>\n",
       "      <td>5719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">9722</th>\n",
       "      <td>6412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th style=\"background: RGBA(176, 224, 230, .25)\">9723</th>\n",
       "      <td>1740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9401 rows × 1 columns</p>\n",
       "<p style=\"margin-top: .35em\"><span style=\"background: RGBA(176, 224, 230, .25)\">7481 rows × 1 columns - Train Set </span></p><p style=\"margin-top: .35em\"><span style=\"background: RGBA(249, 121, 93, .25)\">1920 rows × 1 columns - Test Set </span></p></div></div>\n"
      ],
      "text/plain": [
       "      smri_thick_cort_destrieux_g_and_s_frontomargin_lh  \\\n",
       "0                                               2.34300   \n",
       "1                                               2.74300   \n",
       "2                                               2.45400   \n",
       "3                                               2.96600   \n",
       "4                                               2.48500   \n",
       "...                                                 ...   \n",
       "9717                                            2.71000   \n",
       "9720                                            2.42100   \n",
       "9721                                            2.69000   \n",
       "9722                                            2.56400   \n",
       "9723                                            2.66500   \n",
       "\n",
       "      smri_thick_cort_destrieux_g_and_s_occipital_inf_lh  \\\n",
       "0                                               2.43700    \n",
       "1                                               2.31000    \n",
       "2                                               2.80100    \n",
       "3                                               2.60500    \n",
       "4                                               2.68100    \n",
       "...                                                 ...    \n",
       "9717                                            2.45900    \n",
       "9720                                            2.63900    \n",
       "9721                                            2.71200    \n",
       "9722                                            2.52600    \n",
       "9723                                            2.91500    \n",
       "\n",
       "      smri_thick_cort_destrieux_g_and_s_paracentral_lh  \\\n",
       "0                                              2.41200   \n",
       "1                                              2.86600   \n",
       "2                                              2.70300   \n",
       "3                                              3.19700   \n",
       "4                                              2.65500   \n",
       "...                                                ...   \n",
       "9717                                           2.70200   \n",
       "9720                                           2.50500   \n",
       "9721                                           2.61900   \n",
       "9722                                           2.80000   \n",
       "9723                                           2.66100   \n",
       "\n",
       "      smri_thick_cort_destrieux_g_and_s_subcentral_lh  \\\n",
       "0                                             2.81300   \n",
       "1                                             3.33100   \n",
       "2                                             2.45700   \n",
       "3                                             3.09100   \n",
       "4                                             2.60800   \n",
       "...                                               ...   \n",
       "9717                                          2.79600   \n",
       "9720                                          3.20100   \n",
       "9721                                          2.86100   \n",
       "9722                                          3.10400   \n",
       "9723                                          3.11400   \n",
       "\n",
       "      smri_thick_cort_destrieux_g_and_s_transv_frontopol_lh  \\\n",
       "0                                               2.69700       \n",
       "1                                               3.07600       \n",
       "2                                               2.62100       \n",
       "3                                               3.25200       \n",
       "4                                               3.05000       \n",
       "...                                                 ...       \n",
       "9717                                            2.56900       \n",
       "9720                                            2.69200       \n",
       "9721                                            2.96000       \n",
       "9722                                            2.89300       \n",
       "9723                                            2.96800       \n",
       "\n",
       "      smri_thick_cort_destrieux_g_and_s_cingul_ant_lh  \\\n",
       "0                                             2.92300   \n",
       "1                                             3.24900   \n",
       "2                                             3.17200   \n",
       "3                                             3.12600   \n",
       "4                                             3.23100   \n",
       "...                                               ...   \n",
       "9717                                          2.94600   \n",
       "9720                                          3.16600   \n",
       "9721                                          2.92900   \n",
       "9722                                          3.08100   \n",
       "9723                                          3.16700   \n",
       "\n",
       "      smri_thick_cort_destrieux_g_and_s_cingul_mid_ant_lh  \\\n",
       "0                                               2.91300     \n",
       "1                                               3.23000     \n",
       "2                                               3.13800     \n",
       "3                                               3.24100     \n",
       "4                                               2.95600     \n",
       "...                                                 ...     \n",
       "9717                                            2.88900     \n",
       "9720                                            2.87400     \n",
       "9721                                            3.00500     \n",
       "9722                                            3.07900     \n",
       "9723                                            3.05800     \n",
       "\n",
       "      smri_thick_cort_destrieux_g_and_s_cingul_mid_post_lh  \\\n",
       "0                                               2.75700      \n",
       "1                                               3.08100      \n",
       "2                                               2.84600      \n",
       "3                                               3.05700      \n",
       "4                                               2.84400      \n",
       "...                                                 ...      \n",
       "9717                                            2.72600      \n",
       "9720                                            2.79800      \n",
       "9721                                            2.88300      \n",
       "9722                                            2.86700      \n",
       "9723                                            2.97600      \n",
       "\n",
       "      smri_thick_cort_destrieux_g_cingul_post_dorsal_lh  \\\n",
       "0                                               3.18000   \n",
       "1                                               3.49000   \n",
       "2                                               3.43000   \n",
       "3                                               3.44800   \n",
       "4                                               3.15200   \n",
       "...                                                 ...   \n",
       "9717                                            3.26200   \n",
       "9720                                            3.36600   \n",
       "9721                                            3.17500   \n",
       "9722                                            3.42400   \n",
       "9723                                            3.35500   \n",
       "\n",
       "      smri_thick_cort_destrieux_g_cingul_post_ventral_lh  ...  \\\n",
       "0                                               2.40700   ...   \n",
       "1                                               2.38600   ...   \n",
       "2                                               2.90700   ...   \n",
       "3                                               3.21400   ...   \n",
       "4                                               2.41400   ...   \n",
       "...                                                 ...   ...   \n",
       "9717                                            2.62300   ...   \n",
       "9720                                            2.70900   ...   \n",
       "9721                                            3.05500   ...   \n",
       "9722                                            2.43600   ...   \n",
       "9723                                            2.16800   ...   \n",
       "\n",
       "      dmri_dti_full_md_subcort_aseg_amygdala_rh  \\\n",
       "0                                       0.67408   \n",
       "1                                       0.66106   \n",
       "2                                       0.73185   \n",
       "3                                       0.67879   \n",
       "4                                       0.71735   \n",
       "...                                         ...   \n",
       "9717                                    0.68056   \n",
       "9720                                    0.73108   \n",
       "9721                                    0.66988   \n",
       "9722                                    0.67194   \n",
       "9723                                    0.69006   \n",
       "\n",
       "      dmri_dti_full_md_subcort_aseg_accumbens_area_rh  \\\n",
       "0                                             0.64020   \n",
       "1                                             0.63493   \n",
       "2                                             0.66539   \n",
       "3                                             0.61966   \n",
       "4                                             0.72282   \n",
       "...                                               ...   \n",
       "9717                                          0.63070   \n",
       "9720                                          0.68669   \n",
       "9721                                          0.63930   \n",
       "9722                                          0.64405   \n",
       "9723                                          0.66166   \n",
       "\n",
       "      dmri_dti_full_md_subcort_aseg_ventraldc_rh    b_bmi  b_sex  \\\n",
       "0                                        0.48485 15.17507      0   \n",
       "1                                        0.51481 16.45090      1   \n",
       "2                                        0.55961 24.43703      0   \n",
       "3                                        0.50990 17.38701      1   \n",
       "4                                        0.56224 17.59670      1   \n",
       "...                                          ...      ...    ...   \n",
       "9717                                     0.53454 16.26895      1   \n",
       "9720                                     0.53526 31.95732      1   \n",
       "9721                                     0.51814 14.19771      0   \n",
       "9722                                     0.52716 24.42694      1   \n",
       "9723                                     0.50890 18.25486      1   \n",
       "\n",
       "      b_demo_highest_education_categories  b_race_ethnicity_categories  \\\n",
       "0                                       1                            1   \n",
       "1                                       4                            0   \n",
       "2                                       2                            0   \n",
       "3                                       4                            0   \n",
       "4                                       4                            0   \n",
       "...                                   ...                          ...   \n",
       "9717                                    1                            2   \n",
       "9720                                    4                            0   \n",
       "9721                                    2                            1   \n",
       "9722                                    2                            0   \n",
       "9723                                    4                            0   \n",
       "\n",
       "      b_agemos  b_mri_info_deviceserialnumber  b_rel_family_id  \n",
       "0          124                             26             1589  \n",
       "1          122                              2             7867  \n",
       "2          114                             13             5174  \n",
       "3          130                              2             7864  \n",
       "4          115                             20             5175  \n",
       "...        ...                            ...              ...  \n",
       "9717       119                              2             8070  \n",
       "9720       129                              7             1814  \n",
       "9721       108                              0             5719  \n",
       "9722       110                             17             6412  \n",
       "9723       113                             26             1740  \n",
       "\n",
       "[9401 rows x 668 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's say we want to keep family members in the same train or test split\n",
    "cv_strategy = bp.CVStrategy(groups='b_rel_family_id')\n",
    "\n",
    "# Test split\n",
    "data = data.set_test_split(.2, random_state=5, cv_strategy=cv_strategy)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "enormous-construction",
   "metadata": {},
   "source": [
    "## Single Training Set Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "composed-inquiry",
   "metadata": {},
   "source": [
    "Our Dataset is now fully prepared. We can now define and evaluate a machine learning pipeline.\n",
    "We will start by considering a pipeline which a few steps, these are:\n",
    "\n",
    "1. Winsorize just the brain data.\n",
    "2. Perform Robust Scaling on any 'float' type columns, 'brain' or 'covars'\n",
    "3. One Hot Encode any categorical features\n",
    "4. Fit an Elastic-Net Regression with nested random hyper-parameter search\n",
    "\n",
    "Let's use one other feature of the toolbox, that is a custom cross-validation strategy. This is the same idea that we used when defining the train-test split, but now we want it to apply both during the evaluation and during the splits made when evaluating hyper-parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "collect-tribe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[Scaler(extra_params={'quantile_range': (2, 98)},\n",
       "                       obj='winsorize', scope='brain'),\n",
       "                Scaler(obj='standard'),\n",
       "                Transformer(obj='one hot encoder', scope='category'),\n",
       "                Model(obj='elastic',\n",
       "                      param_search=ParamSearch(cv=CV(cv_strategy=CVStrategy(groups='b_rel_family_id')),\n",
       "                                               n_iter=60),\n",
       "                      params=1)])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1\n",
    "w_scaler = bp.Scaler('winsorize', quantile_range=(2, 98), scope='brain')\n",
    "\n",
    "#2\n",
    "s_scaler = bp.Scaler('standard', scope='float')\n",
    "\n",
    "#3\n",
    "ohe = bp.Transformer('one hot encoder', scope='category')\n",
    "\n",
    "#4\n",
    "param_search=bp.ParamSearch('RandomSearch', n_iter=60,\n",
    "                            cv=bp.CV(splits=3, cv_strategy=cv_strategy))\n",
    "elastic = bp.Model('elastic', params=1,\n",
    "                   param_search=param_search)\n",
    "\n",
    "# Now we can actually defined the pipeline\n",
    "pipe = bp.Pipeline(steps=[w_scaler, s_scaler, ohe, elastic])\n",
    "pipe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ancient-ancient",
   "metadata": {},
   "source": [
    "Let's say we want to use a 5-fold CV to evaluate this model on just the training set. We can first define the cv, the same as for the param_search above, but this time with splits=5. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "executive-infrastructure",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CV(cv_strategy=CVStrategy(groups='b_rel_family_id'), splits=5)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv=bp.CV(splits=5, n_repeats=1, cv_strategy=cv_strategy)\n",
    "cv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elect-necklace",
   "metadata": {},
   "source": [
    "And we will make a problem_spec to store some common params. In this case the random state for reproducibility of results and the number of jobs to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cultural-logic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ProblemSpec(n_jobs=8, random_state=10)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use problem spec just to store n jobs and random state\n",
    "ps = bp.ProblemSpec(n_jobs=8, random_state=10)\n",
    "ps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wired-charleston",
   "metadata": {},
   "source": [
    "Now we are ready to evaluate this pipeline, let's check an example using the function evaluate.\n",
    "\n",
    "We will set just one specific parameter to start, which is a scope of 'brain' to say\n",
    "we just want to run a model with just the brain features (i.e., not the co-variates)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "pleased-panic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "779b2929beec46d4852596afd5e13ddd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'explained_variance': 0.25655836420425737, 'neg_mean_squared_error': -12.15230817006252}\n",
       "std_scores = {'explained_variance': 0.02393695017649266, 'neg_mean_squared_error': 1.1099605948532965}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores', 'fis_', 'coef_']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'get_fis', 'get_coef_', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=8, problem_type='regression', random_state=10, scope='brain',\n",
       "            scorer={'explained_variance': make_scorer(explained_variance_score),\n",
       "                    'neg_mean_squared_error': make_scorer(mean_squared_error, greater_is_better=False)},\n",
       "            subjects='train', target='b_bmi')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator = bp.evaluate(pipeline=pipe,\n",
    "                        dataset=data,\n",
    "                        problem_spec=ps,\n",
    "                        scope='brain',\n",
    "                        cv=cv)\n",
    "evaluator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "italic-korean",
   "metadata": {},
   "source": [
    "Wait, what subset of subjects did we use? We want to make sure we are using just the training set of subjects at this stage. We just used default for the subjects, which in this case, i.e., when their is a test set defined, uses the training set as the subjects when passed to evaluate. We can confirm this by checking the intersection of the first train and validation fold with the test subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "seeing-prairie",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Int64Index([], dtype='int64'), Int64Index([], dtype='int64'))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator.train_subjects[0].intersection(data.test_subjects), evaluator.val_subjects[0].intersection(data.test_subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "broadband-intermediate",
   "metadata": {},
   "source": [
    "As we can see, they are empty. We could also specify subjects='train' above to make sure this behavior occured."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "virgin-battlefield",
   "metadata": {},
   "source": [
    "## How to employ Test Set?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sorted-attitude",
   "metadata": {},
   "source": [
    "Next, we will discuss briefly two methods of evaluating on the test set.\n",
    "\n",
    "1. There is a notion in some of the literature that the test set should be used to evaluate an existing estimator. In this case, that would consist of selecting from the 5 trained models we evaluated on the train set, and testing the one which performed best.\n",
    "\n",
    "2. On the otherhand, we could instead re-train an estimator on the full training set and then test that on the test set. This is actually the reccomended strategy, but its still worth comparing both below.\n",
    "\n",
    "Let's first do strategy 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "heated-break",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2724861166614897\n"
     ]
    }
   ],
   "source": [
    "# Get best model\n",
    "best_model_ind = np.argmax(evaluator.scores['explained_variance'])\n",
    "best_model = evaluator.estimators[best_model_ind]\n",
    "\n",
    "# Get the correct test data - setting the problem spec\n",
    "# as the problem spec saved when running this evaluator\n",
    "X_test, y_test = data.get_Xy(problem_spec=evaluator.ps,\n",
    "                             subjects='test')\n",
    "\n",
    "# Score\n",
    "score = best_model.score(X_test, y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "three-invitation",
   "metadata": {},
   "source": [
    "Next, lets compare that with re-training our model on the full training set, then testing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "affiliated-blues",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8614f7705d3445d4a331bf27ea2a6ba3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'explained_variance': 0.27514029708500876, 'neg_mean_squared_error': -12.269150360411253}\n",
       "std_scores = {'explained_variance': 0.0, 'neg_mean_squared_error': 0.0}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores', 'fis_', 'coef_']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'get_fis', 'get_coef_', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=8, problem_type='regression', random_state=10, scope='brain',\n",
       "            scorer={'explained_variance': make_scorer(explained_variance_score),\n",
       "                    'neg_mean_squared_error': make_scorer(mean_squared_error, greater_is_better=False)},\n",
       "            subjects='all', target='b_bmi')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator = bp.evaluate(pipeline=pipe,\n",
    "                        dataset=data,\n",
    "                        problem_spec=ps,\n",
    "                        scope='brain', # Same as before\n",
    "                        cv='test')\n",
    "\n",
    "evaluator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "competent-psychiatry",
   "metadata": {},
   "source": [
    "Note that in this case, when cv='test' then all of the subjects will be used. If we wanted to make this behavior explicit we couldpass subjects='all'.\n",
    "\n",
    "We can see that the results difer, though not hugely. One explanation is that at these sample sizes we still get boosts in performance from sample size, which means we expect our final model trained on the full and larger training set to do better. Another contributing factor is that by selecting from the 5-folds the model which does 'best' we may actually be choosing an overfit model which happened to do better on its corresponding validation set. We therefore reccomend the latter strategy.\n",
    "\n",
    "In fact, we can formalize this intution, and test it on just the training set. We can do this because really the first method is just a type of meta-estimator. What it does is just in an internal nesting step, trains five models and selects the best one. We can formalize this as a custom model and compare it below.\n",
    "\n",
    "We will also change the pipeline being compared a little bit for simplicity, replacing the elastic net we made via BPt with the ElasticNetCV object from scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "veterinary-irrigation",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.linear_model import ElasticNetCV\n",
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "class BestOfModel(BaseEstimator):\n",
    "    \n",
    "    def __init__(self, estimator, cv=5):\n",
    "        \n",
    "        self.estimator = estimator\n",
    "        self.cv = cv\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        \n",
    "        scores = cross_validate(self.estimator, X, y, cv=self.cv,\n",
    "                                return_estimator=True)\n",
    "        \n",
    "        best_ind = np.argmax(scores['test_score'])\n",
    "        self.estimator_ = scores['estimator'][best_ind]\n",
    "        \n",
    "    def predict(self, X):\n",
    "        \n",
    "        return self.estimator_.predict(X)\n",
    "\n",
    "\n",
    "# The new elastic net cv to use\n",
    "new_elastic = ElasticNetCV()\n",
    "\n",
    "# The best of model\n",
    "best_of_model = bp.Model(\n",
    "    BestOfModel(estimator=new_elastic, cv=5))\n",
    "\n",
    "# The static model\n",
    "static_model = bp.Model(new_elastic)\n",
    "\n",
    "# Use the same initial steps as before, but now with the best of model and the static models\n",
    "best_of_pipe = bp.Pipeline(steps=[w_scaler, s_scaler, ohe, best_of_model])\n",
    "static_pipe = bp.Pipeline(steps=[w_scaler, s_scaler, ohe, static_model])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "peripheral-trial",
   "metadata": {},
   "source": [
    "Let's try the best of model first, evaluating it on the training set with 5-fold CV.\n",
    "\n",
    "Note: This is going to be a bit slow as each time a model is trained in one of the 5-folds it needs to internally train 5 models. So it should take roughly 5x longer to evaluate then it's static counterpart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "responsible-circus",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44aa5bb98ff64190828b57c4cf2ef822",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'explained_variance': 0.2485223109713471, 'neg_mean_squared_error': -12.281307958444154}\n",
       "std_scores = {'explained_variance': 0.024857512022276998, 'neg_mean_squared_error': 1.112832141546892}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=8, problem_type='regression', random_state=10, scope='brain',\n",
       "            scorer={'explained_variance': make_scorer(explained_variance_score),\n",
       "                    'neg_mean_squared_error': make_scorer(mean_squared_error, greater_is_better=False)},\n",
       "            subjects='train', target='b_bmi')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator = bp.evaluate(pipeline=best_of_pipe,\n",
    "                        dataset=data,\n",
    "                        problem_spec=ps, \n",
    "                        scope='brain', \n",
    "                        cv=cv)\n",
    "\n",
    "evaluator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "external-dependence",
   "metadata": {},
   "source": [
    "Next, let's run its corresponding 'static' counterpart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "assigned-helena",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eda02296b3b64194833272a579fc801d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'explained_variance': 0.25832965982663025, 'neg_mean_squared_error': -12.120236239339546}\n",
       "std_scores = {'explained_variance': 0.02451883500218578, 'neg_mean_squared_error': 1.0828442666660394}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores', 'fis_', 'coef_']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'get_fis', 'get_coef_', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=8, problem_type='regression', random_state=10, scope='brain',\n",
       "            scorer={'explained_variance': make_scorer(explained_variance_score),\n",
       "                    'neg_mean_squared_error': make_scorer(mean_squared_error, greater_is_better=False)},\n",
       "            subjects='train', target='b_bmi')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator = bp.evaluate(pipeline=static_pipe,\n",
    "                        dataset=data,\n",
    "                        problem_spec=ps,\n",
    "                        scope='brain', \n",
    "                        cv=cv)\n",
    "\n",
    "evaluator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bridal-clearance",
   "metadata": {},
   "source": [
    "These results confirm the intution, namely that it is better to re-train using as many subjects as possible rather than chose a 'best model' from an internal CV."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "necessary-academy",
   "metadata": {},
   "source": [
    "## Compare & Evaluate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elder-intention",
   "metadata": {},
   "source": [
    "Let's now try out a newer feature the input class Compare. Let's define as our options to compare using different subsets of the input data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "voluntary-typing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7ed3886c8aa43f38561b0065f9418c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Compare:   0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2929fd8e79674b818fc19e12074164f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_scores_explained_variance</th>\n",
       "      <th>mean_scores_neg_mean_squared_error</th>\n",
       "      <th>std_scores_explained_variance</th>\n",
       "      <th>std_scores_neg_mean_squared_error</th>\n",
       "      <th>mean_timing_fit</th>\n",
       "      <th>mean_timing_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scope</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>covars</th>\n",
       "      <td>0.11317</td>\n",
       "      <td>-14.51761</td>\n",
       "      <td>0.01192</td>\n",
       "      <td>1.45243</td>\n",
       "      <td>4.73712</td>\n",
       "      <td>0.03311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brain</th>\n",
       "      <td>0.25656</td>\n",
       "      <td>-12.15231</td>\n",
       "      <td>0.02394</td>\n",
       "      <td>1.10996</td>\n",
       "      <td>15.58119</td>\n",
       "      <td>0.05231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>0.27908</td>\n",
       "      <td>-11.78751</td>\n",
       "      <td>0.02294</td>\n",
       "      <td>1.10537</td>\n",
       "      <td>15.45347</td>\n",
       "      <td>0.07103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thick</th>\n",
       "      <td>0.10118</td>\n",
       "      <td>-14.69564</td>\n",
       "      <td>0.02711</td>\n",
       "      <td>1.38971</td>\n",
       "      <td>8.06942</td>\n",
       "      <td>0.01529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>0.02774</td>\n",
       "      <td>-15.89653</td>\n",
       "      <td>0.00991</td>\n",
       "      <td>1.46288</td>\n",
       "      <td>7.71240</td>\n",
       "      <td>0.01417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subcort</th>\n",
       "      <td>0.05189</td>\n",
       "      <td>-15.50615</td>\n",
       "      <td>0.00831</td>\n",
       "      <td>1.39405</td>\n",
       "      <td>4.38906</td>\n",
       "      <td>0.02439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti_fa</th>\n",
       "      <td>0.10302</td>\n",
       "      <td>-14.66791</td>\n",
       "      <td>0.00659</td>\n",
       "      <td>1.39366</td>\n",
       "      <td>8.24234</td>\n",
       "      <td>0.01827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti_md</th>\n",
       "      <td>0.14541</td>\n",
       "      <td>-13.96297</td>\n",
       "      <td>0.01127</td>\n",
       "      <td>1.21864</td>\n",
       "      <td>8.31585</td>\n",
       "      <td>0.01754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean_scores_explained_variance  mean_scores_neg_mean_squared_error  \\\n",
       "scope                                                                         \n",
       "covars                          0.11317                           -14.51761   \n",
       "brain                           0.25656                           -12.15231   \n",
       "all                             0.27908                           -11.78751   \n",
       "thick                           0.10118                           -14.69564   \n",
       "area                            0.02774                           -15.89653   \n",
       "subcort                         0.05189                           -15.50615   \n",
       "dti_fa                          0.10302                           -14.66791   \n",
       "dti_md                          0.14541                           -13.96297   \n",
       "\n",
       "         std_scores_explained_variance  std_scores_neg_mean_squared_error  \\\n",
       "scope                                                                       \n",
       "covars                         0.01192                            1.45243   \n",
       "brain                          0.02394                            1.10996   \n",
       "all                            0.02294                            1.10537   \n",
       "thick                          0.02711                            1.38971   \n",
       "area                           0.00991                            1.46288   \n",
       "subcort                        0.00831                            1.39405   \n",
       "dti_fa                         0.00659                            1.39366   \n",
       "dti_md                         0.01127                            1.21864   \n",
       "\n",
       "         mean_timing_fit  mean_timing_score  \n",
       "scope                                        \n",
       "covars           4.73712            0.03311  \n",
       "brain           15.58119            0.05231  \n",
       "all             15.45347            0.07103  \n",
       "thick            8.06942            0.01529  \n",
       "area             7.71240            0.01417  \n",
       "subcort          4.38906            0.02439  \n",
       "dti_fa           8.24234            0.01827  \n",
       "dti_md           8.31585            0.01754  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_scopes = bp.Compare(['covars', 'brain', 'all',\n",
    "                             'thick', 'area', 'subcort',\n",
    "                             'dti_fa', 'dti_md'])\n",
    "\n",
    "evaluators = bp.evaluate(pipeline=pipe,\n",
    "                         scope=compare_scopes,\n",
    "                         dataset=data,\n",
    "                         problem_spec=ps,\n",
    "                         cv=cv)\n",
    "\n",
    "# Look at a summary of the results - note this option is only available after a call to evaluate with\n",
    "# Compare's has been made!\n",
    "evaluators.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "whole-mason",
   "metadata": {},
   "source": [
    "We can also try another method available which will run a pairwise model ttest comparison between all options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "entertaining-beaver",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scope (1)</th>\n",
       "      <th>scope (2)</th>\n",
       "      <th>t_stat</th>\n",
       "      <th>p_val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>covars</td>\n",
       "      <td>brain</td>\n",
       "      <td>-11.36825</td>\n",
       "      <td>0.00478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>covars</td>\n",
       "      <td>all</td>\n",
       "      <td>-14.32368</td>\n",
       "      <td>0.00193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>covars</td>\n",
       "      <td>thick</td>\n",
       "      <td>0.86450</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>covars</td>\n",
       "      <td>area</td>\n",
       "      <td>7.66506</td>\n",
       "      <td>0.02180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>covars</td>\n",
       "      <td>subcort</td>\n",
       "      <td>9.07301</td>\n",
       "      <td>0.01145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>covars</td>\n",
       "      <td>dti_fa</td>\n",
       "      <td>1.97369</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>covars</td>\n",
       "      <td>dti_md</td>\n",
       "      <td>-4.56856</td>\n",
       "      <td>0.14381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>brain</td>\n",
       "      <td>all</td>\n",
       "      <td>-15.67812</td>\n",
       "      <td>0.00135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>brain</td>\n",
       "      <td>thick</td>\n",
       "      <td>30.46195</td>\n",
       "      <td>0.00010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>brain</td>\n",
       "      <td>area</td>\n",
       "      <td>17.32117</td>\n",
       "      <td>0.00091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>brain</td>\n",
       "      <td>subcort</td>\n",
       "      <td>17.40024</td>\n",
       "      <td>0.00090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>brain</td>\n",
       "      <td>dti_fa</td>\n",
       "      <td>12.94696</td>\n",
       "      <td>0.00287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>brain</td>\n",
       "      <td>dti_md</td>\n",
       "      <td>11.98864</td>\n",
       "      <td>0.00388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>all</td>\n",
       "      <td>thick</td>\n",
       "      <td>31.58027</td>\n",
       "      <td>0.00008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>all</td>\n",
       "      <td>area</td>\n",
       "      <td>19.16281</td>\n",
       "      <td>0.00061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>all</td>\n",
       "      <td>subcort</td>\n",
       "      <td>20.49440</td>\n",
       "      <td>0.00047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>all</td>\n",
       "      <td>dti_fa</td>\n",
       "      <td>15.58172</td>\n",
       "      <td>0.00139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>all</td>\n",
       "      <td>dti_md</td>\n",
       "      <td>15.37322</td>\n",
       "      <td>0.00146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>thick</td>\n",
       "      <td>area</td>\n",
       "      <td>5.10927</td>\n",
       "      <td>0.09713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>thick</td>\n",
       "      <td>subcort</td>\n",
       "      <td>3.32372</td>\n",
       "      <td>0.40987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>thick</td>\n",
       "      <td>dti_fa</td>\n",
       "      <td>-0.14599</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>thick</td>\n",
       "      <td>dti_md</td>\n",
       "      <td>-3.56252</td>\n",
       "      <td>0.32946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>area</td>\n",
       "      <td>subcort</td>\n",
       "      <td>-2.76953</td>\n",
       "      <td>0.70498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>area</td>\n",
       "      <td>dti_fa</td>\n",
       "      <td>-11.38268</td>\n",
       "      <td>0.00476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>area</td>\n",
       "      <td>dti_md</td>\n",
       "      <td>-12.59938</td>\n",
       "      <td>0.00320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>subcort</td>\n",
       "      <td>dti_fa</td>\n",
       "      <td>-7.99938</td>\n",
       "      <td>0.01854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>subcort</td>\n",
       "      <td>dti_md</td>\n",
       "      <td>-31.20696</td>\n",
       "      <td>0.00009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>dti_fa</td>\n",
       "      <td>dti_md</td>\n",
       "      <td>-6.47580</td>\n",
       "      <td>0.04102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   scope (1) scope (2)    t_stat   p_val\n",
       "0     covars     brain -11.36825 0.00478\n",
       "1     covars       all -14.32368 0.00193\n",
       "2     covars     thick   0.86450 1.00000\n",
       "3     covars      area   7.66506 0.02180\n",
       "4     covars   subcort   9.07301 0.01145\n",
       "5     covars    dti_fa   1.97369 1.00000\n",
       "6     covars    dti_md  -4.56856 0.14381\n",
       "7      brain       all -15.67812 0.00135\n",
       "8      brain     thick  30.46195 0.00010\n",
       "9      brain      area  17.32117 0.00091\n",
       "10     brain   subcort  17.40024 0.00090\n",
       "11     brain    dti_fa  12.94696 0.00287\n",
       "12     brain    dti_md  11.98864 0.00388\n",
       "13       all     thick  31.58027 0.00008\n",
       "14       all      area  19.16281 0.00061\n",
       "15       all   subcort  20.49440 0.00047\n",
       "16       all    dti_fa  15.58172 0.00139\n",
       "17       all    dti_md  15.37322 0.00146\n",
       "18     thick      area   5.10927 0.09713\n",
       "19     thick   subcort   3.32372 0.40987\n",
       "20     thick    dti_fa  -0.14599 1.00000\n",
       "21     thick    dti_md  -3.56252 0.32946\n",
       "22      area   subcort  -2.76953 0.70498\n",
       "23      area    dti_fa -11.38268 0.00476\n",
       "24      area    dti_md -12.59938 0.00320\n",
       "25   subcort    dti_fa  -7.99938 0.01854\n",
       "26   subcort    dti_md -31.20696 0.00009\n",
       "27    dti_fa    dti_md  -6.47580 0.04102"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluators.pairwise_t_stats(metric='explained_variance')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "announced-dodge",
   "metadata": {},
   "source": [
    "We can also explicitly compare two evaluators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "peaceful-florence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        This method is designed to perform a statistical comparison\n",
      "        between the results from the evaluation stored in this object\n",
      "        and another instance of :class:`BPtEvaluator`. The statistics\n",
      "        produced here are explained in:\n",
      "        https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_stats.html\n",
      "\n",
      "        .. note::\n",
      "            In the case that the sizes of the training and validation sets\n",
      "            at each fold vary dramatically, it is unclear if this\n",
      "            statistics are still valid.\n",
      "            In that case, the mean train size and mean validation sizes\n",
      "            are employed when computing statistics.\n",
      "\n",
      "        Parameters\n",
      "        ------------\n",
      "        other : :class:`BPtEvaluator`\n",
      "            Another instance of :class:`BPtEvaluator` in which\n",
      "            to compare which. The cross-validation used\n",
      "            should be the same in both instances, otherwise\n",
      "            statistics will not be generated.\n",
      "\n",
      "        rope_interval : list or dict of\n",
      "            | This parameter allows for passing in a custom\n",
      "                region of practical equivalence interval (or rope interval)\n",
      "                a concept from bayesian statistics. If passed as\n",
      "                a list, this should be a list with two elements, describing\n",
      "                the difference in score which should be treated as two\n",
      "                models or runs being practically equivalent.\n",
      "\n",
      "            | Alternatively, in the case of multiple underlying\n",
      "                scorers / metrics. A dictionary, where keys correspond\n",
      "                to scorer / metric names can be passed with a separate\n",
      "                rope_interval for each. For example:\n",
      "\n",
      "            ::\n",
      "\n",
      "                rope_interval = {'explained_variance': [-0.01, 0.01],\n",
      "                                 'neg_mean_squared_error': [-1, 1]}\n",
      "\n",
      "            This example would define separate rope regions depending\n",
      "            on the metric.\n",
      "\n",
      "            ::\n",
      "\n",
      "                default = [-0.01, 0.01]\n",
      "\n",
      "        Returns\n",
      "        -------\n",
      "        compare_df : pandas DataFrame\n",
      "            | The returned DataFrame will generate separate rows\n",
      "                for all overlapping metrics / scorers between the\n",
      "                evaluators being compared. Further, columns with\n",
      "                statistics of interest will be generated:\n",
      "\n",
      "                - 'mean_diff'\n",
      "                    The mean score minus other's mean score\n",
      "\n",
      "                - 'std_diff'\n",
      "                    The std minus other's std\n",
      "\n",
      "            | Further, only in the case that the cross-validation\n",
      "                folds are identical between the comparisons,\n",
      "                the following additional columns will be generated:\n",
      "\n",
      "                - 't_stat'\n",
      "                    Corrected paired ttest statistic.\n",
      "\n",
      "                - 'p_val'\n",
      "                    The p value for the corrected paired ttest statistic.\n",
      "\n",
      "                - 'better_prob'\n",
      "                    The probability that this evaluated option is better than\n",
      "                    the other evaluated option under a bayesian framework and\n",
      "                    the passed value of rope_interval. See sklearn example\n",
      "                    for more details.\n",
      "\n",
      "                - 'worse_prob'\n",
      "                    The probability that this evaluated option is worse than\n",
      "                    the other evaluated option under a bayesian framework and\n",
      "                    the passed value of rope_interval. See sklearn example\n",
      "                    for more details.\n",
      "\n",
      "                - 'rope_prob'\n",
      "                    The probability that this evaluated option is equivalent to\n",
      "                    the other evaluated option under a bayesian framework and\n",
      "                    the passed value of rope_interval. See sklearn example\n",
      "                    for more details.\n",
      "\n",
      "        \n",
      "\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_diff</th>\n",
       "      <th>std_diff</th>\n",
       "      <th>t_stat</th>\n",
       "      <th>p_val</th>\n",
       "      <th>better_prob</th>\n",
       "      <th>worse_prob</th>\n",
       "      <th>rope_prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>explained_variance</th>\n",
       "      <td>-0.14339</td>\n",
       "      <td>-0.01202</td>\n",
       "      <td>-11.36825</td>\n",
       "      <td>0.00017</td>\n",
       "      <td>0.00013</td>\n",
       "      <td>0.99977</td>\n",
       "      <td>0.00009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neg_mean_squared_error</th>\n",
       "      <td>-2.36530</td>\n",
       "      <td>0.34247</td>\n",
       "      <td>-8.09697</td>\n",
       "      <td>0.00063</td>\n",
       "      <td>0.00062</td>\n",
       "      <td>0.99936</td>\n",
       "      <td>0.00002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        mean_diff  std_diff    t_stat   p_val  better_prob  \\\n",
       "explained_variance       -0.14339  -0.01202 -11.36825 0.00017      0.00013   \n",
       "neg_mean_squared_error   -2.36530   0.34247  -8.09697 0.00063      0.00062   \n",
       "\n",
       "                        worse_prob  rope_prob  \n",
       "explained_variance         0.99977    0.00009  \n",
       "neg_mean_squared_error     0.99936    0.00002  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e1 = evaluators['scope=covars']\n",
    "e2 = evaluators['scope=brain']\n",
    "\n",
    "# Look at function docstring\n",
    "print('       ', e1.compare.__doc__)\n",
    "print()\n",
    "\n",
    "e1.compare(e2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sufficient-poker",
   "metadata": {},
   "source": [
    "Okay so it looks like it ran all the different combinations, but how do we look at each indivudal evaluator / the full results?? There are few different ways to index the object storing the different evaluators, but its essentially a dictionary. We can index one run as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "increasing-throw",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'explained_variance': 0.11316930054010477, 'neg_mean_squared_error': -14.517611278919068}\n",
       "std_scores = {'explained_variance': 0.011916342728361404, 'neg_mean_squared_error': 1.4524277905958483}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores', 'fis_', 'coef_']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'get_fis', 'get_coef_', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=8, problem_type='regression', random_state=10,\n",
       "            scope='covars',\n",
       "            scorer={'explained_variance': make_scorer(explained_variance_score),\n",
       "                    'neg_mean_squared_error': make_scorer(mean_squared_error, greater_is_better=False)},\n",
       "            subjects='train', target='b_bmi')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluators['scope=covars']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "informational-honolulu",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b_demo_highest_education_categories=1           0.59780\n",
       "b_demo_highest_education_categories=2           0.62281\n",
       "b_demo_highest_education_categories=4          -0.79331\n",
       "b_demo_highest_education_categories=5          -1.12268\n",
       "b_mri_info_deviceserialnumber='HASH03db707f'    0.17417\n",
       "b_mri_info_deviceserialnumber='HASH11ad4ed5'   -0.60833\n",
       "b_mri_info_deviceserialnumber='HASH1314a204'   -0.70919\n",
       "b_mri_info_deviceserialnumber='HASH311170b9'   -0.14871\n",
       "b_mri_info_deviceserialnumber='HASH3935c89e'   -0.42419\n",
       "b_mri_info_deviceserialnumber='HASH4b0b8b05'    0.02187\n",
       "b_mri_info_deviceserialnumber='HASH5ac2b20b'   -0.00810\n",
       "b_mri_info_deviceserialnumber='HASH5b0cf1bb'    0.02770\n",
       "b_mri_info_deviceserialnumber='HASH5b2fcf80'   -0.32682\n",
       "b_mri_info_deviceserialnumber='HASH65b39280'    0.44976\n",
       "b_mri_info_deviceserialnumber='HASH69f406fa'    0.00030\n",
       "b_mri_info_deviceserialnumber='HASH6b4422a7'    0.09136\n",
       "b_mri_info_deviceserialnumber='HASH7911780b'    0.73788\n",
       "b_mri_info_deviceserialnumber='HASH7f91147d'   -0.05698\n",
       "b_mri_info_deviceserialnumber='HASH96a0c182'   -0.01863\n",
       "b_mri_info_deviceserialnumber='HASHa3e45734'   -0.09858\n",
       "b_mri_info_deviceserialnumber='HASHb640a1b8'    0.56248\n",
       "b_mri_info_deviceserialnumber='HASHc3bf3d9c'    0.10516\n",
       "b_mri_info_deviceserialnumber='HASHd422be27'    0.00579\n",
       "b_mri_info_deviceserialnumber='HASHd7cb4c6d'    0.01132\n",
       "b_mri_info_deviceserialnumber='HASHdb2589d4'    0.00784\n",
       "b_mri_info_deviceserialnumber='HASHe4f6957a'   -0.19978\n",
       "b_mri_info_deviceserialnumber='HASHfeb7e81a'    0.03217\n",
       "b_race_ethnicity_categories=1                  -0.73047\n",
       "b_race_ethnicity_categories=2                   0.87523\n",
       "b_race_ethnicity_categories=3                   0.74217\n",
       "b_race_ethnicity_categories=4                  -0.32284\n",
       "b_sex=1                                        -0.08126\n",
       "b_sex=2                                         0.07723\n",
       "b_agemos                                        0.39502\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note, with get_fis, if mean=True it will return\n",
    "# the mean and only non-null, and non-zero features directly.\n",
    "evaluators['scope=covars'].get_fis(mean=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suburban-corner",
   "metadata": {},
   "source": [
    "Warning: This doesn't mean that this mean features were selected! Remember this is the average over training 5 models, so it means that if a feature shows up it was selected at least once in one of the models!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "sexual-biodiversity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([Options(scope=covars), Options(scope=brain), Options(scope=all), Options(scope=thick), Options(scope=area), Options(scope=subcort), Options(scope=dti_fa), Options(scope=dti_md)])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the different options\n",
    "evaluators.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "residential-signature",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "african-thanksgiving",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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