analysis_and_tips.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis and Tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numerapi import NumerAPI\n",
    "import random\n",
    "import sklearn\n",
    "import lightgbm\n",
    "import matplotlib.pyplot as plt\n",
    "from utils import save_model, load_model, neutralize, get_biggest_change_features, validation_metrics\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn import (\n",
    "    feature_extraction, feature_selection, decomposition, linear_model,\n",
    "    model_selection, metrics, svm\n",
    ")\n",
    "\n",
    "#pandas.options.display.max_rows=1000\n",
    "#pandas.options.display.max_columns=300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-05 21:04:00,215 INFO numerapi.utils: target file already exists\n",
      "2022-04-05 21:04:00,216 INFO numerapi.utils: download complete\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>era</th>\n",
       "      <th>data_type</th>\n",
       "      <th>feature_honoured_observational_balaamite</th>\n",
       "      <th>feature_polaroid_vadose_quinze</th>\n",
       "      <th>feature_untidy_withdrawn_bargeman</th>\n",
       "      <th>feature_genuine_kyphotic_trehala</th>\n",
       "      <th>feature_unenthralled_sportful_schoolhouse</th>\n",
       "      <th>feature_divulsive_explanatory_ideologue</th>\n",
       "      <th>feature_ichthyotic_roofed_yeshiva</th>\n",
       "      <th>feature_waggly_outlandish_carbonisation</th>\n",
       "      <th>...</th>\n",
       "      <th>target_paul_v4_20</th>\n",
       "      <th>target_paul_v4_60</th>\n",
       "      <th>target_george_v4_20</th>\n",
       "      <th>target_george_v4_60</th>\n",
       "      <th>target_william_v4_20</th>\n",
       "      <th>target_william_v4_60</th>\n",
       "      <th>target_arthur_v4_20</th>\n",
       "      <th>target_arthur_v4_60</th>\n",
       "      <th>target_thomas_v4_20</th>\n",
       "      <th>target_thomas_v4_60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n003bba8a98662e4</th>\n",
       "      <td>0001</td>\n",
       "      <td>train</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n003bee128c2fcfc</th>\n",
       "      <td>0001</td>\n",
       "      <td>train</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n0048ac83aff7194</th>\n",
       "      <td>0001</td>\n",
       "      <td>train</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00691bec80d3e02</th>\n",
       "      <td>0001</td>\n",
       "      <td>train</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n00b8720a2fdc4f2</th>\n",
       "      <td>0001</td>\n",
       "      <td>train</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1214 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   era data_type  feature_honoured_observational_balaamite  \\\n",
       "id                                                                           \n",
       "n003bba8a98662e4  0001     train                                       1.0   \n",
       "n003bee128c2fcfc  0001     train                                       0.5   \n",
       "n0048ac83aff7194  0001     train                                       0.5   \n",
       "n00691bec80d3e02  0001     train                                       1.0   \n",
       "n00b8720a2fdc4f2  0001     train                                       1.0   \n",
       "\n",
       "                  feature_polaroid_vadose_quinze  \\\n",
       "id                                                 \n",
       "n003bba8a98662e4                            0.50   \n",
       "n003bee128c2fcfc                            1.00   \n",
       "n0048ac83aff7194                            0.25   \n",
       "n00691bec80d3e02                            0.50   \n",
       "n00b8720a2fdc4f2                            0.75   \n",
       "\n",
       "                  feature_untidy_withdrawn_bargeman  \\\n",
       "id                                                    \n",
       "n003bba8a98662e4                               1.00   \n",
       "n003bee128c2fcfc                               0.25   \n",
       "n0048ac83aff7194                               0.75   \n",
       "n00691bec80d3e02                               0.50   \n",
       "n00b8720a2fdc4f2                               1.00   \n",
       "\n",
       "                  feature_genuine_kyphotic_trehala  \\\n",
       "id                                                   \n",
       "n003bba8a98662e4                              1.00   \n",
       "n003bee128c2fcfc                              0.75   \n",
       "n0048ac83aff7194                              0.00   \n",
       "n00691bec80d3e02                              0.75   \n",
       "n00b8720a2fdc4f2                              1.00   \n",
       "\n",
       "                  feature_unenthralled_sportful_schoolhouse  \\\n",
       "id                                                            \n",
       "n003bba8a98662e4                                       0.00   \n",
       "n003bee128c2fcfc                                       0.00   \n",
       "n0048ac83aff7194                                       0.75   \n",
       "n00691bec80d3e02                                       0.00   \n",
       "n00b8720a2fdc4f2                                       0.00   \n",
       "\n",
       "                  feature_divulsive_explanatory_ideologue  \\\n",
       "id                                                          \n",
       "n003bba8a98662e4                                     0.00   \n",
       "n003bee128c2fcfc                                     0.75   \n",
       "n0048ac83aff7194                                     0.00   \n",
       "n00691bec80d3e02                                     1.00   \n",
       "n00b8720a2fdc4f2                                     0.00   \n",
       "\n",
       "                  feature_ichthyotic_roofed_yeshiva  \\\n",
       "id                                                    \n",
       "n003bba8a98662e4                               1.00   \n",
       "n003bee128c2fcfc                               0.50   \n",
       "n0048ac83aff7194                               0.75   \n",
       "n00691bec80d3e02                               0.25   \n",
       "n00b8720a2fdc4f2                               1.00   \n",
       "\n",
       "                  feature_waggly_outlandish_carbonisation  ...  \\\n",
       "id                                                         ...   \n",
       "n003bba8a98662e4                                     1.00  ...   \n",
       "n003bee128c2fcfc                                     0.75  ...   \n",
       "n0048ac83aff7194                                     0.75  ...   \n",
       "n00691bec80d3e02                                     1.00  ...   \n",
       "n00b8720a2fdc4f2                                     0.50  ...   \n",
       "\n",
       "                  target_paul_v4_20  target_paul_v4_60  target_george_v4_20  \\\n",
       "id                                                                            \n",
       "n003bba8a98662e4               0.50               0.25                 0.25   \n",
       "n003bee128c2fcfc               0.75               1.00                 1.00   \n",
       "n0048ac83aff7194               0.50               0.25                 0.25   \n",
       "n00691bec80d3e02               0.50               0.50                 0.50   \n",
       "n00b8720a2fdc4f2               0.50               0.50                 0.50   \n",
       "\n",
       "                  target_george_v4_60  target_william_v4_20  \\\n",
       "id                                                            \n",
       "n003bba8a98662e4                 0.00              0.333333   \n",
       "n003bee128c2fcfc                 1.00              0.666667   \n",
       "n0048ac83aff7194                 0.25              0.500000   \n",
       "n00691bec80d3e02                 0.50              0.666667   \n",
       "n00b8720a2fdc4f2                 0.50              0.666667   \n",
       "\n",
       "                  target_william_v4_60  target_arthur_v4_20  \\\n",
       "id                                                            \n",
       "n003bba8a98662e4              0.166667             0.500000   \n",
       "n003bee128c2fcfc              0.666667             0.666667   \n",
       "n0048ac83aff7194              0.333333             0.333333   \n",
       "n00691bec80d3e02              0.500000             0.500000   \n",
       "n00b8720a2fdc4f2              0.500000             0.666667   \n",
       "\n",
       "                  target_arthur_v4_60  target_thomas_v4_20  \\\n",
       "id                                                           \n",
       "n003bba8a98662e4             0.500000             0.333333   \n",
       "n003bee128c2fcfc             0.666667             0.666667   \n",
       "n0048ac83aff7194             0.500000             0.500000   \n",
       "n00691bec80d3e02             0.500000             0.666667   \n",
       "n00b8720a2fdc4f2             0.500000             0.666667   \n",
       "\n",
       "                  target_thomas_v4_60  \n",
       "id                                     \n",
       "n003bba8a98662e4             0.166667  \n",
       "n003bee128c2fcfc             0.666667  \n",
       "n0048ac83aff7194             0.333333  \n",
       "n00691bec80d3e02             0.500000  \n",
       "n00b8720a2fdc4f2             0.500000  \n",
       "\n",
       "[5 rows x 1214 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "napi = NumerAPI()\n",
    "\n",
    "current_round = napi.get_current_round()\n",
    "\n",
    "# read in all of the new datas\n",
    "# tournament data and example predictions change every week so we specify the round in their names\n",
    "# training and validation data only change periodically, so no need to download them over again every single week\n",
    "napi.download_dataset(\"v4/train.parquet\", \"train.parquet\")\n",
    "df = pd.read_parquet('train.parquet')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2420521, 1214)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# There are 2420521 rows grouped into 574 weekly eras\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1191\n",
      "['feature_honoured_observational_balaamite', 'feature_polaroid_vadose_quinze', 'feature_untidy_withdrawn_bargeman', 'feature_genuine_kyphotic_trehala', 'feature_unenthralled_sportful_schoolhouse']\n",
      "\r"
     ]
    }
   ],
   "source": [
    "# There's 1191 features with fun names generated by a hashing function\n",
    "features = [c for c in df if c.startswith(\"feature\")]\n",
    "df[\"erano\"] = df.era.astype(int)\n",
    "eras = df.erano\n",
    "target = \"target\"\n",
    "print(len(features))\n",
    "print(features[:5])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# There are 20 targets!\n",
    "\n",
    "## There are 10 different types of targets constructed and 2 versions of each constructed with a 20 day window or a 60 day window\n",
    "## You are only scored on 'target' which currently corresponds to 'target_nomi_20' but could change in the future\n",
    "## The other  auxillary targets can be very useful for training good models. Indeed you may find that a model trained on some of the auxillary targets generalize out-of-sample to 'target' better than a model trained on 'target'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "target = \"target\"\n",
    "targets = [c for c in df if c.startswith(\"target\")]\n",
    "len(targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>target</th>\n",
       "      <th>target_nomi_v4_20</th>\n",
       "      <th>target_nomi_v4_60</th>\n",
       "      <th>target_jerome_v4_20</th>\n",
       "      <th>target_jerome_v4_60</th>\n",
       "      <th>target_janet_v4_20</th>\n",
       "      <th>target_janet_v4_60</th>\n",
       "      <th>target_ben_v4_20</th>\n",
       "      <th>target_ben_v4_60</th>\n",
       "      <th>target_alan_v4_20</th>\n",
       "      <th>...</th>\n",
       "      <th>target_paul_v4_20</th>\n",
       "      <th>target_paul_v4_60</th>\n",
       "      <th>target_george_v4_20</th>\n",
       "      <th>target_george_v4_60</th>\n",
       "      <th>target_william_v4_20</th>\n",
       "      <th>target_william_v4_60</th>\n",
       "      <th>target_arthur_v4_20</th>\n",
       "      <th>target_arthur_v4_60</th>\n",
       "      <th>target_thomas_v4_20</th>\n",
       "      <th>target_thomas_v4_60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500773</td>\n",
       "      <td>0.798061</td>\n",
       "      <td>0.443519</td>\n",
       "      <td>0.690056</td>\n",
       "      <td>0.386239</td>\n",
       "      <td>0.819984</td>\n",
       "      <td>0.455371</td>\n",
       "      <td>0.679708</td>\n",
       "      <td>...</td>\n",
       "      <td>0.782010</td>\n",
       "      <td>0.429850</td>\n",
       "      <td>0.723801</td>\n",
       "      <td>0.401362</td>\n",
       "      <td>0.870858</td>\n",
       "      <td>0.483031</td>\n",
       "      <td>0.765310</td>\n",
       "      <td>0.426294</td>\n",
       "      <td>0.920556</td>\n",
       "      <td>0.498981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_nomi_v4_20</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500773</td>\n",
       "      <td>0.798061</td>\n",
       "      <td>0.443519</td>\n",
       "      <td>0.690056</td>\n",
       "      <td>0.386239</td>\n",
       "      <td>0.819984</td>\n",
       "      <td>0.455371</td>\n",
       "      <td>0.679708</td>\n",
       "      <td>...</td>\n",
       "      <td>0.782010</td>\n",
       "      <td>0.429850</td>\n",
       "      <td>0.723801</td>\n",
       "      <td>0.401362</td>\n",
       "      <td>0.870858</td>\n",
       "      <td>0.483031</td>\n",
       "      <td>0.765310</td>\n",
       "      <td>0.426294</td>\n",
       "      <td>0.920556</td>\n",
       "      <td>0.498981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_nomi_v4_60</th>\n",
       "      <td>0.500773</td>\n",
       "      <td>0.500773</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.445267</td>\n",
       "      <td>0.792348</td>\n",
       "      <td>0.389002</td>\n",
       "      <td>0.682460</td>\n",
       "      <td>0.455332</td>\n",
       "      <td>0.814605</td>\n",
       "      <td>0.383144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.436680</td>\n",
       "      <td>0.767359</td>\n",
       "      <td>0.408407</td>\n",
       "      <td>0.707522</td>\n",
       "      <td>0.485959</td>\n",
       "      <td>0.868252</td>\n",
       "      <td>0.431037</td>\n",
       "      <td>0.759651</td>\n",
       "      <td>0.503030</td>\n",
       "      <td>0.920563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_jerome_v4_20</th>\n",
       "      <td>0.798061</td>\n",
       "      <td>0.798061</td>\n",
       "      <td>0.445267</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.492944</td>\n",
       "      <td>0.683921</td>\n",
       "      <td>0.400233</td>\n",
       "      <td>0.798668</td>\n",
       "      <td>0.439137</td>\n",
       "      <td>0.649539</td>\n",
       "      <td>...</td>\n",
       "      <td>0.910745</td>\n",
       "      <td>0.475421</td>\n",
       "      <td>0.757360</td>\n",
       "      <td>0.409677</td>\n",
       "      <td>0.870162</td>\n",
       "      <td>0.483651</td>\n",
       "      <td>0.741306</td>\n",
       "      <td>0.419568</td>\n",
       "      <td>0.791638</td>\n",
       "      <td>0.442065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_jerome_v4_60</th>\n",
       "      <td>0.443519</td>\n",
       "      <td>0.443519</td>\n",
       "      <td>0.792348</td>\n",
       "      <td>0.492944</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.401501</td>\n",
       "      <td>0.678924</td>\n",
       "      <td>0.437955</td>\n",
       "      <td>0.792413</td>\n",
       "      <td>0.363231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.481372</td>\n",
       "      <td>0.888310</td>\n",
       "      <td>0.420891</td>\n",
       "      <td>0.736698</td>\n",
       "      <td>0.485411</td>\n",
       "      <td>0.864410</td>\n",
       "      <td>0.422447</td>\n",
       "      <td>0.735165</td>\n",
       "      <td>0.444123</td>\n",
       "      <td>0.783889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_janet_v4_20</th>\n",
       "      <td>0.690056</td>\n",
       "      <td>0.690056</td>\n",
       "      <td>0.389002</td>\n",
       "      <td>0.683921</td>\n",
       "      <td>0.401501</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.448913</td>\n",
       "      <td>0.671666</td>\n",
       "      <td>0.375128</td>\n",
       "      <td>0.764112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.690488</td>\n",
       "      <td>0.389216</td>\n",
       "      <td>0.620629</td>\n",
       "      <td>0.341089</td>\n",
       "      <td>0.730067</td>\n",
       "      <td>0.411652</td>\n",
       "      <td>0.806612</td>\n",
       "      <td>0.433650</td>\n",
       "      <td>0.693096</td>\n",
       "      <td>0.389274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_janet_v4_60</th>\n",
       "      <td>0.386239</td>\n",
       "      <td>0.386239</td>\n",
       "      <td>0.682460</td>\n",
       "      <td>0.400233</td>\n",
       "      <td>0.678924</td>\n",
       "      <td>0.448913</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.372148</td>\n",
       "      <td>0.663199</td>\n",
       "      <td>0.409140</td>\n",
       "      <td>...</td>\n",
       "      <td>0.392584</td>\n",
       "      <td>0.682887</td>\n",
       "      <td>0.346254</td>\n",
       "      <td>0.602458</td>\n",
       "      <td>0.411373</td>\n",
       "      <td>0.725530</td>\n",
       "      <td>0.435592</td>\n",
       "      <td>0.801410</td>\n",
       "      <td>0.389649</td>\n",
       "      <td>0.684489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_ben_v4_20</th>\n",
       "      <td>0.819984</td>\n",
       "      <td>0.819984</td>\n",
       "      <td>0.455332</td>\n",
       "      <td>0.798668</td>\n",
       "      <td>0.437955</td>\n",
       "      <td>0.671666</td>\n",
       "      <td>0.372148</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.485997</td>\n",
       "      <td>0.656629</td>\n",
       "      <td>...</td>\n",
       "      <td>0.774470</td>\n",
       "      <td>0.421242</td>\n",
       "      <td>0.758041</td>\n",
       "      <td>0.417847</td>\n",
       "      <td>0.787179</td>\n",
       "      <td>0.440693</td>\n",
       "      <td>0.695605</td>\n",
       "      <td>0.388025</td>\n",
       "      <td>0.830498</td>\n",
       "      <td>0.453324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_ben_v4_60</th>\n",
       "      <td>0.455371</td>\n",
       "      <td>0.455371</td>\n",
       "      <td>0.814605</td>\n",
       "      <td>0.439137</td>\n",
       "      <td>0.792413</td>\n",
       "      <td>0.375128</td>\n",
       "      <td>0.663199</td>\n",
       "      <td>0.485997</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.373571</td>\n",
       "      <td>...</td>\n",
       "      <td>0.428769</td>\n",
       "      <td>0.759599</td>\n",
       "      <td>0.425654</td>\n",
       "      <td>0.746935</td>\n",
       "      <td>0.443047</td>\n",
       "      <td>0.778659</td>\n",
       "      <td>0.392377</td>\n",
       "      <td>0.685300</td>\n",
       "      <td>0.457032</td>\n",
       "      <td>0.821724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_alan_v4_20</th>\n",
       "      <td>0.679708</td>\n",
       "      <td>0.679708</td>\n",
       "      <td>0.383144</td>\n",
       "      <td>0.649539</td>\n",
       "      <td>0.363231</td>\n",
       "      <td>0.764112</td>\n",
       "      <td>0.409140</td>\n",
       "      <td>0.656629</td>\n",
       "      <td>0.373571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.636716</td>\n",
       "      <td>0.352221</td>\n",
       "      <td>0.581884</td>\n",
       "      <td>0.329149</td>\n",
       "      <td>0.682348</td>\n",
       "      <td>0.380664</td>\n",
       "      <td>0.735155</td>\n",
       "      <td>0.403115</td>\n",
       "      <td>0.715509</td>\n",
       "      <td>0.386849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_alan_v4_60</th>\n",
       "      <td>0.383552</td>\n",
       "      <td>0.383552</td>\n",
       "      <td>0.674669</td>\n",
       "      <td>0.365170</td>\n",
       "      <td>0.643125</td>\n",
       "      <td>0.410794</td>\n",
       "      <td>0.758133</td>\n",
       "      <td>0.374093</td>\n",
       "      <td>0.647820</td>\n",
       "      <td>0.418862</td>\n",
       "      <td>...</td>\n",
       "      <td>0.358383</td>\n",
       "      <td>0.624078</td>\n",
       "      <td>0.335640</td>\n",
       "      <td>0.569079</td>\n",
       "      <td>0.383054</td>\n",
       "      <td>0.676481</td>\n",
       "      <td>0.406490</td>\n",
       "      <td>0.726871</td>\n",
       "      <td>0.389971</td>\n",
       "      <td>0.709218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_paul_v4_20</th>\n",
       "      <td>0.782010</td>\n",
       "      <td>0.782010</td>\n",
       "      <td>0.436680</td>\n",
       "      <td>0.910745</td>\n",
       "      <td>0.481372</td>\n",
       "      <td>0.690488</td>\n",
       "      <td>0.392584</td>\n",
       "      <td>0.774470</td>\n",
       "      <td>0.428769</td>\n",
       "      <td>0.636716</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499098</td>\n",
       "      <td>0.764901</td>\n",
       "      <td>0.427141</td>\n",
       "      <td>0.849811</td>\n",
       "      <td>0.473975</td>\n",
       "      <td>0.721710</td>\n",
       "      <td>0.405308</td>\n",
       "      <td>0.774743</td>\n",
       "      <td>0.433230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_paul_v4_60</th>\n",
       "      <td>0.429850</td>\n",
       "      <td>0.429850</td>\n",
       "      <td>0.767359</td>\n",
       "      <td>0.475421</td>\n",
       "      <td>0.888310</td>\n",
       "      <td>0.389216</td>\n",
       "      <td>0.682887</td>\n",
       "      <td>0.421242</td>\n",
       "      <td>0.759599</td>\n",
       "      <td>0.352221</td>\n",
       "      <td>...</td>\n",
       "      <td>0.499098</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.428179</td>\n",
       "      <td>0.758721</td>\n",
       "      <td>0.470078</td>\n",
       "      <td>0.833241</td>\n",
       "      <td>0.401605</td>\n",
       "      <td>0.706316</td>\n",
       "      <td>0.430414</td>\n",
       "      <td>0.757722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_george_v4_20</th>\n",
       "      <td>0.723801</td>\n",
       "      <td>0.723801</td>\n",
       "      <td>0.408407</td>\n",
       "      <td>0.757360</td>\n",
       "      <td>0.420891</td>\n",
       "      <td>0.620629</td>\n",
       "      <td>0.346254</td>\n",
       "      <td>0.758041</td>\n",
       "      <td>0.425654</td>\n",
       "      <td>0.581884</td>\n",
       "      <td>...</td>\n",
       "      <td>0.764901</td>\n",
       "      <td>0.428179</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.482488</td>\n",
       "      <td>0.738800</td>\n",
       "      <td>0.416952</td>\n",
       "      <td>0.641637</td>\n",
       "      <td>0.359278</td>\n",
       "      <td>0.711978</td>\n",
       "      <td>0.402623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_george_v4_60</th>\n",
       "      <td>0.401362</td>\n",
       "      <td>0.401362</td>\n",
       "      <td>0.707522</td>\n",
       "      <td>0.409677</td>\n",
       "      <td>0.736698</td>\n",
       "      <td>0.341089</td>\n",
       "      <td>0.602458</td>\n",
       "      <td>0.417847</td>\n",
       "      <td>0.746935</td>\n",
       "      <td>0.329149</td>\n",
       "      <td>...</td>\n",
       "      <td>0.427141</td>\n",
       "      <td>0.758721</td>\n",
       "      <td>0.482488</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.408795</td>\n",
       "      <td>0.716525</td>\n",
       "      <td>0.353902</td>\n",
       "      <td>0.619670</td>\n",
       "      <td>0.399175</td>\n",
       "      <td>0.692453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_william_v4_20</th>\n",
       "      <td>0.870858</td>\n",
       "      <td>0.870858</td>\n",
       "      <td>0.485959</td>\n",
       "      <td>0.870162</td>\n",
       "      <td>0.485411</td>\n",
       "      <td>0.730067</td>\n",
       "      <td>0.411373</td>\n",
       "      <td>0.787179</td>\n",
       "      <td>0.443047</td>\n",
       "      <td>0.682348</td>\n",
       "      <td>...</td>\n",
       "      <td>0.849811</td>\n",
       "      <td>0.470078</td>\n",
       "      <td>0.738800</td>\n",
       "      <td>0.408795</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.526445</td>\n",
       "      <td>0.807738</td>\n",
       "      <td>0.458092</td>\n",
       "      <td>0.872103</td>\n",
       "      <td>0.487194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_william_v4_60</th>\n",
       "      <td>0.483031</td>\n",
       "      <td>0.483031</td>\n",
       "      <td>0.868252</td>\n",
       "      <td>0.483651</td>\n",
       "      <td>0.864410</td>\n",
       "      <td>0.411652</td>\n",
       "      <td>0.725530</td>\n",
       "      <td>0.440693</td>\n",
       "      <td>0.778659</td>\n",
       "      <td>0.380664</td>\n",
       "      <td>...</td>\n",
       "      <td>0.473975</td>\n",
       "      <td>0.833241</td>\n",
       "      <td>0.416952</td>\n",
       "      <td>0.716525</td>\n",
       "      <td>0.526445</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.460304</td>\n",
       "      <td>0.804516</td>\n",
       "      <td>0.488892</td>\n",
       "      <td>0.868444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_arthur_v4_20</th>\n",
       "      <td>0.765310</td>\n",
       "      <td>0.765310</td>\n",
       "      <td>0.431037</td>\n",
       "      <td>0.741306</td>\n",
       "      <td>0.422447</td>\n",
       "      <td>0.806612</td>\n",
       "      <td>0.435592</td>\n",
       "      <td>0.695605</td>\n",
       "      <td>0.392377</td>\n",
       "      <td>0.735155</td>\n",
       "      <td>...</td>\n",
       "      <td>0.721710</td>\n",
       "      <td>0.401605</td>\n",
       "      <td>0.641637</td>\n",
       "      <td>0.353902</td>\n",
       "      <td>0.807738</td>\n",
       "      <td>0.460304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.530236</td>\n",
       "      <td>0.774044</td>\n",
       "      <td>0.434952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_arthur_v4_60</th>\n",
       "      <td>0.426294</td>\n",
       "      <td>0.426294</td>\n",
       "      <td>0.759651</td>\n",
       "      <td>0.419568</td>\n",
       "      <td>0.735165</td>\n",
       "      <td>0.433650</td>\n",
       "      <td>0.801410</td>\n",
       "      <td>0.388025</td>\n",
       "      <td>0.685300</td>\n",
       "      <td>0.403115</td>\n",
       "      <td>...</td>\n",
       "      <td>0.405308</td>\n",
       "      <td>0.706316</td>\n",
       "      <td>0.359278</td>\n",
       "      <td>0.619670</td>\n",
       "      <td>0.458092</td>\n",
       "      <td>0.804516</td>\n",
       "      <td>0.530236</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.434250</td>\n",
       "      <td>0.767290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_thomas_v4_20</th>\n",
       "      <td>0.920556</td>\n",
       "      <td>0.920556</td>\n",
       "      <td>0.503030</td>\n",
       "      <td>0.791638</td>\n",
       "      <td>0.444123</td>\n",
       "      <td>0.693096</td>\n",
       "      <td>0.389649</td>\n",
       "      <td>0.830498</td>\n",
       "      <td>0.457032</td>\n",
       "      <td>0.715509</td>\n",
       "      <td>...</td>\n",
       "      <td>0.774743</td>\n",
       "      <td>0.430414</td>\n",
       "      <td>0.711978</td>\n",
       "      <td>0.399175</td>\n",
       "      <td>0.872103</td>\n",
       "      <td>0.488892</td>\n",
       "      <td>0.774044</td>\n",
       "      <td>0.434250</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_thomas_v4_60</th>\n",
       "      <td>0.498981</td>\n",
       "      <td>0.498981</td>\n",
       "      <td>0.920563</td>\n",
       "      <td>0.442065</td>\n",
       "      <td>0.783889</td>\n",
       "      <td>0.389274</td>\n",
       "      <td>0.684489</td>\n",
       "      <td>0.453324</td>\n",
       "      <td>0.821724</td>\n",
       "      <td>0.386849</td>\n",
       "      <td>...</td>\n",
       "      <td>0.433230</td>\n",
       "      <td>0.757722</td>\n",
       "      <td>0.402623</td>\n",
       "      <td>0.692453</td>\n",
       "      <td>0.487194</td>\n",
       "      <td>0.868444</td>\n",
       "      <td>0.434952</td>\n",
       "      <td>0.767290</td>\n",
       "      <td>0.511123</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        target  target_nomi_v4_20  target_nomi_v4_60  \\\n",
       "target                1.000000           1.000000           0.500773   \n",
       "target_nomi_v4_20     1.000000           1.000000           0.500773   \n",
       "target_nomi_v4_60     0.500773           0.500773           1.000000   \n",
       "target_jerome_v4_20   0.798061           0.798061           0.445267   \n",
       "target_jerome_v4_60   0.443519           0.443519           0.792348   \n",
       "target_janet_v4_20    0.690056           0.690056           0.389002   \n",
       "target_janet_v4_60    0.386239           0.386239           0.682460   \n",
       "target_ben_v4_20      0.819984           0.819984           0.455332   \n",
       "target_ben_v4_60      0.455371           0.455371           0.814605   \n",
       "target_alan_v4_20     0.679708           0.679708           0.383144   \n",
       "target_alan_v4_60     0.383552           0.383552           0.674669   \n",
       "target_paul_v4_20     0.782010           0.782010           0.436680   \n",
       "target_paul_v4_60     0.429850           0.429850           0.767359   \n",
       "target_george_v4_20   0.723801           0.723801           0.408407   \n",
       "target_george_v4_60   0.401362           0.401362           0.707522   \n",
       "target_william_v4_20  0.870858           0.870858           0.485959   \n",
       "target_william_v4_60  0.483031           0.483031           0.868252   \n",
       "target_arthur_v4_20   0.765310           0.765310           0.431037   \n",
       "target_arthur_v4_60   0.426294           0.426294           0.759651   \n",
       "target_thomas_v4_20   0.920556           0.920556           0.503030   \n",
       "target_thomas_v4_60   0.498981           0.498981           0.920563   \n",
       "\n",
       "                      target_jerome_v4_20  target_jerome_v4_60  \\\n",
       "target                           0.798061             0.443519   \n",
       "target_nomi_v4_20                0.798061             0.443519   \n",
       "target_nomi_v4_60                0.445267             0.792348   \n",
       "target_jerome_v4_20              1.000000             0.492944   \n",
       "target_jerome_v4_60              0.492944             1.000000   \n",
       "target_janet_v4_20               0.683921             0.401501   \n",
       "target_janet_v4_60               0.400233             0.678924   \n",
       "target_ben_v4_20                 0.798668             0.437955   \n",
       "target_ben_v4_60                 0.439137             0.792413   \n",
       "target_alan_v4_20                0.649539             0.363231   \n",
       "target_alan_v4_60                0.365170             0.643125   \n",
       "target_paul_v4_20                0.910745             0.481372   \n",
       "target_paul_v4_60                0.475421             0.888310   \n",
       "target_george_v4_20              0.757360             0.420891   \n",
       "target_george_v4_60              0.409677             0.736698   \n",
       "target_william_v4_20             0.870162             0.485411   \n",
       "target_william_v4_60             0.483651             0.864410   \n",
       "target_arthur_v4_20              0.741306             0.422447   \n",
       "target_arthur_v4_60              0.419568             0.735165   \n",
       "target_thomas_v4_20              0.791638             0.444123   \n",
       "target_thomas_v4_60              0.442065             0.783889   \n",
       "\n",
       "                      target_janet_v4_20  target_janet_v4_60  \\\n",
       "target                          0.690056            0.386239   \n",
       "target_nomi_v4_20               0.690056            0.386239   \n",
       "target_nomi_v4_60               0.389002            0.682460   \n",
       "target_jerome_v4_20             0.683921            0.400233   \n",
       "target_jerome_v4_60             0.401501            0.678924   \n",
       "target_janet_v4_20              1.000000            0.448913   \n",
       "target_janet_v4_60              0.448913            1.000000   \n",
       "target_ben_v4_20                0.671666            0.372148   \n",
       "target_ben_v4_60                0.375128            0.663199   \n",
       "target_alan_v4_20               0.764112            0.409140   \n",
       "target_alan_v4_60               0.410794            0.758133   \n",
       "target_paul_v4_20               0.690488            0.392584   \n",
       "target_paul_v4_60               0.389216            0.682887   \n",
       "target_george_v4_20             0.620629            0.346254   \n",
       "target_george_v4_60             0.341089            0.602458   \n",
       "target_william_v4_20            0.730067            0.411373   \n",
       "target_william_v4_60            0.411652            0.725530   \n",
       "target_arthur_v4_20             0.806612            0.435592   \n",
       "target_arthur_v4_60             0.433650            0.801410   \n",
       "target_thomas_v4_20             0.693096            0.389649   \n",
       "target_thomas_v4_60             0.389274            0.684489   \n",
       "\n",
       "                      target_ben_v4_20  target_ben_v4_60  target_alan_v4_20  \\\n",
       "target                        0.819984          0.455371           0.679708   \n",
       "target_nomi_v4_20             0.819984          0.455371           0.679708   \n",
       "target_nomi_v4_60             0.455332          0.814605           0.383144   \n",
       "target_jerome_v4_20           0.798668          0.439137           0.649539   \n",
       "target_jerome_v4_60           0.437955          0.792413           0.363231   \n",
       "target_janet_v4_20            0.671666          0.375128           0.764112   \n",
       "target_janet_v4_60            0.372148          0.663199           0.409140   \n",
       "target_ben_v4_20              1.000000          0.485997           0.656629   \n",
       "target_ben_v4_60              0.485997          1.000000           0.373571   \n",
       "target_alan_v4_20             0.656629          0.373571           1.000000   \n",
       "target_alan_v4_60             0.374093          0.647820           0.418862   \n",
       "target_paul_v4_20             0.774470          0.428769           0.636716   \n",
       "target_paul_v4_60             0.421242          0.759599           0.352221   \n",
       "target_george_v4_20           0.758041          0.425654           0.581884   \n",
       "target_george_v4_60           0.417847          0.746935           0.329149   \n",
       "target_william_v4_20          0.787179          0.443047           0.682348   \n",
       "target_william_v4_60          0.440693          0.778659           0.380664   \n",
       "target_arthur_v4_20           0.695605          0.392377           0.735155   \n",
       "target_arthur_v4_60           0.388025          0.685300           0.403115   \n",
       "target_thomas_v4_20           0.830498          0.457032           0.715509   \n",
       "target_thomas_v4_60           0.453324          0.821724           0.386849   \n",
       "\n",
       "                      ...  target_paul_v4_20  target_paul_v4_60  \\\n",
       "target                ...           0.782010           0.429850   \n",
       "target_nomi_v4_20     ...           0.782010           0.429850   \n",
       "target_nomi_v4_60     ...           0.436680           0.767359   \n",
       "target_jerome_v4_20   ...           0.910745           0.475421   \n",
       "target_jerome_v4_60   ...           0.481372           0.888310   \n",
       "target_janet_v4_20    ...           0.690488           0.389216   \n",
       "target_janet_v4_60    ...           0.392584           0.682887   \n",
       "target_ben_v4_20      ...           0.774470           0.421242   \n",
       "target_ben_v4_60      ...           0.428769           0.759599   \n",
       "target_alan_v4_20     ...           0.636716           0.352221   \n",
       "target_alan_v4_60     ...           0.358383           0.624078   \n",
       "target_paul_v4_20     ...           1.000000           0.499098   \n",
       "target_paul_v4_60     ...           0.499098           1.000000   \n",
       "target_george_v4_20   ...           0.764901           0.428179   \n",
       "target_george_v4_60   ...           0.427141           0.758721   \n",
       "target_william_v4_20  ...           0.849811           0.470078   \n",
       "target_william_v4_60  ...           0.473975           0.833241   \n",
       "target_arthur_v4_20   ...           0.721710           0.401605   \n",
       "target_arthur_v4_60   ...           0.405308           0.706316   \n",
       "target_thomas_v4_20   ...           0.774743           0.430414   \n",
       "target_thomas_v4_60   ...           0.433230           0.757722   \n",
       "\n",
       "                      target_george_v4_20  target_george_v4_60  \\\n",
       "target                           0.723801             0.401362   \n",
       "target_nomi_v4_20                0.723801             0.401362   \n",
       "target_nomi_v4_60                0.408407             0.707522   \n",
       "target_jerome_v4_20              0.757360             0.409677   \n",
       "target_jerome_v4_60              0.420891             0.736698   \n",
       "target_janet_v4_20               0.620629             0.341089   \n",
       "target_janet_v4_60               0.346254             0.602458   \n",
       "target_ben_v4_20                 0.758041             0.417847   \n",
       "target_ben_v4_60                 0.425654             0.746935   \n",
       "target_alan_v4_20                0.581884             0.329149   \n",
       "target_alan_v4_60                0.335640             0.569079   \n",
       "target_paul_v4_20                0.764901             0.427141   \n",
       "target_paul_v4_60                0.428179             0.758721   \n",
       "target_george_v4_20              1.000000             0.482488   \n",
       "target_george_v4_60              0.482488             1.000000   \n",
       "target_william_v4_20             0.738800             0.408795   \n",
       "target_william_v4_60             0.416952             0.716525   \n",
       "target_arthur_v4_20              0.641637             0.353902   \n",
       "target_arthur_v4_60              0.359278             0.619670   \n",
       "target_thomas_v4_20              0.711978             0.399175   \n",
       "target_thomas_v4_60              0.402623             0.692453   \n",
       "\n",
       "                      target_william_v4_20  target_william_v4_60  \\\n",
       "target                            0.870858              0.483031   \n",
       "target_nomi_v4_20                 0.870858              0.483031   \n",
       "target_nomi_v4_60                 0.485959              0.868252   \n",
       "target_jerome_v4_20               0.870162              0.483651   \n",
       "target_jerome_v4_60               0.485411              0.864410   \n",
       "target_janet_v4_20                0.730067              0.411652   \n",
       "target_janet_v4_60                0.411373              0.725530   \n",
       "target_ben_v4_20                  0.787179              0.440693   \n",
       "target_ben_v4_60                  0.443047              0.778659   \n",
       "target_alan_v4_20                 0.682348              0.380664   \n",
       "target_alan_v4_60                 0.383054              0.676481   \n",
       "target_paul_v4_20                 0.849811              0.473975   \n",
       "target_paul_v4_60                 0.470078              0.833241   \n",
       "target_george_v4_20               0.738800              0.416952   \n",
       "target_george_v4_60               0.408795              0.716525   \n",
       "target_william_v4_20              1.000000              0.526445   \n",
       "target_william_v4_60              0.526445              1.000000   \n",
       "target_arthur_v4_20               0.807738              0.460304   \n",
       "target_arthur_v4_60               0.458092              0.804516   \n",
       "target_thomas_v4_20               0.872103              0.488892   \n",
       "target_thomas_v4_60               0.487194              0.868444   \n",
       "\n",
       "                      target_arthur_v4_20  target_arthur_v4_60  \\\n",
       "target                           0.765310             0.426294   \n",
       "target_nomi_v4_20                0.765310             0.426294   \n",
       "target_nomi_v4_60                0.431037             0.759651   \n",
       "target_jerome_v4_20              0.741306             0.419568   \n",
       "target_jerome_v4_60              0.422447             0.735165   \n",
       "target_janet_v4_20               0.806612             0.433650   \n",
       "target_janet_v4_60               0.435592             0.801410   \n",
       "target_ben_v4_20                 0.695605             0.388025   \n",
       "target_ben_v4_60                 0.392377             0.685300   \n",
       "target_alan_v4_20                0.735155             0.403115   \n",
       "target_alan_v4_60                0.406490             0.726871   \n",
       "target_paul_v4_20                0.721710             0.405308   \n",
       "target_paul_v4_60                0.401605             0.706316   \n",
       "target_george_v4_20              0.641637             0.359278   \n",
       "target_george_v4_60              0.353902             0.619670   \n",
       "target_william_v4_20             0.807738             0.458092   \n",
       "target_william_v4_60             0.460304             0.804516   \n",
       "target_arthur_v4_20              1.000000             0.530236   \n",
       "target_arthur_v4_60              0.530236             1.000000   \n",
       "target_thomas_v4_20              0.774044             0.434250   \n",
       "target_thomas_v4_60              0.434952             0.767290   \n",
       "\n",
       "                      target_thomas_v4_20  target_thomas_v4_60  \n",
       "target                           0.920556             0.498981  \n",
       "target_nomi_v4_20                0.920556             0.498981  \n",
       "target_nomi_v4_60                0.503030             0.920563  \n",
       "target_jerome_v4_20              0.791638             0.442065  \n",
       "target_jerome_v4_60              0.444123             0.783889  \n",
       "target_janet_v4_20               0.693096             0.389274  \n",
       "target_janet_v4_60               0.389649             0.684489  \n",
       "target_ben_v4_20                 0.830498             0.453324  \n",
       "target_ben_v4_60                 0.457032             0.821724  \n",
       "target_alan_v4_20                0.715509             0.386849  \n",
       "target_alan_v4_60                0.389971             0.709218  \n",
       "target_paul_v4_20                0.774743             0.433230  \n",
       "target_paul_v4_60                0.430414             0.757722  \n",
       "target_george_v4_20              0.711978             0.402623  \n",
       "target_george_v4_60              0.399175             0.692453  \n",
       "target_william_v4_20             0.872103             0.487194  \n",
       "target_william_v4_60             0.488892             0.868444  \n",
       "target_arthur_v4_20              0.774044             0.434952  \n",
       "target_arthur_v4_60              0.434250             0.767290  \n",
       "target_thomas_v4_20              1.000000             0.511123  \n",
       "target_thomas_v4_60              0.511123             1.000000  \n",
       "\n",
       "[21 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# The targets have a wide range of correlations with each other from ~0.3 to ~0.9\n",
    "# This should allow the construction of many diverse models which ensemble nicely\n",
    "df[targets].corr()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1191 features, no feature groups!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x17407d37790>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# The features are no longer explicit feature groups, but you can see some pretty obvious patterns if you\n",
    "# visualize the feature correlation matrix. Feel free to construct your own groupings!\n",
    "plt.figure(figsize = (8,8))\n",
    "plt.imshow(df[df.era=='0001'][features].corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# The models should be scored based on the rank-correlation (spearman) with the target\n",
    "def numerai_score(y_true, y_pred):\n",
    "    rank_pred = y_pred.groupby(eras).apply(lambda x: x.rank(pct=True, method=\"first\"))\n",
    "    return np.corrcoef(y_true, rank_pred)[0,1]\n",
    "\n",
    "# It can also be convenient while working to evaluate based on the regular (pearson) correlation\n",
    "def correlation_score(y_true, y_pred):\n",
    "    return np.corrcoef(y_true, y_pred)[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.420521e+06\n",
       "mean     3.042725e+02\n",
       "std      1.598538e+02\n",
       "min      1.000000e+00\n",
       "25%      1.720000e+02\n",
       "50%      3.040000e+02\n",
       "75%      4.430000e+02\n",
       "max      5.740000e+02\n",
       "Name: erano, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# There are 574 eras numbered from 1 to 574\n",
    "eras.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='erano'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# The earlier eras are smaller, but generally each era is 4000-5000 rows\n",
    "df.groupby(eras).size().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0.00    0.050007\n",
       "0.25    0.200001\n",
       "0.50    0.499975\n",
       "0.75    0.200014\n",
       "1.00    0.050003\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# The target is discrete and takes on 5 different values with about 5% in 0 & 1, 20% in 0.75 & 0.25, and 50% in 0.50\n",
    "df.groupby(target).size()/len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target_william_v4_20\n",
      "0.000000    0.023418\n",
      "0.166667    0.061311\n",
      "0.333333    0.227122\n",
      "0.500000    0.388675\n",
      "0.666667    0.202978\n",
      "0.833333    0.061387\n",
      "1.000000    0.030743\n",
      "dtype: float64\n",
      "\n",
      "target_thomas_v4_20\n",
      "0.000000    0.016413\n",
      "0.166667    0.048281\n",
      "0.333333    0.191793\n",
      "0.500000    0.494680\n",
      "0.666667    0.174882\n",
      "0.833333    0.048829\n",
      "1.000000    0.021508\n",
      "dtype: float64\n",
      "\r"
     ]
    }
   ],
   "source": [
    "# Some new targets have a different distribution and the bins are less rigid in the percent of samples contained\n",
    "print(df.groupby('target_william_v4_20').size()/len(df))\n",
    "print()\n",
    "print(df.groupby('target_thomas_v4_20').size()/len(df))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some of the features are very correlated\n",
    "Some even perfectly correlated within a particular era"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "feature_corrs = df[df.era=='0001'][features].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "feature_honoured_observational_balaamite  feature_honoured_observational_balaamite     1.000000\n",
       "                                          feature_polaroid_vadose_quinze               0.002170\n",
       "                                          feature_untidy_withdrawn_bargeman            0.141514\n",
       "                                          feature_genuine_kyphotic_trehala             0.171890\n",
       "                                          feature_unenthralled_sportful_schoolhouse   -0.149952\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "feature_corrs.stack().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "feature_optic_mycelial_whimper                      feature_productile_auriform_fil            -0.950579\n",
       "feature_careworn_motivational_requisite             feature_loftier_sightly_lyric              -0.940453\n",
       "feature_dissident_templed_shippon                   feature_rose_buttoned_dandy                -0.939489\n",
       "feature_applausive_forgettable_mishanter            feature_marled_coliform_proctorship        -0.936837\n",
       "feature_sodding_choosy_eruption                     feature_utmost_excitable_emir              -0.934667\n",
       "                                                                                                  ...   \n",
       "feature_bimanual_godly_witloof                      feature_healthier_unconnected_clave         1.000000\n",
       "feature_associate_unproper_gridder                  feature_asteriated_invigorated_penitence    1.000000\n",
       "feature_agricultural_uranic_ankerite                feature_pseudo_bounden_hereafter            1.000000\n",
       "feature_associate_unproper_gridder                  feature_maglemosian_kittle_coachbuilding    1.000000\n",
       "feature_australasian_dissatisfactory_intermarriage  feature_unsensing_enterprising_janissary    1.000000\n",
       "Length: 708645, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "tdf = feature_corrs.stack()\n",
    "tdf = tdf[tdf.index.get_level_values(0) < tdf.index.get_level_values(1)]\n",
    "tdf.sort_values()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The correlation can change over time\n",
    "You can see this by comparing feature correlations on the first half and second half on the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "df1 = df[eras<=eras.median()]\n",
    "df2 = df[eras>eras.median()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "corr1 = df1[df1.era=='0001'][features].corr().unstack()\n",
    "corr1 = corr1[corr1.index.get_level_values(0) < corr1.index.get_level_values(1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "corr2 = df2[df2.era=='0574'][features].corr().unstack()\n",
    "corr2 = corr2[corr2.index.get_level_values(0) < corr2.index.get_level_values(1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>corr1</th>\n",
       "      <th>corr2</th>\n",
       "      <th>corr_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>feature_cislunar_ritardando_gadwall</th>\n",
       "      <th>feature_uranian_gorier_melancholy</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.165348</td>\n",
       "      <td>-1.165348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_baggier_supernormal_calcedony</th>\n",
       "      <th>feature_uranian_gorier_melancholy</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.164931</td>\n",
       "      <td>-1.164931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_cislunar_ritardando_gadwall</th>\n",
       "      <th>feature_unscaling_pedantic_fingernail</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.162745</td>\n",
       "      <td>-1.162745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">feature_rutilant_obliterate_potassium</th>\n",
       "      <th>feature_torose_unspiritualised_kylie</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.161912</td>\n",
       "      <td>-1.161912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_uranian_gorier_melancholy</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.161704</td>\n",
       "      <td>-1.161704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_anucleate_knotted_nonage</th>\n",
       "      <th>feature_asteriated_invigorated_penitence</th>\n",
       "      <td>-0.097878</td>\n",
       "      <td>0.736776</td>\n",
       "      <td>0.834655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_flamier_confusing_dithering</th>\n",
       "      <th>feature_nonagenarian_roundish_publication</th>\n",
       "      <td>-0.078833</td>\n",
       "      <td>0.757289</td>\n",
       "      <td>0.836122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_comprisable_commensurable_cyrenaic</th>\n",
       "      <th>feature_nonagenarian_roundish_publication</th>\n",
       "      <td>-0.070878</td>\n",
       "      <td>0.766556</td>\n",
       "      <td>0.837433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_cragged_sacred_malabo</th>\n",
       "      <th>feature_disarrayed_rarefactive_trisulphide</th>\n",
       "      <td>-0.137175</td>\n",
       "      <td>0.720117</td>\n",
       "      <td>0.857291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_maglemosian_kittle_coachbuilding</th>\n",
       "      <th>feature_unreeling_homeothermic_macedonia</th>\n",
       "      <td>-0.088717</td>\n",
       "      <td>0.783528</td>\n",
       "      <td>0.872245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>708645 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                          corr1  \\\n",
       "feature_cislunar_ritardando_gadwall        feature_uranian_gorier_melancholy           1.000000   \n",
       "feature_baggier_supernormal_calcedony      feature_uranian_gorier_melancholy           1.000000   \n",
       "feature_cislunar_ritardando_gadwall        feature_unscaling_pedantic_fingernail       1.000000   \n",
       "feature_rutilant_obliterate_potassium      feature_torose_unspiritualised_kylie        1.000000   \n",
       "                                           feature_uranian_gorier_melancholy           1.000000   \n",
       "...                                                                                         ...   \n",
       "feature_anucleate_knotted_nonage           feature_asteriated_invigorated_penitence   -0.097878   \n",
       "feature_flamier_confusing_dithering        feature_nonagenarian_roundish_publication  -0.078833   \n",
       "feature_comprisable_commensurable_cyrenaic feature_nonagenarian_roundish_publication  -0.070878   \n",
       "feature_cragged_sacred_malabo              feature_disarrayed_rarefactive_trisulphide -0.137175   \n",
       "feature_maglemosian_kittle_coachbuilding   feature_unreeling_homeothermic_macedonia   -0.088717   \n",
       "\n",
       "                                                                                          corr2  \\\n",
       "feature_cislunar_ritardando_gadwall        feature_uranian_gorier_melancholy          -0.165348   \n",
       "feature_baggier_supernormal_calcedony      feature_uranian_gorier_melancholy          -0.164931   \n",
       "feature_cislunar_ritardando_gadwall        feature_unscaling_pedantic_fingernail      -0.162745   \n",
       "feature_rutilant_obliterate_potassium      feature_torose_unspiritualised_kylie       -0.161912   \n",
       "                                           feature_uranian_gorier_melancholy          -0.161704   \n",
       "...                                                                                         ...   \n",
       "feature_anucleate_knotted_nonage           feature_asteriated_invigorated_penitence    0.736776   \n",
       "feature_flamier_confusing_dithering        feature_nonagenarian_roundish_publication   0.757289   \n",
       "feature_comprisable_commensurable_cyrenaic feature_nonagenarian_roundish_publication   0.766556   \n",
       "feature_cragged_sacred_malabo              feature_disarrayed_rarefactive_trisulphide  0.720117   \n",
       "feature_maglemosian_kittle_coachbuilding   feature_unreeling_homeothermic_macedonia    0.783528   \n",
       "\n",
       "                                                                                       corr_diff  \n",
       "feature_cislunar_ritardando_gadwall        feature_uranian_gorier_melancholy           -1.165348  \n",
       "feature_baggier_supernormal_calcedony      feature_uranian_gorier_melancholy           -1.164931  \n",
       "feature_cislunar_ritardando_gadwall        feature_unscaling_pedantic_fingernail       -1.162745  \n",
       "feature_rutilant_obliterate_potassium      feature_torose_unspiritualised_kylie        -1.161912  \n",
       "                                           feature_uranian_gorier_melancholy           -1.161704  \n",
       "...                                                                                          ...  \n",
       "feature_anucleate_knotted_nonage           feature_asteriated_invigorated_penitence     0.834655  \n",
       "feature_flamier_confusing_dithering        feature_nonagenarian_roundish_publication    0.836122  \n",
       "feature_comprisable_commensurable_cyrenaic feature_nonagenarian_roundish_publication    0.837433  \n",
       "feature_cragged_sacred_malabo              feature_disarrayed_rarefactive_trisulphide   0.857291  \n",
       "feature_maglemosian_kittle_coachbuilding   feature_unreeling_homeothermic_macedonia     0.872245  \n",
       "\n",
       "[708645 rows x 3 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "tdf = pd.DataFrame({\n",
    "    \"corr1\": corr1,\n",
    "    \"corr2\": corr2,\n",
    "})\n",
    "tdf[\"corr_diff\"] = tdf.corr2 - tdf.corr1\n",
    "tdf.sort_values(by=\"corr_diff\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some features are predictive on their own"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "feature_scores = {feature: score for feature, score in zip(features, np.corrcoef(df[df.era=='0001'][[target]+features].T)[1:,0])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "feature_steric_coxcombic_relinquishment          -0.081221\n",
       "feature_unsustaining_chewier_adnoun              -0.081221\n",
       "feature_drizzling_refrigerative_imperfection     -0.072832\n",
       "feature_censorial_leachier_rickshaw              -0.071307\n",
       "feature_quodlibetic_enrapt_miscalculation        -0.069781\n",
       "                                                    ...   \n",
       "feature_unperched_forgetive_vasoconstrictor       0.056435\n",
       "feature_cockiest_ditheistical_pennyworth          0.056817\n",
       "feature_tuberculate_patelliform_paging            0.057198\n",
       "feature_uninquiring_unspiritualized_gradualism    0.059867\n",
       "feature_unsizable_ancestral_collocutor            0.061011\n",
       "Length: 1191, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "pd.Series(feature_scores).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# Single features do not work consistently though\n",
    "by_era_correlation = pd.Series({\n",
    "    era: np.corrcoef(tdf[target], tdf[\"feature_untidy_withdrawn_bargeman\"])[0,1]\n",
    "    for era, tdf in df.groupby(eras)\n",
    "})\n",
    "by_era_correlation.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# With a rolling 10 era average you can see some trends\n",
    "by_era_correlation.rolling(10).mean().plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The feature exposures of different targets can be very different, even orthogonal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "feature_scores_nomi20 = {feature: score for feature, score in zip(features, np.corrcoef(df[df.era=='0001'][['target_nomi_v4_20']+features].T)[1:,0])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "feature_scores_nomi60 = {feature: score for feature, score in zip(features, np.corrcoef(df[df.era=='0001'][['target_nomi_v4_60']+features].fillna(0.5).T)[1:,0])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.680901621349402\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "plt.scatter(feature_scores_nomi20.values(), feature_scores_nomi60.values())\n",
    "print(np.corrcoef(list(feature_scores_nomi20.values()), list(feature_scores_nomi60.values()))[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "feature_scores_jerome = {feature: score for feature, score in zip(features, np.corrcoef(df[df.era=='0001'][['target_jerome_v4_60']+features].fillna(0.5).T)[1:,0])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "feature_scores_ben = {feature: score for feature, score in zip(features, np.corrcoef(df[df.era=='0001'][['target_ben_v4_20']+features].fillna(0.5).T)[1:,0])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.33993486663275885\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "plt.scatter(feature_scores_jerome.values(), feature_scores_ben.values())\n",
    "print(np.corrcoef(list(feature_scores_jerome.values()), list(feature_scores_ben.values()))[0,1])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gotcha: MSE looks worse than correlation out of sample\n",
    "Models will generally be overconfident, so even if they are good at ranking rows, the Mean-Squared-Error of the residuals could be larger than even the Mean-Squared-Error of the target (r-squared<0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "df1 = df[eras<=eras.median()]\n",
    "df2 = df[eras>eras.median()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "linear1 = linear_model.LinearRegression()\n",
    "linear1.fit(df1[features], df1[target])\n",
    "linear2 = linear_model.LinearRegression()\n",
    "linear2.fit(df2[features], df2[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eval_on_1</th>\n",
       "      <th>eval_on_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_on_1</th>\n",
       "      <td>0.007814</td>\n",
       "      <td>-0.001092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_on_2</th>\n",
       "      <td>-0.000677</td>\n",
       "      <td>0.007060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            eval_on_1  eval_on_2\n",
       "train_on_1   0.007814  -0.001092\n",
       "train_on_2  -0.000677   0.007060"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# Note in particular that the R-squared of (train_on_1, eval_on_2) is slightly negative!\n",
    "r2 = [\n",
    "    [\n",
    "        model.score(dfX[features], dfX[target])\n",
    "        for dfX in [df1, df2]\n",
    "    ]\n",
    "    for model in [linear1, linear2]\n",
    "]\n",
    "pd.DataFrame(r2, columns=[\"eval_on_1\", \"eval_on_2\"], index=[\"train_on_1\", \"train_on_2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eval_on_1</th>\n",
       "      <th>eval_on_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_on_1</th>\n",
       "      <td>0.086000</td>\n",
       "      <td>0.036300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_on_2</th>\n",
       "      <td>0.039793</td>\n",
       "      <td>0.080654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            eval_on_1  eval_on_2\n",
       "train_on_1   0.086000   0.036300\n",
       "train_on_2   0.039793   0.080654"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# Note in particular that the correlation of (train_on_1, eval_on_2) is quite decent\n",
    "corrs = [\n",
    "    [\n",
    "        numerai_score(dfX[target], pd.Series(model.predict(dfX[features]), index=dfX.index))\n",
    "        for dfX in [df1, df2]\n",
    "    ]\n",
    "    for model in [linear1, linear2]\n",
    "]\n",
    "pd.DataFrame(corrs, columns=[\"eval_on_1\", \"eval_on_2\"], index=[\"train_on_1\", \"train_on_2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# This can be be run with LightGBM as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "lgb1 = lightgbm.LGBMRegressor()\n",
    "lgb1.fit(df1[features], df1[target])\n",
    "lgb2 = lightgbm.LGBMRegressor()\n",
    "lgb2.fit(df2[features], df2[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eval_on_1</th>\n",
       "      <th>eval_on_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_on_1</th>\n",
       "      <td>0.025325</td>\n",
       "      <td>0.002091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_on_2</th>\n",
       "      <td>0.002315</td>\n",
       "      <td>0.025903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            eval_on_1  eval_on_2\n",
       "train_on_1   0.025325   0.002091\n",
       "train_on_2   0.002315   0.025903"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "r2 = [\n",
    "    [\n",
    "        model.score(dfX[features], dfX[target])\n",
    "        for dfX in [df1, df2]\n",
    "    ]\n",
    "    for model in [lgb1, lgb2]\n",
    "]\n",
    "pd.DataFrame(r2, columns=[\"eval_on_1\", \"eval_on_2\"], index=[\"train_on_1\", \"train_on_2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eval_on_1</th>\n",
       "      <th>eval_on_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>train_on_1</th>\n",
       "      <td>0.194260</td>\n",
       "      <td>0.047544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_on_2</th>\n",
       "      <td>0.049658</td>\n",
       "      <td>0.197819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            eval_on_1  eval_on_2\n",
       "train_on_1   0.194260   0.047544\n",
       "train_on_2   0.049658   0.197819"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "corrs = [\n",
    "    [\n",
    "        numerai_score(dfX[target], pd.Series(model.predict(dfX[features]), index=dfX.index))\n",
    "        for dfX in [df1, df2]\n",
    "    ]\n",
    "    for model in [lgb1, lgb2]\n",
    "]\n",
    "pd.DataFrame(corrs, columns=[\"eval_on_1\", \"eval_on_2\"], index=[\"train_on_1\", \"train_on_2\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gotcha: Eras are now overlapping!\n",
    "\n",
    "Eras are weekly (5 days), but the target is four weeks out (20 days). You can subsample the eras (every 4th era) to prevent models from overfitting due to overlapping eras. You can then ensemble the models trained on each of the 4 different ways to subsample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# train model on all eras in df1\n",
    "lgb_all = lightgbm.LGBMRegressor()\n",
    "lgb_all.fit(df1[features], df1[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['feature_honoured_observational_balaamite',\n",
       " 'feature_polaroid_vadose_quinze',\n",
       " 'feature_untidy_withdrawn_bargeman',\n",
       " 'feature_genuine_kyphotic_trehala',\n",
       " 'feature_unenthralled_sportful_schoolhouse',\n",
       " 'feature_divulsive_explanatory_ideologue',\n",
       " 'feature_ichthyotic_roofed_yeshiva',\n",
       " 'feature_waggly_outlandish_carbonisation',\n",
       " 'feature_floriated_amish_sprite',\n",
       " 'feature_iconoclastic_parietal_agonist',\n",
       " 'feature_demolished_unfrightened_superpower',\n",
       " 'feature_styloid_subdermal_cytotoxin',\n",
       " 'feature_ironfisted_nonvintage_chlorpromazine',\n",
       " 'feature_torose_unspiritualised_kylie',\n",
       " 'feature_tearing_unkingly_adulthood',\n",
       " 'feature_stylolitic_brown_spume',\n",
       " 'feature_ferial_incumbent_engraving',\n",
       " 'feature_litigant_unsizable_rhebok',\n",
       " 'feature_floatiest_quintuplicate_carpentering',\n",
       " 'feature_tuberculate_patelliform_paging',\n",
       " 'feature_cuddlesome_undernamed_incidental',\n",
       " 'feature_loony_zirconic_hoofer',\n",
       " 'feature_indign_tardier_borough',\n",
       " 'feature_fair_papal_vinaigrette',\n",
       " 'feature_attack_unlit_milling',\n",
       " 'feature_froggier_unlearned_underworkman',\n",
       " 'feature_peninsular_pulsatile_vapor',\n",
       " 'feature_midmost_perspiratory_hubert',\n",
       " 'feature_laminable_unspecified_gynoecium',\n",
       " 'feature_bally_bathymetrical_isadora',\n",
       " 'feature_skim_unmeant_bandsman',\n",
       " 'feature_ungenuine_sporophytic_evangelist',\n",
       " 'feature_supercelestial_telic_dyfed',\n",
       " 'feature_inconsiderate_unbooted_ricer',\n",
       " 'feature_inured_conservable_forcer',\n",
       " 'feature_glibber_deficient_jakarta',\n",
       " 'feature_morbific_irredentist_interregnum',\n",
       " 'feature_conjoint_transverse_superstructure',\n",
       " 'feature_tingling_large_primordiality',\n",
       " 'feature_phyllopod_unconstrainable_blubberer',\n",
       " 'feature_deformable_unitary_schistosity',\n",
       " 'feature_unprovisioned_aquatic_deuterogamy',\n",
       " 'feature_equipped_undoubted_athanasian',\n",
       " 'feature_inflammable_numb_anticline',\n",
       " 'feature_kinky_benzal_holotype',\n",
       " 'feature_ruptured_designing_interpolator',\n",
       " 'feature_hierologic_expectable_maiolica',\n",
       " 'feature_boiling_won_rama',\n",
       " 'feature_lovelorn_aided_limiter',\n",
       " 'feature_soviet_zibeline_profiler',\n",
       " 'feature_altimetrical_muddled_symbolism',\n",
       " 'feature_bratty_disrespectable_bookstand',\n",
       " 'feature_unshaken_ahorse_wehrmacht',\n",
       " 'feature_mightier_chivalric_kana',\n",
       " 'feature_gambrel_unblessed_gigantomachy',\n",
       " 'feature_ethiopic_anhedonic_stob',\n",
       " 'feature_overstrung_dysmenorrheal_ingolstadt',\n",
       " 'feature_rose_buttoned_dandy',\n",
       " 'feature_recipient_perched_dendrochronologist',\n",
       " 'feature_spikier_ordinate_taira',\n",
       " 'feature_mercian_luddite_aganippe',\n",
       " 'feature_faint_consociate_rhytidectomy',\n",
       " 'feature_unpressed_mahratta_dah',\n",
       " 'feature_maxillary_orphic_despicability',\n",
       " 'feature_clasping_fast_menstruation',\n",
       " 'feature_obeliscal_bewildered_reviewer',\n",
       " 'feature_babist_moribund_myna',\n",
       " 'feature_underdressed_tanagrine_prying',\n",
       " 'feature_corniest_undue_scall',\n",
       " 'feature_reduplicative_appalling_metastable',\n",
       " 'feature_wrathful_prolix_colotomy',\n",
       " 'feature_limonitic_issuable_melancholy',\n",
       " 'feature_approximal_telautographic_sharkskin',\n",
       " 'feature_fribble_gusseted_stickjaw',\n",
       " 'feature_spec_subversive_plotter',\n",
       " 'feature_unsinkable_dumbstruck_octuplet',\n",
       " 'feature_integrative_reviviscent_governed',\n",
       " 'feature_tamil_grungy_empathy',\n",
       " 'feature_canopic_exigible_schoolgirl',\n",
       " 'feature_plumular_constantinian_repositing',\n",
       " 'feature_serpentiform_trinary_imponderability',\n",
       " 'feature_gyroidal_embowed_pilcher',\n",
       " 'feature_unlivable_armenian_wedge',\n",
       " 'feature_flawed_demonological_toady',\n",
       " 'feature_pruinose_raploch_roubaix',\n",
       " 'feature_seediest_ramshackle_reclamation',\n",
       " 'feature_hagiological_refer_vitamin',\n",
       " 'feature_alcibiadean_lumpier_origan',\n",
       " 'feature_encased_unamiable_hasidism',\n",
       " 'feature_evocable_woollen_guarder',\n",
       " 'feature_hunchbacked_unturning_meditation',\n",
       " 'feature_circumnavigable_naughty_retranslation',\n",
       " 'feature_testicular_slashed_ventosity',\n",
       " 'feature_potential_subsessile_disconnection',\n",
       " 'feature_unswaddled_inenarrable_goody',\n",
       " 'feature_stellular_paler_centralisation',\n",
       " 'feature_angevin_fitful_sultan',\n",
       " 'feature_subinfeudatory_brainy_carmel',\n",
       " 'feature_simpatico_cadential_pup',\n",
       " 'feature_esculent_erotic_epoxy',\n",
       " 'feature_milliary_hyperpyretic_medea',\n",
       " 'feature_coraciiform_sciurine_reef',\n",
       " 'feature_weightiest_protozoic_brawler',\n",
       " 'feature_cooled_perkiest_electrodeposition',\n",
       " 'feature_differing_peptizing_womaniser',\n",
       " 'feature_gleaming_monosyllabic_scrod',\n",
       " 'feature_unyielding_dismal_divertissement',\n",
       " 'feature_rankine_meaty_port',\n",
       " 'feature_southernmost_unhuman_arbiter',\n",
       " 'feature_singhalese_cerographical_ego',\n",
       " 'feature_malignant_campodeid_pluton',\n",
       " 'feature_dure_jaspery_mugging',\n",
       " 'feature_educational_caustic_mythologisation',\n",
       " 'feature_diverted_astral_dunghill',\n",
       " 'feature_degenerate_diaphragmatic_literalizer',\n",
       " 'feature_laced_scraggly_grimalkin',\n",
       " 'feature_wheezier_unjaundiced_game',\n",
       " 'feature_unimpressed_uninflected_theophylline',\n",
       " 'feature_shiite_overfed_mense',\n",
       " 'feature_irritant_reciprocal_pelage',\n",
       " 'feature_bricky_runed_bottleful',\n",
       " 'feature_phyletic_separate_genuflexion',\n",
       " 'feature_peckish_impetrative_kanpur',\n",
       " 'feature_unshrinking_semiarid_floccule',\n",
       " 'feature_heartier_salverform_nephew',\n",
       " 'feature_geostrophic_adaptative_karla',\n",
       " 'feature_navigational_enured_condensability',\n",
       " 'feature_confusable_pursy_plosion',\n",
       " 'feature_clenched_wayward_coelostat',\n",
       " 'feature_developed_arbitrary_traditionalist',\n",
       " 'feature_unnameable_abysmal_net',\n",
       " 'feature_completive_pedantical_sinecurist',\n",
       " 'feature_witchy_orange_muley',\n",
       " 'feature_misfeatured_sometime_tunneler',\n",
       " 'feature_agaze_lancinate_zohar',\n",
       " 'feature_subservient_wedged_limping',\n",
       " 'feature_urticant_ultracentrifugal_wane',\n",
       " 'feature_pulverized_unified_dupery',\n",
       " 'feature_stoichiometric_unanswerable_leveller',\n",
       " 'feature_cyanophyte_emasculated_turpin',\n",
       " 'feature_unruly_salian_impetuosity',\n",
       " 'feature_ataractic_swept_rubeola',\n",
       " 'feature_pansophical_agitato_theatricality',\n",
       " 'feature_recreational_homiletic_nubian',\n",
       " 'feature_burning_phrygian_axinomancy',\n",
       " 'feature_protractive_moral_forswearing',\n",
       " 'feature_certificated_putrescent_godship',\n",
       " 'feature_dietetic_unscholarly_methamphetamine',\n",
       " 'feature_vegetable_manlier_macaco',\n",
       " 'feature_anthropoid_pithy_newscast',\n",
       " 'feature_verifying_imagism_sublease',\n",
       " 'feature_deckled_exaggerative_algol',\n",
       " 'feature_songful_intercostal_frightener',\n",
       " 'feature_additive_untrustworthy_hierologist',\n",
       " 'feature_translative_quantitative_eschewer',\n",
       " 'feature_coseismic_surpassable_invariance',\n",
       " 'feature_blubbery_octahedral_bushfire',\n",
       " 'feature_continued_conjugated_natalia',\n",
       " 'feature_dissident_templed_shippon',\n",
       " 'feature_wally_unrotted_eccrinology',\n",
       " 'feature_unforgivable_airtight_reinsurance',\n",
       " 'feature_unrelenting_intravascular_mesenchyme',\n",
       " 'feature_linear_scummiest_insobriety',\n",
       " 'feature_ovine_bramblier_leaven',\n",
       " 'feature_uninforming_predictable_pepino',\n",
       " 'feature_pluviometrical_biannual_saiga',\n",
       " 'feature_affettuoso_taxidermic_greg',\n",
       " 'feature_lateral_confervoid_belgravia',\n",
       " 'feature_coalier_hircine_brokerage',\n",
       " 'feature_undiverted_analyzed_accidie',\n",
       " 'feature_favourable_swankiest_tympanist',\n",
       " 'feature_refractory_topped_dependance',\n",
       " 'feature_bustled_fieriest_doukhobor',\n",
       " 'feature_isobilateral_olden_nephron',\n",
       " 'feature_circassian_leathern_impugner',\n",
       " 'feature_signed_ringent_sunna',\n",
       " 'feature_cornute_potentiometric_tinhorn',\n",
       " 'feature_veristic_parklike_halcyon',\n",
       " 'feature_geochemical_unsavoury_collection',\n",
       " 'feature_guerrilla_arrested_flavine',\n",
       " 'feature_undependable_stedfast_donegal',\n",
       " 'feature_bijou_penetrant_syringa',\n",
       " 'feature_lamarckian_tarnal_egestion',\n",
       " 'feature_horticultural_footworn_superscription',\n",
       " 'feature_unwithered_personate_dilatation',\n",
       " 'feature_wrought_muckier_temporality',\n",
       " 'feature_rival_undepraved_countermarch',\n",
       " 'feature_irrevocable_unlawful_oral',\n",
       " 'feature_flawy_caller_superior',\n",
       " 'feature_elohistic_totalitarian_underline',\n",
       " 'feature_unrecognisable_waxier_paging',\n",
       " 'feature_paraffinoid_flashiest_brotherhood',\n",
       " 'feature_depauperate_armipotent_decentralisation',\n",
       " 'feature_palpebral_univalve_pennoncel',\n",
       " 'feature_received_veiniest_tamarix',\n",
       " 'feature_scissile_dejected_kainite',\n",
       " 'feature_narcotized_collectivist_evzone',\n",
       " 'feature_jamesian_scutiform_ionium',\n",
       " 'feature_gambogian_feudalist_diocletian',\n",
       " 'feature_moneyed_mesophytic_lester',\n",
       " 'feature_purblind_autarkic_pyrenoid',\n",
       " 'feature_paleolithic_myalgic_lech',\n",
       " 'feature_fortyish_neptunian_catechumenate',\n",
       " 'feature_tricksiest_pending_voile',\n",
       " 'feature_forcipate_laced_greenlet',\n",
       " 'feature_overjoyed_undriven_sauna',\n",
       " 'feature_small_cumulative_graywacke',\n",
       " 'feature_incertain_catchable_zibet',\n",
       " 'feature_unsustaining_chewier_adnoun',\n",
       " 'feature_ruthenic_peremptory_truth',\n",
       " 'feature_drier_worshipping_hetairist',\n",
       " 'feature_incredible_plane_sacque',\n",
       " 'feature_inducible_home_immovability',\n",
       " 'feature_feral_telling_marquessate',\n",
       " 'feature_agitato_unlineal_perspicacity',\n",
       " 'feature_turanian_satiable_millicent',\n",
       " 'feature_girlish_uncoated_shammy',\n",
       " 'feature_runniest_unstaying_toom',\n",
       " 'feature_smashed_gynaecoid_septa',\n",
       " 'feature_humiliating_numerate_goldminer',\n",
       " 'feature_steadier_untrenched_bernstein',\n",
       " 'feature_battled_premillennial_omelette',\n",
       " 'feature_hefty_hesitant_mantissa',\n",
       " 'feature_unscaling_pedantic_fingernail',\n",
       " 'feature_peachiest_felted_removal',\n",
       " 'feature_bloomed_touchable_alastair',\n",
       " 'feature_indefinite_plenipotent_absorbent',\n",
       " 'feature_subfusc_furriest_nervule',\n",
       " 'feature_yeasty_castilian_nicaragua',\n",
       " 'feature_antacid_thermogenic_wilhelm',\n",
       " 'feature_peak_interpretive_lahti',\n",
       " 'feature_lithotomical_periodontal_systemization',\n",
       " 'feature_martinique_tingliest_gynaecocracy',\n",
       " 'feature_nymphomaniac_disappointing_greenaway',\n",
       " 'feature_discombobulated_fourierism_histopathologist',\n",
       " 'feature_tallish_grimier_tumbrel',\n",
       " 'feature_partitive_labyrinthine_sard',\n",
       " 'feature_granulitic_cordial_infield',\n",
       " 'feature_piebald_unresisted_soldo',\n",
       " 'feature_inhospitable_necked_duckbill',\n",
       " 'feature_stolid_unhacked_schoolgirl',\n",
       " 'feature_blushful_protean_granduncle',\n",
       " 'feature_scombroid_genoese_kilojoule',\n",
       " 'feature_untired_flighty_tungstate',\n",
       " 'feature_adjustable_ruffled_lumberjacket',\n",
       " 'feature_toadyish_illiterate_famishment',\n",
       " 'feature_inaccessible_radioactive_glassine',\n",
       " 'feature_augean_contraceptive_subsequence',\n",
       " 'feature_unbearded_rustiest_fiddlewood',\n",
       " 'feature_contrasty_ablaze_canaster',\n",
       " 'feature_uncharmed_rallying_ostracon',\n",
       " 'feature_spicate_photolithographic_buckskin',\n",
       " 'feature_disliked_undersized_vermiculation',\n",
       " 'feature_gerundival_puristic_gobioid',\n",
       " 'feature_frogged_slightest_patmore',\n",
       " 'feature_fascial_biserrate_pout',\n",
       " 'feature_coercible_fecal_steradian',\n",
       " 'feature_inadequate_unisex_internationalisation',\n",
       " 'feature_darkened_campanulate_decerebrate',\n",
       " 'feature_costliest_heavenly_bovver',\n",
       " 'feature_topologic_subcaliber_radiotelephony',\n",
       " 'feature_nephritic_grammatical_lithograph',\n",
       " 'feature_diluted_maxillary_denouement',\n",
       " 'feature_lenient_electrothermal_phoenix',\n",
       " 'feature_flauntier_forethoughtful_festival',\n",
       " 'feature_phasmid_accommodating_aftercare',\n",
       " 'feature_kingly_gemmological_electrodynamometer',\n",
       " 'feature_applausive_forgettable_mishanter',\n",
       " 'feature_unconfessed_paltry_finn',\n",
       " 'feature_botchier_universalistic_nullifier',\n",
       " 'feature_excursive_slaggy_confutation',\n",
       " 'feature_tearing_inharmonic_employee',\n",
       " 'feature_exhilarative_agleam_hebron',\n",
       " 'feature_grasping_unmetrical_trollopian',\n",
       " 'feature_interruptive_breached_protanomaly',\n",
       " 'feature_emarginate_enigmatical_yiddish',\n",
       " 'feature_reviving_mystifying_antwerp',\n",
       " 'feature_planular_naturistic_pinhead',\n",
       " 'feature_insipid_unshakable_panne',\n",
       " 'feature_abreast_viscoelastic_commander',\n",
       " 'feature_uneffaced_unintermitted_spurt',\n",
       " 'feature_tenth_contrasting_slice',\n",
       " 'feature_geomorphological_uncompanioned_colander',\n",
       " 'feature_deflective_demographic_saragossa',\n",
       " 'feature_teratogenic_wet_calkin',\n",
       " 'feature_graceful_vaunted_accommodator',\n",
       " 'feature_perpetuable_stuffed_manxman',\n",
       " 'feature_belittled_scenographic_antidisestablishmentarianism',\n",
       " 'feature_plausive_skaldic_shoebox',\n",
       " 'feature_incognita_cleaned_asphyxiant',\n",
       " 'feature_biaxal_forworn_sixty',\n",
       " 'feature_dressy_skiable_hypognathism',\n",
       " 'feature_maltese_flaggiest_platelayer',\n",
       " 'feature_gory_medullated_waverer',\n",
       " 'feature_precursory_maltese_wetting',\n",
       " 'feature_overexcitable_filmier_queening',\n",
       " 'feature_analgesic_ardent_conservatoire',\n",
       " 'feature_realisable_defeatist_japer',\n",
       " 'feature_adventive_isohyetal_newshawk',\n",
       " 'feature_interatomic_doggish_teutonisation',\n",
       " 'feature_peeled_singing_smacking',\n",
       " 'feature_sophomoric_perseverant_sclaff',\n",
       " 'feature_regrettable_liberating_crabber',\n",
       " 'feature_polyzoarial_histologic_shallop',\n",
       " 'feature_revitalizing_intersectional_dysthymia',\n",
       " 'feature_sanctioned_sunny_lily',\n",
       " 'feature_chariest_unimplored_towboat',\n",
       " 'feature_unchaste_raisable_tetraploidy',\n",
       " 'feature_disapproving_behind_dampener',\n",
       " 'feature_instinctive_dappled_upholder',\n",
       " 'feature_unforeboding_succeeding_wildcatter',\n",
       " 'feature_cockiest_ditheistical_pennyworth',\n",
       " 'feature_baggier_supernormal_calcedony',\n",
       " 'feature_unmethodized_straining_heartburning',\n",
       " 'feature_holometabolic_speeding_subinfeudatory',\n",
       " 'feature_maigre_twinkling_overstand',\n",
       " 'feature_handled_crescent_ciselure',\n",
       " 'feature_touring_urnfield_sequestrum',\n",
       " 'feature_sunproof_unmurmuring_heliotype',\n",
       " 'feature_apprehensible_assuring_schappe',\n",
       " 'feature_ministrative_slurred_parenchyma',\n",
       " 'feature_squabbiest_receivable_foreyard',\n",
       " 'feature_daytime_arching_expropriator',\n",
       " 'feature_underdeveloped_eastern_goner',\n",
       " 'feature_noetic_thermometric_pique',\n",
       " 'feature_glial_birefringent_popery',\n",
       " 'feature_rural_apogean_titbit',\n",
       " 'feature_aaronic_unexampled_arguer',\n",
       " 'feature_chorial_sapheaded_humberside',\n",
       " 'feature_juvenalian_typical_langton',\n",
       " 'feature_heterotactic_masculine_liquidity',\n",
       " 'feature_piping_unabolished_statocyst',\n",
       " 'feature_scroddled_groaning_sanctuary',\n",
       " 'feature_overlarge_societal_alternator',\n",
       " 'feature_unquestioning_dappled_phenomenalism',\n",
       " 'feature_pacifist_unmeaning_haslet',\n",
       " 'feature_cantharidian_lightful_cubicle',\n",
       " 'feature_supportive_sublime_phenolic',\n",
       " 'feature_derogate_bigoted_mnemonic',\n",
       " 'feature_balinese_uncomforted_mimicker',\n",
       " 'feature_revolutionist_proportionate_headscarf',\n",
       " 'feature_anticivic_blistery_knot',\n",
       " 'feature_anamnestic_conformable_spaceship',\n",
       " 'feature_leprose_corroborant_incapacitation',\n",
       " 'feature_trifling_sleety_amylase',\n",
       " 'feature_tutelary_mangier_cryoscopy',\n",
       " 'feature_depressant_blinded_yellowstone',\n",
       " 'feature_untinned_dextrogyrate_fining',\n",
       " 'feature_saturated_protozoal_unsociability',\n",
       " 'feature_nutant_legatine_fairfax',\n",
       " 'feature_suety_mystagogical_islamization',\n",
       " 'feature_defeasible_bustiest_trimetrogon',\n",
       " 'feature_unsatisfactory_lovelorn_brainstorm',\n",
       " 'feature_terminative_extroverted_interpretation',\n",
       " 'feature_antitussive_vixenly_sacque',\n",
       " 'feature_poetical_individualist_laterite',\n",
       " 'feature_convergent_reborn_autoroute',\n",
       " \"feature_seminarial_catachrestic_i've\",\n",
       " 'feature_tangled_dropsical_uprooter',\n",
       " 'feature_colourable_lapsable_foliage',\n",
       " 'feature_heterotopic_choreographic_argentina',\n",
       " 'feature_choric_illustrated_inch',\n",
       " 'feature_epithelial_apodictic_constructivism',\n",
       " 'feature_resorbent_unmurmuring_humoring',\n",
       " 'feature_uneducated_afraid_sip',\n",
       " 'feature_spellable_quintic_idiotism',\n",
       " 'feature_spiroid_semblable_glimpse',\n",
       " 'feature_metagrobolized_incorrigible_berkelium',\n",
       " 'feature_marled_coliform_proctorship',\n",
       " 'feature_jammed_stearic_gaper',\n",
       " 'feature_biosynthetic_wambly_cullender',\n",
       " 'feature_digressive_ratty_supernatant',\n",
       " 'feature_incredible_glimmering_hoydenism',\n",
       " 'feature_scrappier_seen_phalanx',\n",
       " 'feature_defective_sectional_stenotype',\n",
       " 'feature_unworked_tribadic_catalyst',\n",
       " 'feature_woodier_slimmest_supplanter',\n",
       " 'feature_conducive_underlying_snood',\n",
       " 'feature_prostate_kernelly_chromogen',\n",
       " 'feature_muggiest_explicit_barnardo',\n",
       " 'feature_banal_microanalytical_posset',\n",
       " 'feature_constrained_disused_perishable',\n",
       " 'feature_apt_trimeter_bucketful',\n",
       " 'feature_retardative_telencephalic_heme',\n",
       " 'feature_quintuplicate_hortative_merionethshire',\n",
       " 'feature_tetraploid_vorticose_mil',\n",
       " 'feature_likeliest_exhaled_middlemarch',\n",
       " 'feature_rhinological_downier_gamb',\n",
       " 'feature_anencephalic_unattempted_pschent',\n",
       " 'feature_bronchitic_miscible_inwall',\n",
       " 'feature_sophistic_translucid_abutment',\n",
       " 'feature_fortissimo_undrawn_ratio',\n",
       " 'feature_appellant_verbalized_duckbill',\n",
       " 'feature_sinister_footworn_tilling',\n",
       " 'feature_expectative_zonate_stockbroker',\n",
       " 'feature_entrepreneurial_glumpiest_longhorn',\n",
       " 'feature_recommendatory_prissy_flutter',\n",
       " 'feature_blathering_unwell_despiser',\n",
       " 'feature_manufactured_nodal_seeking',\n",
       " 'feature_intromittent_surer_pederasty',\n",
       " 'feature_uninquiring_unspiritualized_gradualism',\n",
       " 'feature_undealt_tonal_fictionalization',\n",
       " 'feature_undrawn_oldish_deprivation',\n",
       " 'feature_brainish_nonabsorbent_assurance',\n",
       " 'feature_pacific_sleeved_devise',\n",
       " 'feature_multipolar_syncopated_ambrotype',\n",
       " 'feature_asphyxiated_peaceful_effleurage',\n",
       " 'feature_twistable_comely_licensee',\n",
       " 'feature_overweening_incommunicable_pealing',\n",
       " 'feature_phagocytic_humanistic_chappal',\n",
       " 'feature_cyanotic_unblissful_aeration',\n",
       " 'feature_unconstitutional_chiseled_coalport',\n",
       " 'feature_embolic_menial_mariculture',\n",
       " 'feature_pectoral_couthie_epiphenomenon',\n",
       " 'feature_literal_memoriter_heavy',\n",
       " 'feature_sunset_slouchy_alisma',\n",
       " 'feature_subglobose_sleekier_calcaneum',\n",
       " 'feature_addressable_intransitive_reconnoitrer',\n",
       " 'feature_coastal_edible_whang',\n",
       " 'feature_flamier_confusing_dithering',\n",
       " 'feature_waxing_jaggy_bondswoman',\n",
       " 'feature_hidden_blue_bibber',\n",
       " 'feature_marginal_irredeemable_neat',\n",
       " 'feature_baptist_undelayed_mannerism',\n",
       " 'feature_eruciform_scorbutic_overkill',\n",
       " 'feature_lentissimo_zymolytic_earwig',\n",
       " 'feature_unreprimanded_evocable_briard',\n",
       " 'feature_unqualifying_pursuant_antihistamine',\n",
       " 'feature_crisscrossed_audible_hafiz',\n",
       " 'feature_ugrian_schizocarpic_skulk',\n",
       " 'feature_associable_additional_bough',\n",
       " 'feature_doggone_seeable_mask',\n",
       " 'feature_interconnected_correlatable_exogamy',\n",
       " 'feature_uranian_gorier_melancholy',\n",
       " 'feature_petitory_norwegian_tsaritza',\n",
       " 'feature_wounded_crook_gargoylism',\n",
       " 'feature_crapulent_appropriate_kanga',\n",
       " 'feature_smuggest_galvanic_memorial',\n",
       " 'feature_indefeasible_abject_faucet',\n",
       " 'feature_cased_polycrystalline_groschen',\n",
       " 'feature_adolescent_anguilliform_staging',\n",
       " 'feature_escheatable_miscreative_provence',\n",
       " 'feature_coloratura_preclusive_micromillimetre',\n",
       " 'feature_agape_untamable_towmond',\n",
       " 'feature_underemployed_opiate_aube',\n",
       " 'feature_trad_unreduced_banian',\n",
       " 'feature_fogyish_cruciate_starter',\n",
       " 'feature_winglike_hydropathic_sedimentology',\n",
       " 'feature_conglomerate_amphipod_sewellel',\n",
       " 'feature_unpainted_censual_pinacoid',\n",
       " 'feature_isoseismic_rhinocerotic_narceine',\n",
       " 'feature_virtuosic_afflicted_sumatra',\n",
       " 'feature_fatalistic_cramoisy_locative',\n",
       " 'feature_chorionic_coated_undergraduette',\n",
       " 'feature_tinkly_driftiest_maurya',\n",
       " 'feature_pulsing_ionian_flatterer',\n",
       " 'feature_downiest_spenserian_evadne',\n",
       " 'feature_logistical_ahistorical_congregation',\n",
       " 'feature_sensationalistic_puritan_mirador',\n",
       " 'feature_steely_delusory_anesthesiologist',\n",
       " 'feature_dissymmetric_stressed_stonewalling',\n",
       " 'feature_lathery_uncarpeted_prolactin',\n",
       " 'feature_coronate_congeneric_stockhausen',\n",
       " 'feature_fremd_cricoid_gibberellin',\n",
       " 'feature_aleatory_phallic_swingtree',\n",
       " 'feature_tragical_rainbowy_seafarer',\n",
       " 'feature_chaliced_evolutional_street',\n",
       " 'feature_legged_spatiotemporal_basalt',\n",
       " 'feature_obligate_quadruplication_feathering',\n",
       " 'feature_sane_disqualifying_whimperer',\n",
       " 'feature_grassier_sizy_chorale',\n",
       " 'feature_interpenetrative_boustrophedon_proudhon',\n",
       " 'feature_quaternate_loftier_directorate',\n",
       " 'feature_pulmonic_bladed_affray',\n",
       " 'feature_stunning_unladen_ockham',\n",
       " 'feature_superhuman_coenobitical_scotticism',\n",
       " 'feature_undisguised_photoelectric_floorboard',\n",
       " 'feature_sodding_choosy_eruption',\n",
       " 'feature_perverted_unapproving_sawyer',\n",
       " 'feature_etched_furry_biriani',\n",
       " 'feature_financed_striped_libertarian',\n",
       " 'feature_pudendal_unterrifying_hagdon',\n",
       " 'feature_standardized_rosiny_suslik',\n",
       " 'feature_rifled_mum_ova',\n",
       " 'feature_vibratory_prejudicial_quadrillion',\n",
       " 'feature_prototypical_daylong_glop',\n",
       " 'feature_enervated_tearier_septation',\n",
       " 'feature_leafiest_unrepaired_indemnification',\n",
       " 'feature_ecumenical_neuron_equity',\n",
       " 'feature_castrated_inculpatory_pea',\n",
       " 'feature_unstarched_isogonic_alternation',\n",
       " 'feature_unthawed_moved_theft',\n",
       " 'feature_depleted_omnidirectional_baluchi',\n",
       " 'feature_unsocial_pouring_barbe',\n",
       " 'feature_like_inflorescent_sierra',\n",
       " 'feature_mauve_supernumerary_hogan',\n",
       " 'feature_harmonized_intrinsic_disapproval',\n",
       " 'feature_unplumb_prefectorial_gloucester',\n",
       " 'feature_towery_eighteenth_enough',\n",
       " 'feature_kinematical_absorbable_specialisation',\n",
       " 'feature_lamplit_unengaged_mommy',\n",
       " 'feature_unmoaned_attritional_crenation',\n",
       " 'feature_deltoid_chalkier_connecticut',\n",
       " 'feature_disincentive_unchallenged_aerobe',\n",
       " 'feature_regurgitate_demolition_downstate',\n",
       " 'feature_antenuptial_moonlit_incidence',\n",
       " 'feature_disclosed_transcendental_pungency',\n",
       " 'feature_certified_sensorial_occiput',\n",
       " 'feature_froward_flattering_cretic',\n",
       " 'feature_packaged_topological_paradigm',\n",
       " 'feature_interfemoral_battered_ghetto',\n",
       " 'feature_intrusive_sniffy_gangland',\n",
       " 'feature_pedicular_fanfold_beseecher',\n",
       " 'feature_effortful_inenarrable_johnsonianism',\n",
       " 'feature_tardigrade_intercommunal_propitiatory',\n",
       " 'feature_elasmobranch_braving_typhoid',\n",
       " 'feature_aweary_fruiting_climb',\n",
       " 'feature_unwomanly_pitying_saffian',\n",
       " 'feature_aeriform_discomposed_moolvie',\n",
       " 'feature_laniary_chelated_rejoicing',\n",
       " 'feature_tricksome_unimproved_accidental',\n",
       " 'feature_unperched_forgetive_vasoconstrictor',\n",
       " 'feature_cislunar_ritardando_gadwall',\n",
       " 'feature_autumn_prefectural_viscera',\n",
       " 'feature_quaint_lyophobic_draper',\n",
       " 'feature_exploding_delectable_aril',\n",
       " 'feature_hemihedral_fumed_marquisette',\n",
       " 'feature_nephritic_unrelievable_piperonal',\n",
       " 'feature_mesarch_discreditable_calming',\n",
       " 'feature_disillusive_saltant_placidity',\n",
       " 'feature_knowing_humid_justifiability',\n",
       " 'feature_croaking_japhetic_fifer',\n",
       " 'feature_prothallium_burst_pledget',\n",
       " 'feature_unassimilable_chunky_flattie',\n",
       " 'feature_inhibiting_supernatural_runyon',\n",
       " 'feature_resentful_eisteddfodic_assyrian',\n",
       " 'feature_studded_premonitory_periostracum',\n",
       " 'feature_dendritic_phytographic_skydiving',\n",
       " 'feature_transpositional_undisciplinable_ancestor',\n",
       " 'feature_redeemed_petiolar_lethe',\n",
       " 'feature_enveloping_diabolic_serviette',\n",
       " 'feature_excaudate_laboured_loquitur',\n",
       " 'feature_unsympathetic_classic_abac',\n",
       " 'feature_concise_tangy_bentham',\n",
       " 'feature_freezing_unrenewed_gillray',\n",
       " 'feature_intranational_sepulchral_clacton',\n",
       " 'feature_mesarch_disentangled_catalytic',\n",
       " 'feature_cauline_herpetic_videocassette',\n",
       " 'feature_gravest_insincere_bronwen',\n",
       " 'feature_plutocratic_cressy_plasterboard',\n",
       " 'feature_massive_demisable_spouse',\n",
       " 'feature_ugly_admissible_firm',\n",
       " 'feature_reproachable_foliaged_lysozyme',\n",
       " 'feature_sunburnt_sympodial_cookhouse',\n",
       " 'feature_squirarchal_bioplasmic_delay',\n",
       " 'feature_differential_intercrossed_royalism',\n",
       " 'feature_revelational_natty_nephralgia',\n",
       " 'feature_wendish_synchronal_advertency',\n",
       " 'feature_subversive_guardable_kago',\n",
       " 'feature_vulcanian_brimstony_autobiographer',\n",
       " 'feature_unsoaped_waspier_much',\n",
       " 'feature_vermillion_platyrrhine_demulsification',\n",
       " 'feature_unventilated_sollar_bason',\n",
       " 'feature_godliest_consistorian_woodpecker',\n",
       " 'feature_interchangeable_yellow_thinker',\n",
       " 'feature_unknighted_unarticulated_stalag',\n",
       " 'feature_odd_desiderative_godet',\n",
       " 'feature_undermanned_transfusible_executive',\n",
       " 'feature_jiggish_mechanized_patricide',\n",
       " 'feature_tetrabasic_enervated_hemiparasite',\n",
       " 'feature_chiseled_dastard_analyst',\n",
       " 'feature_natant_wriest_dollop',\n",
       " 'feature_speediest_nautical_edge',\n",
       " 'feature_fertilised_lakier_offside',\n",
       " 'feature_parsonish_rutty_citronella',\n",
       " 'feature_cyclone_disappointing_trollopean',\n",
       " 'feature_nymphomaniac_cleaned_museologist',\n",
       " 'feature_immovable_liquified_potpie',\n",
       " 'feature_utmost_excitable_emir',\n",
       " 'feature_gathered_owlish_judgment',\n",
       " 'feature_dichotomic_tenpenny_myotonia',\n",
       " 'feature_reverable_sunk_quiet',\n",
       " 'feature_sweatier_orbital_arrhenotoky',\n",
       " 'feature_hydrokinetic_idyllic_archetype',\n",
       " 'feature_unapprehensive_thickety_etherification',\n",
       " 'feature_unweary_avionic_claudine',\n",
       " 'feature_imitable_unnatural_samuel',\n",
       " 'feature_wuthering_stinky_bimetallist',\n",
       " 'feature_mantuan_cricoid_responder',\n",
       " 'feature_undesirable_candied_computist',\n",
       " 'feature_tethered_exceptive_altimeter',\n",
       " 'feature_mediative_sufferable_serosity',\n",
       " 'feature_pinnatiped_unelected_irreverence',\n",
       " 'feature_coated_transitory_oersted',\n",
       " 'feature_epicontinental_centum_raine',\n",
       " 'feature_desensitizing_distributive_bidder',\n",
       " 'feature_syncytial_exterior_remora',\n",
       " 'feature_sullied_vulval_disappointing',\n",
       " 'feature_fungible_allotted_deterioration',\n",
       " 'feature_saut_shalwar_culpability',\n",
       " 'feature_dental_stormier_chape',\n",
       " 'feature_irresponsible_unearthly_neat',\n",
       " 'feature_alive_romansh_stinging',\n",
       " 'feature_thermophile_noisette_swamper',\n",
       " 'feature_totipalmate_rightable_occultist',\n",
       " 'feature_acanthoid_slimiest_decor',\n",
       " 'feature_submontane_schmaltzy_piggyback',\n",
       " 'feature_wannish_record_lunette',\n",
       " 'feature_satisfied_aymaran_enterotomy',\n",
       " 'feature_smuggled_scarabaeoid_fastball',\n",
       " 'feature_tiptoe_decadent_statue',\n",
       " 'feature_sematic_helminthoid_tricentenary',\n",
       " 'feature_untamed_contemplative_deism',\n",
       " 'feature_disprovable_topmost_burrower',\n",
       " 'feature_weedier_indistinctive_haranguer',\n",
       " 'feature_undrilled_wheezier_countermand',\n",
       " 'feature_indentured_insuperable_spider',\n",
       " 'feature_municipal_curvier_hegelianism',\n",
       " 'feature_pitchiest_dresden_barnard',\n",
       " 'feature_korean_bassy_strewing',\n",
       " 'feature_homelike_telltale_silvan',\n",
       " 'feature_faddiest_clement_repatriation',\n",
       " 'feature_quaggy_chilliest_inchoative',\n",
       " 'feature_explanatory_immature_cautery',\n",
       " 'feature_versicular_buoyant_codetta',\n",
       " 'feature_expended_evitable_darwinian',\n",
       " 'feature_corporatist_seborrheic_hopi',\n",
       " 'feature_undisguised_unenviable_stamen',\n",
       " 'feature_trisomic_hagiographic_fragrance',\n",
       " 'feature_fearsome_merry_bluewing',\n",
       " 'feature_blind_concordant_tribalist',\n",
       " 'feature_strigose_rugose_interjector',\n",
       " 'feature_binding_lanky_rushing',\n",
       " 'feature_carolean_tearable_smoothie',\n",
       " 'feature_nappiest_unportioned_readjustment',\n",
       " 'feature_sarmatia_foldable_eutectic',\n",
       " 'feature_plum_anemometrical_guessing',\n",
       " 'feature_gubernacular_liguloid_frankie',\n",
       " 'feature_castigatory_hundredfold_hearthrug',\n",
       " 'feature_pennsylvanian_sibylic_chanoyu',\n",
       " 'feature_unreaving_intensive_docudrama',\n",
       " 'feature_relinquished_incognizable_batholith',\n",
       " 'feature_indusiate_canned_cosh',\n",
       " 'feature_maglemosian_kittle_coachbuilding',\n",
       " 'feature_unreeling_homeothermic_macedonia',\n",
       " 'feature_asteriated_invigorated_penitence',\n",
       " 'feature_anucleate_knotted_nonage',\n",
       " 'feature_shrinelike_unreplaceable_nitrogenization',\n",
       " 'feature_lacerable_backmost_vaseline',\n",
       " 'feature_unreceipted_latest_lesser',\n",
       " 'feature_unimaginable_sec_kaka',\n",
       " 'feature_goidelic_gobelin_ledge',\n",
       " 'feature_incondite_undisappointing_telephotograph',\n",
       " 'feature_concoctive_symmetric_abulia',\n",
       " 'feature_anglophobic_unformed_maneuverer',\n",
       " 'feature_gravimetric_ski_enigma',\n",
       " 'feature_balmiest_spinal_roundelay',\n",
       " 'feature_required_bibliological_tonga',\n",
       " 'feature_amoroso_wimpish_maturing',\n",
       " 'feature_exertive_unmodernised_scaup',\n",
       " 'feature_rude_booziest_ilium',\n",
       " 'feature_uncompelled_curvy_amerindian',\n",
       " 'feature_septuple_bonapartean_sanbenito',\n",
       " 'feature_tottery_unmetalled_codder',\n",
       " 'feature_tachygraphical_sedimentological_mesoderm',\n",
       " 'feature_adsorbed_blizzardy_burlesque',\n",
       " 'feature_wistful_tussive_cycloserine',\n",
       " 'feature_superjacent_grubby_axillary',\n",
       " 'feature_biological_caprine_cannoneer',\n",
       " 'feature_unreversed_fain_jute',\n",
       " 'feature_unexalted_rebel_kofta',\n",
       " 'feature_doggish_mouthwatering_abelard',\n",
       " 'feature_forfeit_contributing_joinder',\n",
       " 'feature_necked_moresque_lowell',\n",
       " 'feature_footling_unpuckered_lophophore',\n",
       " 'feature_thorniest_laughable_hindustani',\n",
       " 'feature_hotter_cattish_aridity',\n",
       " 'feature_developing_behind_joan',\n",
       " 'feature_ectodermal_mandaean_saffian',\n",
       " 'feature_crimpier_gude_housedog',\n",
       " 'feature_probationary_readying_roundelay',\n",
       " 'feature_inserted_inconvertible_functioning',\n",
       " 'feature_manifold_melodramatic_girl',\n",
       " 'feature_drizzling_refrigerative_imperfection',\n",
       " 'feature_sardonic_primary_shadwell',\n",
       " 'feature_monocyclic_galliambic_par',\n",
       " 'feature_smutty_prohibited_sullivan',\n",
       " 'feature_productile_auriform_fil',\n",
       " 'feature_accommodable_crinite_cleft',\n",
       " 'feature_clipped_kurdish_grainer',\n",
       " 'feature_dustproof_unafraid_stampede',\n",
       " 'feature_neutered_postpositive_writ',\n",
       " 'feature_twelve_haphazard_pantography',\n",
       " 'feature_riskier_ended_typo',\n",
       " 'feature_smaller_colored_immurement',\n",
       " 'feature_snatchy_xylic_institution',\n",
       " 'feature_conchal_angriest_oophyte',\n",
       " 'feature_multiseriate_oak_benzidine',\n",
       " 'feature_gobioid_transhuman_interconnection',\n",
       " 'feature_reservable_peristomal_emden',\n",
       " 'feature_inestimable_unmoral_extraversion',\n",
       " 'feature_nubby_sissified_value',\n",
       " 'feature_incorporating_abominable_daily',\n",
       " 'feature_herbaged_brownish_consubstantialist',\n",
       " 'feature_solemn_wordier_needlework',\n",
       " 'feature_evangelistic_cruel_dissimilitude',\n",
       " 'feature_impetratory_shuttered_chewer',\n",
       " 'feature_referenced_biliteral_chiropody',\n",
       " 'feature_eleatic_fellow_auctioneer',\n",
       " 'feature_malpighian_vaporized_biogen',\n",
       " 'feature_expiscatory_wriest_colportage',\n",
       " 'feature_yelled_hysteretic_eath',\n",
       " 'feature_bitterish_buttocked_turtleneck',\n",
       " 'feature_percipient_atelectatic_cinnamon',\n",
       " 'feature_gobony_premonitory_twinkler',\n",
       " 'feature_twittery_tai_attainment',\n",
       " 'feature_crooked_wally_lobation',\n",
       " 'feature_crookback_workable_infringement',\n",
       " 'feature_brawling_unpeppered_comedian',\n",
       " 'feature_glyphographic_reparable_empyrean',\n",
       " 'feature_noctilucent_subcortical_proportionality',\n",
       " 'feature_guardian_frore_rolling',\n",
       " 'feature_denuded_typed_wattmeter',\n",
       " 'feature_unreachable_neritic_saracen',\n",
       " 'feature_enzymatic_poorest_advocaat',\n",
       " 'feature_wariest_vulnerable_unmorality',\n",
       " 'feature_guttering_half_spondee',\n",
       " 'feature_distressed_bloated_disquietude',\n",
       " 'feature_leaky_overloaded_rhodium',\n",
       " 'feature_unsapped_anionic_catherine',\n",
       " 'feature_kissable_forfeit_egotism',\n",
       " 'feature_unsizable_ancestral_collocutor',\n",
       " 'feature_healthier_unconnected_clave',\n",
       " 'feature_cirsoid_buddhism_vespa',\n",
       " 'feature_rid_conveyable_cinchonization',\n",
       " 'feature_donsie_folkish_renitency',\n",
       " 'feature_agee_sold_microhabitat',\n",
       " 'feature_newfangled_huddled_gest',\n",
       " 'feature_clandestine_inkiest_silkworm',\n",
       " 'feature_unutterable_softening_roper',\n",
       " 'feature_balaamitical_electropositive_exhaustibility',\n",
       " 'feature_unvalued_untangled_keener',\n",
       " 'feature_undisturbing_quadrifid_reinhardt',\n",
       " 'feature_bucked_costume_malagasy',\n",
       " 'feature_joint_unreturning_basalt',\n",
       " 'feature_coordinate_shyer_evildoing',\n",
       " 'feature_carunculate_discursive_hectare',\n",
       " 'feature_cynic_unreckonable_feoffment',\n",
       " 'feature_cnidarian_micrologic_sousaphone',\n",
       " 'feature_unperceivable_unrumpled_appendant',\n",
       " 'feature_dissolvable_chrismal_obtund',\n",
       " 'feature_choosier_uncongenial_coachwood',\n",
       " 'feature_grimmest_prostate_doctrinaire',\n",
       " 'feature_granulative_uncritical_agostini',\n",
       " 'feature_convalescence_deuteranopic_lemuroid',\n",
       " 'feature_disintegrable_snakier_zion',\n",
       " 'feature_thoughtful_accommodable_lack',\n",
       " 'feature_basophil_urdy_matzo',\n",
       " 'feature_repellant_unwanted_clarinetist',\n",
       " 'feature_antimonarchist_ordainable_quarterage',\n",
       " 'feature_hardback_saturnalian_cyclometer',\n",
       " 'feature_mythic_florentine_psammite',\n",
       " 'feature_serpentiform_incomplete_bessarabia',\n",
       " 'feature_unappeasable_employed_photoelectron',\n",
       " 'feature_seaboard_adducent_polynesian',\n",
       " 'feature_genoese_uncreditable_subregion',\n",
       " 'feature_dexter_unstifled_snoring',\n",
       " 'feature_protonematal_springtime_varioloid',\n",
       " 'feature_orchitic_reported_coloration',\n",
       " 'feature_stelliform_curling_trawler',\n",
       " 'feature_athenian_pragmatism_isomorphism',\n",
       " 'feature_abating_unadaptable_weakfish',\n",
       " 'feature_instructional_desensitized_symmetallism',\n",
       " 'feature_disarrayed_rarefactive_trisulphide',\n",
       " 'feature_partible_amphibrachic_classicism',\n",
       " 'feature_ecstatic_foundational_crinoidea',\n",
       " 'feature_unimproved_courtliest_uncongeniality',\n",
       " 'feature_cosy_microtonal_cedar',\n",
       " 'feature_heedful_argyle_russianization',\n",
       " 'feature_unhonoured_detested_xenocryst',\n",
       " 'feature_sicker_spelaean_endplay',\n",
       " 'feature_coordinated_astir_vituperation',\n",
       " 'feature_stratocratic_aerodynamic_herero',\n",
       " 'feature_uneasy_unaccommodating_immortality',\n",
       " 'feature_professional_platonic_marten',\n",
       " 'feature_detrital_respected_parlance',\n",
       " 'feature_contraceptive_cartelist_beast',\n",
       " 'feature_tapestried_madding_acclimatiser',\n",
       " 'feature_optic_mycelial_whimper',\n",
       " 'feature_liftable_direful_polyploid',\n",
       " 'feature_objective_micro_langton',\n",
       " 'feature_entopic_interpreted_subsidiary',\n",
       " 'feature_saclike_hyphal_postulator',\n",
       " 'feature_recent_shorty_preferment',\n",
       " 'feature_strip_honoured_trail',\n",
       " 'feature_unsheltered_doughtiest_episiotomy',\n",
       " 'feature_acclimatisable_unfeigned_maghreb',\n",
       " 'feature_galactopoietic_luckiest_protecting',\n",
       " 'feature_scarcest_vaporized_max',\n",
       " 'feature_spicier_unstripped_initial',\n",
       " 'feature_hooly_chekhovian_phytogeographer',\n",
       " 'feature_smouldering_underground_wingspan',\n",
       " 'feature_phantasmal_extenuative_britain',\n",
       " 'feature_sciurine_stibial_lintwhite',\n",
       " 'feature_eucharistic_widowed_misfeasance',\n",
       " 'feature_libratory_seizable_orlando',\n",
       " 'feature_brackish_obstructed_almighty',\n",
       " 'feature_translucid_neuroanatomical_sego',\n",
       " 'feature_unheeded_stylar_planarian',\n",
       " 'feature_preceptive_rushed_swedenborgian',\n",
       " 'feature_sumerian_descendible_kalpa',\n",
       " 'feature_jazziest_spellbinding_philabeg',\n",
       " 'feature_dormie_sodden_steed',\n",
       " 'feature_directoire_propositional_clydebank',\n",
       " 'feature_triangled_rubber_skein',\n",
       " 'feature_vendean_thwartwise_resistant',\n",
       " 'feature_preoral_tonsorial_souk',\n",
       " 'feature_virescent_telugu_neighbour',\n",
       " 'feature_prefigurative_downstream_transvaluation',\n",
       " 'feature_undepreciated_partitive_ipomoea',\n",
       " 'feature_coactive_bandoleered_trogon',\n",
       " 'feature_southerly_assonant_amicability',\n",
       " 'feature_cortical_halt_catcher',\n",
       " 'feature_queenliest_childing_ritual',\n",
       " 'feature_antarthritic_syzygial_wonderland',\n",
       " 'feature_revitalizing_rutilant_swastika',\n",
       " 'feature_holy_chic_cali',\n",
       " 'feature_hermitical_stark_serfhood',\n",
       " 'feature_deformable_productile_piglet',\n",
       " 'feature_lentissimo_ducky_quadroon',\n",
       " 'feature_happening_tristful_yodeling',\n",
       " 'feature_guardant_giocoso_natterjack',\n",
       " 'feature_bootleg_clement_joe',\n",
       " 'feature_thousandth_hierarchal_plight',\n",
       " 'feature_unhoped_hex_ventriloquism',\n",
       " 'feature_unappreciated_humiliated_misapprehension',\n",
       " 'feature_cragged_sacred_malabo',\n",
       " 'feature_idled_unwieldy_improvement',\n",
       " 'feature_censorial_leachier_rickshaw',\n",
       " 'feature_carbuncled_athanasian_ampul',\n",
       " 'feature_teased_pinpoint_grant',\n",
       " 'feature_periclean_proportionable_amaranth',\n",
       " 'feature_lithuanian_fabianism_pedagogy',\n",
       " 'feature_unamenable_prevalent_trilobite',\n",
       " 'feature_intermingled_reedier_rookery',\n",
       " 'feature_jangly_weedier_bhang',\n",
       " 'feature_bubbling_pedestrian_convection',\n",
       " 'feature_supportive_explanatory_powder',\n",
       " 'feature_ruttier_freakier_perversion',\n",
       " 'feature_duskier_wispiest_midwesterner',\n",
       " 'feature_martial_hallowed_incorruptibility',\n",
       " 'feature_exhaling_awkward_inhabitant',\n",
       " 'feature_cristate_desirable_chime',\n",
       " 'feature_pseudo_bounden_hereafter',\n",
       " 'feature_plentiful_numeral_pascal',\n",
       " 'feature_australasian_dissatisfactory_intermarriage',\n",
       " 'feature_adverbial_inexplicable_schul',\n",
       " 'feature_synodal_feisty_weave',\n",
       " 'feature_geomedical_imbued_clunk',\n",
       " 'feature_turbaned_excentric_rockery',\n",
       " 'feature_unadjusted_dissectible_warley',\n",
       " 'feature_demountable_unprejudiced_neighbourhood',\n",
       " 'feature_twisted_saronic_necrologist',\n",
       " 'feature_celebratory_assayable_carlisle',\n",
       " 'feature_cheerful_aphidian_orchestrion',\n",
       " 'feature_huggable_interim_doline',\n",
       " 'feature_tinkliest_unstuffy_manhunt',\n",
       " 'feature_transisthmian_inculcative_heldentenor',\n",
       " 'feature_rampant_barren_sapodilla',\n",
       " 'feature_parturient_liberian_gamal',\n",
       " 'feature_circulating_abolition_ethyne',\n",
       " 'feature_often_undermanned_nudist',\n",
       " 'feature_treacly_heuristic_binding',\n",
       " 'feature_stannic_peevish_idocrase',\n",
       " 'feature_biobibliographical_carnal_atomisation',\n",
       " 'feature_depletory_cannular_automatism',\n",
       " 'feature_collectable_distinguishing_dichroite',\n",
       " 'feature_garlicky_allopatric_sarcocarp',\n",
       " 'feature_whiskered_unobjectionable_quintet',\n",
       " 'feature_enteric_booked_flexography',\n",
       " 'feature_inlaid_defensible_gladiator',\n",
       " 'feature_natal_scalloped_edwardianism',\n",
       " 'feature_sphygmic_young_latium',\n",
       " 'feature_aymaran_indusial_elodea',\n",
       " 'feature_ideological_trinal_rebuttal',\n",
       " 'feature_figurative_uncertificated_indigent',\n",
       " 'feature_improbable_pouched_gaitskell',\n",
       " 'feature_unhazarded_droning_bellow',\n",
       " 'feature_monarchic_blah_cellarman',\n",
       " 'feature_infested_feathered_pen',\n",
       " 'feature_bulbed_viscose_toy',\n",
       " 'feature_walnut_sceptical_crystallization',\n",
       " 'feature_patristic_intelligential_crackajack',\n",
       " 'feature_quodlibetic_enrapt_miscalculation',\n",
       " 'feature_lucullian_unshunned_ulex',\n",
       " 'feature_ten_male_phoenician',\n",
       " 'feature_doctrinal_viewier_dentary',\n",
       " 'feature_careworn_motivational_requisite',\n",
       " 'feature_psycholinguistic_junoesque_central',\n",
       " 'feature_revolting_pharmacological_notability',\n",
       " 'feature_unheeding_tauromachian_ballup',\n",
       " 'feature_octopod_skirting_jurat',\n",
       " 'feature_precursory_catching_inertia',\n",
       " 'feature_monosyllabic_homey_omicron',\n",
       " 'feature_thrasonical_subaltern_inoculation',\n",
       " 'feature_unrealistic_inoperable_publishing',\n",
       " 'feature_exuberant_helicoidal_baldachin',\n",
       " 'feature_perceptive_unnavigable_elegancy',\n",
       " 'feature_recurrent_perversive_injury',\n",
       " 'feature_overrank_shavian_epitaxy',\n",
       " 'feature_petticoated_unobeyed_mugwort',\n",
       " 'feature_stimulant_proximal_moocher',\n",
       " 'feature_datival_crucial_chevrotain',\n",
       " 'feature_just_flavescent_draff',\n",
       " 'feature_cephalopod_arrased_jird',\n",
       " 'feature_endogamic_suasible_boasting',\n",
       " 'feature_rheumatic_bravest_pantisocracy',\n",
       " 'feature_store_comforted_goiter',\n",
       " 'feature_goofy_glassed_cetacea',\n",
       " 'feature_crushed_gleg_reintroduction',\n",
       " 'feature_scald_vanishing_enchainment',\n",
       " 'feature_blinded_memorable_wring',\n",
       " 'feature_clavate_geriatric_aldebaran',\n",
       " 'feature_palimpsest_inoffensive_coiffeuse',\n",
       " 'feature_adored_empyreal_revel',\n",
       " 'feature_schizomycetic_gooey_mynah',\n",
       " 'feature_pourable_multangular_requisition',\n",
       " 'feature_resuscitated_taloned_borak',\n",
       " \"feature_devoured_disunited_didn't\",\n",
       " 'feature_undefeated_unworkmanlike_probe',\n",
       " 'feature_archaean_unregarded_caravel',\n",
       " 'feature_concealed_artful_thaw',\n",
       " 'feature_bluff_carbonyl_verbena',\n",
       " 'feature_gymnorhinal_unreached_carthusian',\n",
       " 'feature_falsifiable_performative_maxixe',\n",
       " 'feature_vapourish_ichthyotic_causerie',\n",
       " 'feature_craggier_windier_apologia',\n",
       " 'feature_elaborate_intimate_bor',\n",
       " 'feature_meteorological_tushed_ester',\n",
       " 'feature_iffy_pretty_gumming',\n",
       " 'feature_numeral_cagey_haulm',\n",
       " 'feature_piping_geotactic_cusp',\n",
       " 'feature_rutilant_obliterate_potassium',\n",
       " 'feature_intromittent_evasive_swordcraft',\n",
       " 'feature_augmentable_scriabin_fortnight',\n",
       " 'feature_bellicose_lunatic_glorification',\n",
       " 'feature_undebauched_cobaltic_guerrilla',\n",
       " 'feature_exoergic_unschooled_lipid',\n",
       " 'feature_inhospitable_baked_elopement',\n",
       " 'feature_dysgenic_putrefied_nosegay',\n",
       " 'feature_terpsichorean_hatable_glut',\n",
       " 'feature_aeolic_downy_forefinger',\n",
       " 'feature_undrowned_ascending_pungency',\n",
       " 'feature_flagging_undenominational_gauffering',\n",
       " 'feature_sworn_satanic_sprechstimme',\n",
       " 'feature_atheist_pompeian_fogsignal',\n",
       " 'feature_commotional_unhealable_berserk',\n",
       " 'feature_grizzled_reformist_soberer',\n",
       " 'feature_twiggier_fogged_prosodist',\n",
       " 'feature_easterly_subtractive_faroese',\n",
       " 'feature_looted_eleven_corpora',\n",
       " 'feature_mensal_amusive_phosphorylase',\n",
       " 'feature_shellier_dowable_chyme',\n",
       " 'feature_daunting_nomenclatorial_facility',\n",
       " 'feature_bedfast_primordial_hyponym',\n",
       " 'feature_herpetologic_unjoyful_lodgepole',\n",
       " 'feature_unlearned_plicate_megabit',\n",
       " 'feature_cyclopedic_maestoso_daguerreotypist',\n",
       " 'feature_disjunct_hardened_picturing',\n",
       " 'feature_congolese_convenable_coolth',\n",
       " 'feature_epigraphic_leucocratic_rutherford',\n",
       " 'feature_hatched_myriad_biogen',\n",
       " 'feature_gnomonic_fixative_vocalise',\n",
       " 'feature_commo_flavored_epitomizer',\n",
       " 'feature_occurrent_suggestible_doubter',\n",
       " 'feature_intracardiac_circumfluent_pepper',\n",
       " 'feature_contaminable_exilic_girandole',\n",
       " 'feature_guardant_irrepealable_onanist',\n",
       " 'feature_lowered_toric_charmeuse',\n",
       " 'feature_altruistic_congenital_disinflation',\n",
       " 'feature_rowdyish_overcritical_digression',\n",
       " 'feature_peccant_zanier_undersigned',\n",
       " 'feature_promised_intramolecular_sora',\n",
       " 'feature_bespoke_combining_farrow',\n",
       " 'feature_entozoic_adolescent_asci',\n",
       " 'feature_titulary_merriest_intelligencer',\n",
       " 'feature_unhomely_monoclinic_acajou',\n",
       " 'feature_antistatic_cabbagy_bluecoat',\n",
       " 'feature_uncropped_tipsier_postulator',\n",
       " 'feature_diocesan_reinvigorated_ebullience',\n",
       " 'feature_axonometric_unkindly_sienna',\n",
       " 'feature_adynamic_ramstam_urtica',\n",
       " 'feature_ultra_unpolluted_adsorbent',\n",
       " 'feature_bespangled_prim_might',\n",
       " 'feature_comely_typal_softie',\n",
       " 'feature_adherent_judaic_gerry',\n",
       " 'feature_unspoilt_astronomical_lumper',\n",
       " 'feature_sovietism_interred_toile',\n",
       " 'feature_loftier_sightly_lyric',\n",
       " 'feature_dada_draughtiest_cinchonisation',\n",
       " ...]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "lgb_all.feature_name_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-67-5fc463007f8b>:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  lgb1.fit(df1[eras.isin(np.arange(1, 304, 4))][features], df1[eras.isin(np.arange(1, 304, 4))][target])\n",
      "<ipython-input-67-5fc463007f8b>:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  lgb2.fit(df1[eras.isin(np.arange(2, 304, 4))][features], df1[eras.isin(np.arange(2, 304, 4))][target])\n",
      "<ipython-input-67-5fc463007f8b>:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  lgb3.fit(df1[eras.isin(np.arange(3, 304, 4))][features], df1[eras.isin(np.arange(3, 304, 4))][target])\n",
      "<ipython-input-67-5fc463007f8b>:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  lgb4.fit(df1[eras.isin(np.arange(4, 304, 4))][features], df1[eras.isin(np.arange(4, 304, 4))][target])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# train models on subsamples eras in df1\n",
    "lgb1 = lightgbm.LGBMRegressor()\n",
    "lgb1.fit(df1[eras.isin(np.arange(1, 304, 4))][features], df1[eras.isin(np.arange(1, 304, 4))][target])\n",
    "\n",
    "lgb2 = lightgbm.LGBMRegressor()\n",
    "lgb2.fit(df1[eras.isin(np.arange(2, 304, 4))][features], df1[eras.isin(np.arange(2, 304, 4))][target])\n",
    "\n",
    "lgb3 = lightgbm.LGBMRegressor()\n",
    "lgb3.fit(df1[eras.isin(np.arange(3, 304, 4))][features], df1[eras.isin(np.arange(3, 304, 4))][target])\n",
    "\n",
    "lgb4 = lightgbm.LGBMRegressor()\n",
    "lgb4.fit(df1[eras.isin(np.arange(4, 304, 4))][features], df1[eras.isin(np.arange(4, 304, 4))][target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# calculate predictions for each model\n",
    "preds = [\n",
    "        pd.Series(model.predict(df2[features]), index=df2.index)\n",
    "    for model in [lgb_all, lgb1, lgb2, lgb3, lgb4]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04754437089655174"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# numerai score of model trained on all eras of df1\n",
    "numerai_score(df2[target], preds[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05004420837447432"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# numerai score of ensemble of models trained on all subsampled eras of df1\n",
    "numerai_score(df2[target], pd.concat(preds, axis=1)[[1,2,3,4]].mean(axis=1))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gotcha: eras are homogenous, but different from each other\n",
    "##  Random cross-validation will look much better than cross-validating by era\n",
    "\n",
    "Even for a simple linear model, taking a random shuffle reports a correlation of ~5.1%, but a time series split reports a lower score of ~2.6%"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gotcha: Eras are now overlapping!\n",
    "##  Even era-wise cross-validation will now have leakage!\n",
    "Eras are weekly (5 days), but the target is four weeks out (20 days). You can either use blocks of eras and purge the overlapping sections or subsample the eras (every 4th era) to make them not overlapping and prevent leakage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-71-73ffccb7a00f>:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df1 = df[eras<=eras.median()]\n",
      "<ipython-input-71-73ffccb7a00f>:4: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df2 = df[eras>eras.median()]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# Subsample data to every 4th era to prevent overlapping eras\n",
    "df = df[eras.isin(np.arange(1, 575, 4))]\n",
    "df1 = df[eras<=eras.median()]\n",
    "df2 = df[eras>eras.median()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "eras = eras[eras.isin(np.arange(1, 575, 4))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# Because the TimeSeriesSplit class in sklearn does not use groups and won't respect era boundries, we implement\n",
    "# a version that will\n",
    "\n",
    "from sklearn.model_selection._split import _BaseKFold, indexable, _num_samples\n",
    "\n",
    "class TimeSeriesSplitGroups(_BaseKFold):\n",
    "    def __init__(self, n_splits=5):\n",
    "        super().__init__(n_splits, shuffle=False, random_state=None)\n",
    "\n",
    "    def split(self, X, y=None, groups=None):\n",
    "        X, y, groups = indexable(X, y, groups)\n",
    "        n_samples = _num_samples(X)\n",
    "        n_splits = self.n_splits\n",
    "        n_folds = n_splits + 1\n",
    "        group_list = np.unique(groups)\n",
    "        n_groups = len(group_list)\n",
    "        if n_folds > n_groups:\n",
    "            raise ValueError(\n",
    "                (\"Cannot have number of folds ={0} greater\"\n",
    "                 \" than the number of samples: {1}.\").format(n_folds,\n",
    "                                                             n_groups))\n",
    "        indices = np.arange(n_samples)\n",
    "        test_size = (n_groups // n_folds)\n",
    "        test_starts = range(test_size + n_groups % n_folds,\n",
    "                            n_groups, test_size)\n",
    "        test_starts = list(test_starts)[::-1]\n",
    "        for test_start in test_starts:\n",
    "            \n",
    "            yield (indices[groups.isin(group_list[:test_start])],\n",
    "                   indices[groups.isin(group_list[test_start:test_start + test_size])])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "crossvalidators = [\n",
    "    model_selection.KFold(5),\n",
    "    model_selection.KFold(5, shuffle=True),\n",
    "    model_selection.GroupKFold(5),\n",
    "    TimeSeriesSplitGroups(5)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "def correlation_score(y_true, y_pred):\n",
    "    return np.corrcoef(y_true, y_pred)[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold(n_splits=5, random_state=None, shuffle=False)\n",
      "0.043051034520594625\n",
      "\n",
      "KFold(n_splits=5, random_state=None, shuffle=True)\n",
      "0.053213488214390016\n",
      "\n",
      "GroupKFold(n_splits=5)\n",
      "0.0431732239247547\n",
      "\n",
      "TimeSeriesSplitGroups(n_splits=5)\n",
      "0.02830277262900164\n",
      "\n",
      "\r"
     ]
    }
   ],
   "source": [
    "for cv in crossvalidators:\n",
    "    print(cv)\n",
    "    print(np.mean(\n",
    "            model_selection.cross_val_score(\n",
    "            linear_model.LinearRegression(),\n",
    "            df[features],\n",
    "            df[target],\n",
    "            cv=cv,\n",
    "            n_jobs=1,\n",
    "            groups=eras,\n",
    "            scoring=metrics.make_scorer(correlation_score, greater_is_better=True)\n",
    "        )))\n",
    "    print()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gotcha:  {0, 1} are noticeably different from {0.25, 0.75}\n",
    "This makes training a classifier one-versus-rest behave counterintuitively.\n",
    "\n",
    "Specifically, a multinomial classifier seem to learn how to pick out extreme targets, and their predictions are the most correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4997237888887051"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# Train a standard logistic regression as a classifier\n",
    "logistic = linear_model.LogisticRegression(multi_class='multinomial', solver='saga')\n",
    "logistic.fit(df1[features], (df1[target]*4).astype(int))\n",
    "logistic.score(df1[features], (df1[target]*4).astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.30231447, -0.82085036,  0.23839784,  0.74575541],\n",
       "       [ 0.30231447,  1.        , -0.56979841,  0.14798245,  0.19669416],\n",
       "       [-0.82085036, -0.56979841,  1.        , -0.59999079, -0.83168858],\n",
       "       [ 0.23839784,  0.14798245, -0.59999079,  1.        ,  0.35628795],\n",
       "       [ 0.74575541,  0.19669416, -0.83168858,  0.35628795,  1.        ]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# The first and last class are highly correlated\n",
    "corrs=np.corrcoef(logistic.predict_proba(df2[features]).T)\n",
    "plt.imshow(corrs, vmin=-1, vmax=1, cmap=\"RdYlGn\")\n",
    "corrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03342941514243905"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# Out-of-sample correlation is 3.5%\n",
    "preds = pd.Series(logistic.predict_proba(df2[features]).dot(logistic.classes_), index=df2.index)\n",
    "numerai_score(df2[target], preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03388223159438873"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# A standard linear model has a slightly lower correlation out-of-sample\n",
    "linear = linear_model.LinearRegression()\n",
    "linear.fit(df1[features], df1[target])\n",
    "linear.score(df2[features], df2[target])\n",
    "preds = pd.Series(linear.predict(df2[features]), index=df2.index)\n",
    "numerai_score(df2[target], preds)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eras can be more or less applicable to other eras\n",
    "You can test this be splitting the eras into blocks of 15, training on each block, and evaluating on each other block."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# convert the subsampled era labels to continuous integers\n",
    "eras_sub = (eras-1)//4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60     71302\n",
       "105    69495\n",
       "120    67799\n",
       "45     67629\n",
       "90     66498\n",
       "75     62093\n",
       "30     61595\n",
       "15     54362\n",
       "0      43007\n",
       "135    42396\n",
       "Name: erano, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "eras15 = (eras_sub // 15) * 15\n",
    "eras15.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "15\n",
      "30\n",
      "45\n",
      "60\n",
      "75\n",
      "90\n",
      "105\n",
      "120\n",
      "135\n",
      "\r"
     ]
    }
   ],
   "source": [
    "results15 = []\n",
    "for train_era, tdf in df.groupby(eras15):\n",
    "    print(train_era)\n",
    "    model = linear_model.LinearRegression()\n",
    "    model.fit(tdf[features], tdf[target])\n",
    "    for test_era, tdf in df.groupby(eras15):\n",
    "        results15.append([\n",
    "            train_era,\n",
    "            test_era,\n",
    "            correlation_score(tdf[target], model.predict(tdf[features]))\n",
    "        ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>test_era</th>\n",
       "      <th>0</th>\n",
       "      <th>15</th>\n",
       "      <th>30</th>\n",
       "      <th>45</th>\n",
       "      <th>60</th>\n",
       "      <th>75</th>\n",
       "      <th>90</th>\n",
       "      <th>105</th>\n",
       "      <th>120</th>\n",
       "      <th>135</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_era</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.201005</td>\n",
       "      <td>0.002764</td>\n",
       "      <td>0.002194</td>\n",
       "      <td>-0.000582</td>\n",
       "      <td>0.002822</td>\n",
       "      <td>0.001914</td>\n",
       "      <td>0.002899</td>\n",
       "      <td>0.003945</td>\n",
       "      <td>-0.008350</td>\n",
       "      <td>-0.000860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.044271</td>\n",
       "      <td>0.173445</td>\n",
       "      <td>0.030770</td>\n",
       "      <td>-0.004885</td>\n",
       "      <td>-0.002098</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>-0.002952</td>\n",
       "      <td>-0.007466</td>\n",
       "      <td>-0.002334</td>\n",
       "      <td>0.002434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.035998</td>\n",
       "      <td>0.033648</td>\n",
       "      <td>0.164322</td>\n",
       "      <td>0.001591</td>\n",
       "      <td>-0.004259</td>\n",
       "      <td>-0.007258</td>\n",
       "      <td>-0.001260</td>\n",
       "      <td>0.006638</td>\n",
       "      <td>0.006121</td>\n",
       "      <td>-0.001279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.035116</td>\n",
       "      <td>0.032812</td>\n",
       "      <td>0.034598</td>\n",
       "      <td>0.158456</td>\n",
       "      <td>0.016940</td>\n",
       "      <td>0.026077</td>\n",
       "      <td>0.023469</td>\n",
       "      <td>0.011119</td>\n",
       "      <td>0.023130</td>\n",
       "      <td>0.021435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.019893</td>\n",
       "      <td>0.017161</td>\n",
       "      <td>0.014331</td>\n",
       "      <td>0.017833</td>\n",
       "      <td>0.157544</td>\n",
       "      <td>0.018488</td>\n",
       "      <td>0.012532</td>\n",
       "      <td>0.017500</td>\n",
       "      <td>0.014019</td>\n",
       "      <td>0.003835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.031152</td>\n",
       "      <td>0.026209</td>\n",
       "      <td>0.025698</td>\n",
       "      <td>0.019656</td>\n",
       "      <td>0.016988</td>\n",
       "      <td>0.173625</td>\n",
       "      <td>0.024868</td>\n",
       "      <td>0.015309</td>\n",
       "      <td>0.024914</td>\n",
       "      <td>0.011541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.043645</td>\n",
       "      <td>0.028661</td>\n",
       "      <td>0.024327</td>\n",
       "      <td>0.023561</td>\n",
       "      <td>0.005410</td>\n",
       "      <td>0.027441</td>\n",
       "      <td>0.164304</td>\n",
       "      <td>0.028544</td>\n",
       "      <td>0.024550</td>\n",
       "      <td>0.017838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0.036637</td>\n",
       "      <td>0.026314</td>\n",
       "      <td>0.018259</td>\n",
       "      <td>0.008183</td>\n",
       "      <td>0.010747</td>\n",
       "      <td>0.012723</td>\n",
       "      <td>0.030185</td>\n",
       "      <td>0.153321</td>\n",
       "      <td>0.020505</td>\n",
       "      <td>0.009049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.033544</td>\n",
       "      <td>0.030863</td>\n",
       "      <td>0.019560</td>\n",
       "      <td>0.026703</td>\n",
       "      <td>0.010307</td>\n",
       "      <td>0.030687</td>\n",
       "      <td>0.025627</td>\n",
       "      <td>0.027043</td>\n",
       "      <td>0.151762</td>\n",
       "      <td>0.022775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>0.021913</td>\n",
       "      <td>0.014696</td>\n",
       "      <td>0.012057</td>\n",
       "      <td>0.018858</td>\n",
       "      <td>0.001777</td>\n",
       "      <td>0.012767</td>\n",
       "      <td>0.013768</td>\n",
       "      <td>0.009278</td>\n",
       "      <td>0.021799</td>\n",
       "      <td>0.190487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "test_era        0         15        30        45        60        75   \\\n",
       "train_era                                                               \n",
       "0          0.201005  0.002764  0.002194 -0.000582  0.002822  0.001914   \n",
       "15         0.044271  0.173445  0.030770 -0.004885 -0.002098  0.000718   \n",
       "30         0.035998  0.033648  0.164322  0.001591 -0.004259 -0.007258   \n",
       "45         0.035116  0.032812  0.034598  0.158456  0.016940  0.026077   \n",
       "60         0.019893  0.017161  0.014331  0.017833  0.157544  0.018488   \n",
       "75         0.031152  0.026209  0.025698  0.019656  0.016988  0.173625   \n",
       "90         0.043645  0.028661  0.024327  0.023561  0.005410  0.027441   \n",
       "105        0.036637  0.026314  0.018259  0.008183  0.010747  0.012723   \n",
       "120        0.033544  0.030863  0.019560  0.026703  0.010307  0.030687   \n",
       "135        0.021913  0.014696  0.012057  0.018858  0.001777  0.012767   \n",
       "\n",
       "test_era        90        105       120       135  \n",
       "train_era                                          \n",
       "0          0.002899  0.003945 -0.008350 -0.000860  \n",
       "15        -0.002952 -0.007466 -0.002334  0.002434  \n",
       "30        -0.001260  0.006638  0.006121 -0.001279  \n",
       "45         0.023469  0.011119  0.023130  0.021435  \n",
       "60         0.012532  0.017500  0.014019  0.003835  \n",
       "75         0.024868  0.015309  0.024914  0.011541  \n",
       "90         0.164304  0.028544  0.024550  0.017838  \n",
       "105        0.030185  0.153321  0.020505  0.009049  \n",
       "120        0.025627  0.027043  0.151762  0.022775  \n",
       "135        0.013768  0.009278  0.021799  0.190487  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "results_df = pd.DataFrame(\n",
    "    results15,\n",
    "    columns=[\"train_era\", \"test_era\", \"score\"]\n",
    ").pivot(index=\"train_era\", columns=\"test_era\", values=\"score\")\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1740e76fe20>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# Each row here is the training block of eras, each column is a testing block of eras.\n",
    "# Note that there is a period in the early groups that does not seem to be relevant to other eras, and the\n",
    "# overall performance seems to decrease a bit over time.\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.imshow(results_df, vmin=-0.04, vmax=0.04, cmap=\"RdYlGn\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Here is an advanced paper that talks about generalization.\n",
    "Eras can be thought about in the same way that \"distributions\" or \"environments\" are talked about here\n",
    "https://arxiv.org/pdf/1907.02893.pdf"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gotcha: Since the signal-to-noise ratio is so low, models can take many more iterations than expected, and have scarily high in-sample performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "def our_score(preds, dtrain):\n",
    "    return \"score\", -np.corrcoef(preds, dtrain.get_label())[0,1], False\n",
    "\n",
    "df1 = df[eras<=eras.median()]\n",
    "df2 = df[eras>eras.median()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "dtrain = lightgbm.Dataset(df1[features], df1[target])\n",
    "dtest = lightgbm.Dataset(df2[features], df2[target])\n",
    "dall = lightgbm.Dataset(df[features], df[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.165444 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 5955\n",
      "[LightGBM] [Info] Number of data points in the train set: 306441, number of used features: 1191\n",
      "[LightGBM] [Info] Start training from score 0.500002\n",
      "[20]\ttrain's l2: 0.0498404\ttrain's score: -0.0705298\ttest's l2: 0.0499147\ttest's score: -0.0432763\n",
      "[40]\ttrain's l2: 0.0497469\ttrain's score: -0.0857452\ttest's l2: 0.0498944\ttest's score: -0.0460672\n",
      "[60]\ttrain's l2: 0.0496696\ttrain's score: -0.0967963\ttest's l2: 0.049885\ttest's score: -0.0474742\n",
      "[80]\ttrain's l2: 0.0496023\ttrain's score: -0.105104\ttest's l2: 0.0498799\ttest's score: -0.04843\n",
      "[100]\ttrain's l2: 0.0495423\ttrain's score: -0.112957\ttest's l2: 0.0498792\ttest's score: -0.0487272\n",
      "[120]\ttrain's l2: 0.0494852\ttrain's score: -0.119411\ttest's l2: 0.0498773\ttest's score: -0.0494071\n",
      "[140]\ttrain's l2: 0.0494293\ttrain's score: -0.126061\ttest's l2: 0.0498799\ttest's score: -0.0493329\n",
      "[160]\ttrain's l2: 0.049379\ttrain's score: -0.131419\ttest's l2: 0.0498805\ttest's score: -0.0496771\n",
      "[180]\ttrain's l2: 0.0493279\ttrain's score: -0.137041\ttest's l2: 0.0498819\ttest's score: -0.0498874\n",
      "[200]\ttrain's l2: 0.0492786\ttrain's score: -0.142031\ttest's l2: 0.0498865\ttest's score: -0.0496792\n",
      "[220]\ttrain's l2: 0.0492333\ttrain's score: -0.146381\ttest's l2: 0.0498887\ttest's score: -0.0498318\n",
      "[240]\ttrain's l2: 0.0491874\ttrain's score: -0.150958\ttest's l2: 0.0498928\ttest's score: -0.0496967\n",
      "[260]\ttrain's l2: 0.0491447\ttrain's score: -0.154994\ttest's l2: 0.0498949\ttest's score: -0.0498942\n",
      "[280]\ttrain's l2: 0.0491036\ttrain's score: -0.158454\ttest's l2: 0.0498964\ttest's score: -0.050205\n",
      "[300]\ttrain's l2: 0.0490599\ttrain's score: -0.162638\ttest's l2: 0.0498992\ttest's score: -0.0502883\n",
      "[320]\ttrain's l2: 0.0490188\ttrain's score: -0.166305\ttest's l2: 0.0499066\ttest's score: -0.0497654\n",
      "[340]\ttrain's l2: 0.0489799\ttrain's score: -0.169851\ttest's l2: 0.0499086\ttest's score: -0.0499042\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[360]\ttrain's l2: 0.048939\ttrain's score: -0.173344\ttest's l2: 0.0499133\ttest's score: -0.0498026\n",
      "[380]\ttrain's l2: 0.0488997\ttrain's score: -0.176715\ttest's l2: 0.0499178\ttest's score: -0.0496814\n",
      "[400]\ttrain's l2: 0.0488611\ttrain's score: -0.180243\ttest's l2: 0.0499223\ttest's score: -0.0494745\n",
      "[420]\ttrain's l2: 0.0488229\ttrain's score: -0.183243\ttest's l2: 0.0499256\ttest's score: -0.0495597\n",
      "[440]\ttrain's l2: 0.0487866\ttrain's score: -0.186135\ttest's l2: 0.0499275\ttest's score: -0.0497634\n",
      "[460]\ttrain's l2: 0.0487493\ttrain's score: -0.189103\ttest's l2: 0.0499298\ttest's score: -0.0499453\n",
      "[480]\ttrain's l2: 0.0487116\ttrain's score: -0.191827\ttest's l2: 0.0499307\ttest's score: -0.0503031\n",
      "[500]\ttrain's l2: 0.048675\ttrain's score: -0.194938\ttest's l2: 0.0499368\ttest's score: -0.0499583\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "param = {\n",
    "    'max_depth':3,\n",
    "    'eta':0.1,\n",
    "    'objective':'regression',\n",
    "    'metric':'mse',\n",
    "    'nthread': -1,\n",
    "}\n",
    "evals_result = {}\n",
    "bst = lightgbm.train(\n",
    "    params=param,\n",
    "    train_set=dtrain,\n",
    "    feval=our_score,\n",
    "    num_boost_round=500,\n",
    "    valid_sets=[dtrain, dtest],\n",
    "    valid_names=['train', 'test'],\n",
    "    evals_result=evals_result,\n",
    "    verbose_eval=20,\n",
    ")\n",
    "\n",
    "(-pd.DataFrame({k: v['score'] for k,v in evals_result.items()})).plot(ylim=[0,0.25])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The results are sensitive to the choice of parameters, which should be picked through cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "df1 = df[eras<=eras.median()]\n",
    "df2 = df[eras>eras.median()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "models = [\n",
    "    linear_model.LinearRegression(),\n",
    "] + [\n",
    "    linear_model.ElasticNet(alpha=alpha)\n",
    "    for alpha in [0.01, 0.005, 0.002, 0.001, 0.0005, 0.0002, 0.0001, 0.00005, 0.00002, 0.00001]\n",
    "] + [\n",
    "    lightgbm.LGBMRegressor(n_jobs=-1),\n",
    "    lightgbm.LGBMRegressor(n_jobs=-1, learning_rate=0.01, n_estimators=1000),\n",
    "    lightgbm.LGBMRegressor(n_jobs=-1, colsample_bytree=0.1, learning_rate=0.01, n_estimators=1000),\n",
    "    lightgbm.LGBMRegressor(n_jobs=-1, colsample_bytree=0.1, learning_rate=0.01, n_estimators=1000, max_depth=5),\n",
    "    lightgbm.LGBMRegressor(n_jobs=-1, colsample_bytree=0.1, learning_rate=0.001, n_estimators=10000, max_depth=5),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --  LinearRegression()\n",
      "outsample: 0.03367750447474235, insample: 0.09402339542708368\n",
      "\n",
      " --  ElasticNet(alpha=0.01)\n",
      "outsample: -0.0005272078536780539, insample: 0.0010714748211746435\n",
      "\n",
      " --  ElasticNet(alpha=0.005)\n",
      "outsample: -0.0005272078536780539, insample: 0.0010714748211746435\n",
      "\n",
      " --  ElasticNet(alpha=0.002)\n",
      "outsample: 0.032098604901949525, insample: 0.040379220178221285\n",
      "\n",
      " --  ElasticNet(alpha=0.001)\n",
      "outsample: 0.041284885702416715, insample: 0.056924844014858\n",
      "\n",
      " --  ElasticNet(alpha=0.0005)\n",
      "outsample: 0.042444732328359755, insample: 0.06629127174479907\n",
      "\n",
      " --  ElasticNet(alpha=0.0002)\n",
      "outsample: 0.0442041745202924, insample: 0.07463755180346122\n",
      "\n",
      " --  ElasticNet(alpha=0.0001)\n",
      "outsample: 0.04329204278664591, insample: 0.08110850148323803\n",
      "\n",
      " --  ElasticNet(alpha=5e-05)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.933646559715271, tolerance: 1.5322749614715576\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outsample: 0.04065634510146776, insample: 0.08783594230216349\n",
      "\n",
      " --  ElasticNet(alpha=2e-05)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 158.9556884765625, tolerance: 1.5322749614715576\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outsample: 0.03716145458407298, insample: 0.09243820266795078\n",
      "\n",
      " --  ElasticNet(alpha=1e-05)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 527.0285034179688, tolerance: 1.5322749614715576\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outsample: 0.035643305867069666, insample: 0.0935615475341538\n",
      "\n",
      " --  LGBMRegressor()\n",
      "outsample: 0.042720826857846374, insample: 0.2458630260023973\n",
      "\n",
      " --  LGBMRegressor(learning_rate=0.01, n_estimators=1000)\n",
      "outsample: 0.046965150788966174, insample: 0.2849646676117908\n",
      "\n",
      " --  LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, n_estimators=1000)\n",
      "outsample: 0.05047522452208998, insample: 0.2528724183700595\n",
      "\n",
      " --  LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
      "              n_estimators=1000)\n",
      "outsample: 0.05217323624012492, insample: 0.20903096055051976\n",
      "\n",
      " --  LGBMRegressor(colsample_bytree=0.1, learning_rate=0.001, max_depth=5,\n",
      "              n_estimators=10000)\n",
      "outsample: 0.05290511638289955, insample: 0.2111367205185391\n",
      "\n",
      "\r"
     ]
    }
   ],
   "source": [
    "for model in models:\n",
    "    print(\" -- \", model)   \n",
    "    model.fit(df1[features], df1[target])\n",
    "    outsample = numerai_score(df2[target], pd.Series(model.predict(df2[features]), index=df2.index))\n",
    "    insample = numerai_score(df1[target], pd.Series(model.predict(df1[features]), index=df1.index))\n",
    "    print(\n",
    "        f\"outsample: {outsample}, insample: {insample}\"\n",
    "    )\n",
    "    print()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gotcha: Models with large exposures to individual features tend to perform poorly or inconsistently out of sample ## \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# Train a standard xgboost on half the train eras\n",
    "lgb = lightgbm.LGBMRegressor(n_estimators=1000, max_depth=5, learning_rate=0.01, n_jobs=-1)\n",
    "lgb.fit(df1[features], df1[target])\n",
    "lgb_preds = lgb.predict(df2[features])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Our predictions have correlation < -0.4 and > 0.20 for some single features!\n",
    "Sure hope those features continue to act as they have in the past!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1191.000000\n",
       "mean       -0.001187\n",
       "std         0.085577\n",
       "min        -0.415303\n",
       "25%        -0.059811\n",
       "50%         0.002233\n",
       "75%         0.059406\n",
       "max         0.225358\n",
       "dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "corr_list = []\n",
    "for feature in features:\n",
    "    corr_list.append(np.corrcoef(df2[feature], lgb_preds)[0,1])\n",
    "corr_series = pd.Series(corr_list, index=features)\n",
    "corr_series.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import scipy\n",
    "\n",
    "def neutralize(df,\n",
    "               columns,\n",
    "               neutralizers=None,\n",
    "               proportion=1.0,\n",
    "               normalize=True,\n",
    "               era_col=\"era\"):\n",
    "    if neutralizers is None:\n",
    "        neutralizers = []\n",
    "    unique_eras = df[era_col].unique()\n",
    "    computed = []\n",
    "    for u in unique_eras:\n",
    "        print(u, end=\"\\r\")\n",
    "        df_era = df[df[era_col] == u]\n",
    "        scores = df_era[columns].values\n",
    "        if normalize:\n",
    "            scores2 = []\n",
    "            for x in scores.T:\n",
    "                x = (scipy.stats.rankdata(x, method='ordinal') - .5) / len(x)\n",
    "                x = scipy.stats.norm.ppf(x)\n",
    "                scores2.append(x)\n",
    "            scores = np.array(scores2).T\n",
    "        exposures = df_era[neutralizers].values\n",
    "\n",
    "        scores -= proportion * exposures.dot(\n",
    "            np.linalg.pinv(exposures.astype(np.float32)).dot(scores.astype(np.float32)))\n",
    "\n",
    "        scores /= scores.std(ddof=0)\n",
    "\n",
    "        computed.append(scores)\n",
    "\n",
    "    return pd.DataFrame(np.concatenate(computed),\n",
    "                        columns=columns,\n",
    "                        index=df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-96-4aa87b479082>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2[\"preds\"] = lgb_preds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0573\r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-96-4aa87b479082>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2[\"preds_neutralized\"] = neutralize(df2,\n",
      "<ipython-input-96-4aa87b479082>:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2[\"preds_neutralized\"] = scaler.fit_transform(df2[[\"preds_neutralized\"]]) # transform back to 0-1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "df2[\"preds\"] = lgb_preds\n",
    "df2[\"preds_neutralized\"] = neutralize(df2, \n",
    "                                      columns=[\"preds\"], \n",
    "                                      neutralizers=features, \n",
    "                                      proportion=0.5, # neutralize by 50% within each era\n",
    "                                      normalize=True,\n",
    "                                      era_col='era') \n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "df2[\"preds_neutralized\"] = scaler.fit_transform(df2[[\"preds_neutralized\"]]) # transform back to 0-1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now our single feature exposures are much smaller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "corr_list2 = []\n",
    "for feature in features:\n",
    "    corr_list2.append(np.corrcoef(df2[feature], df2[\"preds_neutralized\"])[0,1])\n",
    "corr_series2 = pd.Series(corr_list2, index=features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1191.000000\n",
       "mean       -0.000598\n",
       "std         0.063819\n",
       "min        -0.306429\n",
       "25%        -0.044350\n",
       "50%         0.002995\n",
       "75%         0.043859\n",
       "max         0.170538\n",
       "dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "corr_series2.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Our overall score goes down, but the scores are more consistent than before. This leads to a higher sharpe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score for high feature exposure: 0.053302201866559894\n",
      "score for balanced feature expo: 0.04761935904746826\n",
      "std for high feature exposure: 0.024266557776271847\n",
      "std for balanced feature expo: 0.021660308401340596\n",
      "sharpe for high feature exposure: 2.196529164044827\n",
      "sharpe for balanced feature expo: 2.198461728482177\n",
      "\r"
     ]
    }
   ],
   "source": [
    "unbalanced_scores_per_era = df2.groupby(\"era\").apply(lambda d: np.corrcoef(d[\"preds\"], d[target])[0,1])\n",
    "balanced_scores_per_era = df2.groupby(\"era\").apply(lambda d: np.corrcoef(d[\"preds_neutralized\"], d[target])[0,1])\n",
    "\n",
    "print(f\"score for high feature exposure: {unbalanced_scores_per_era.mean()}\")\n",
    "print(f\"score for balanced feature expo: {balanced_scores_per_era.mean()}\")\n",
    "\n",
    "print(f\"std for high feature exposure: {unbalanced_scores_per_era.std(ddof=0)}\")\n",
    "print(f\"std for balanced feature expo: {balanced_scores_per_era.std(ddof=0)}\")\n",
    "\n",
    "print(f\"sharpe for high feature exposure: {unbalanced_scores_per_era.mean()/unbalanced_scores_per_era.std(ddof=0)}\")\n",
    "print(f\"sharpe for balanced feature expo: {balanced_scores_per_era.mean()/balanced_scores_per_era.std(ddof=0)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    67.000000\n",
       "mean      0.047619\n",
       "std       0.021824\n",
       "min      -0.000897\n",
       "25%       0.036598\n",
       "50%       0.046031\n",
       "75%       0.060867\n",
       "max       0.110289\n",
       "dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "balanced_scores_per_era.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    67.000000\n",
       "mean      0.053302\n",
       "std       0.024450\n",
       "min      -0.008029\n",
       "25%       0.039055\n",
       "50%       0.054649\n",
       "75%       0.066387\n",
       "max       0.127016\n",
       "dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "unbalanced_scores_per_era.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can also try to be more clever about what features we neutralize\n",
    "### The \"riskiest\" features are the features the predictions are most exposed to that also have the highest volatility "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# compute feature correlations with target on the first half data\n",
    "all_feature_corrs = df1.groupby('era').apply(lambda d: d[features].corrwith(d[target]))\n",
    "# compute the volatility of the feature correlations\n",
    "feature_corr_volatility = all_feature_corrs.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# calculate the feature exposures of the predictions\n",
    "feature_exposure_list = []\n",
    "for feature in features:\n",
    "    feature_exposure_list.append(np.corrcoef(df2[feature], lgb_preds)[0,1])\n",
    "feature_exposure_list = pd.Series(feature_exposure_list, index=features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# get list of 100 riskiest features\n",
    "riskiest_features = (feature_exposure_list.abs()*feature_corr_volatility).sort_values()[-100:].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0573\r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-105-a2ec8b9e6f35>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2[\"preds_neutralized_riskiest_100\"] = neutralize(df2,\n",
      "<ipython-input-105-a2ec8b9e6f35>:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2[\"preds_neutralized_riskiest_100\"] = scaler.fit_transform(df2[[\"preds_neutralized_riskiest_100\"]]) # transform back to 0-1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# 50% neutralize to the riskiest features\n",
    "df2[\"preds_neutralized_riskiest_100\"] = neutralize(df2, \n",
    "                                                  columns=[\"preds\"], \n",
    "                                                  neutralizers=riskiest_features, \n",
    "                                                  proportion=0.5, # 50% neutralize only those 100 features\n",
    "                                                  normalize=True, \n",
    "                                                  era_col='era')\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "df2[\"preds_neutralized_riskiest_100\"] = scaler.fit_transform(df2[[\"preds_neutralized_riskiest_100\"]]) # transform back to 0-1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The score goes down less and the sharpe goes up more than 50% neutralizing all the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score for high feature exposure: 0.053302201866559894\n",
      "score for balanced feature expo: 0.048994273179504645\n",
      "std for high feature exposure: 0.024266557776271847\n",
      "std for balanced feature expo: 0.02088815498811286\n",
      "sharpe for high feature exposure: 2.196529164044827\n",
      "sharpe for balanced feature expo: 2.3455529321467865\n",
      "\r"
     ]
    }
   ],
   "source": [
    "unbalanced_scores_per_era = df2.groupby(\"era\").apply(lambda d: np.corrcoef(d[\"preds\"], d[target])[0,1])\n",
    "balanced_scores_per_era = df2.groupby(\"era\").apply(lambda d: np.corrcoef(d[\"preds_neutralized_riskiest_100\"], d[target])[0,1])\n",
    "\n",
    "print(f\"score for high feature exposure: {unbalanced_scores_per_era.mean()}\")\n",
    "print(f\"score for balanced feature expo: {balanced_scores_per_era.mean()}\")\n",
    "\n",
    "print(f\"std for high feature exposure: {unbalanced_scores_per_era.std(ddof=0)}\")\n",
    "print(f\"std for balanced feature expo: {balanced_scores_per_era.std(ddof=0)}\")\n",
    "\n",
    "print(f\"sharpe for high feature exposure: {unbalanced_scores_per_era.mean()/unbalanced_scores_per_era.std(ddof=0)}\")\n",
    "print(f\"sharpe for balanced feature expo: {balanced_scores_per_era.mean()/balanced_scores_per_era.std(ddof=0)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interestingly, the maximal feature exposure magnitude is actually lower (~0.28 vs ~0.31) than when 50% neutralizing all features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1191.000000\n",
       "mean        0.006863\n",
       "std         0.060897\n",
       "min        -0.248102\n",
       "25%        -0.031736\n",
       "50%         0.009357\n",
       "75%         0.048210\n",
       "max         0.165298\n",
       "dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "corr_list3 = []\n",
    "for feature in features:\n",
    "    corr_list3.append(np.corrcoef(df2[feature], df2[\"preds_neutralized_riskiest_100\"])[0,1])\n",
    "corr_series3 = pd.Series(corr_list3, index=features)\n",
    "corr_series3.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1191.000000\n",
       "mean        0.006863\n",
       "std         0.060897\n",
       "min        -0.248102\n",
       "25%        -0.031736\n",
       "50%         0.009357\n",
       "75%         0.048210\n",
       "max         0.165298\n",
       "dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "corr_series3.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training on the alternative targets can result in models with different patterns of feature exposures\n",
    "## This is a great way to go after MMC and very useful for ensembling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(learning_rate=0.01, max_depth=5, n_estimators=1000)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "lgb = lightgbm.LGBMRegressor(n_jobs=-1, learning_rate=0.01, n_estimators=1000, max_depth=5)\n",
    "lgb.fit(df1[features], df1['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(learning_rate=0.01, max_depth=5, n_estimators=1000)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "lgb_jerome = lightgbm.LGBMRegressor(n_jobs=-1, learning_rate=0.01, n_estimators=1000, max_depth=5)\n",
    "lgb_jerome.fit(df1[features], df1['target_jerome_v4_20'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# The feature importances for each model are about 60% correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5950846349519291\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize = (16,6))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(lgb.feature_importances_, alpha=.5)\n",
    "plt.plot(lgb_jerome.feature_importances_, alpha=.5)\n",
    "plt.subplot(1,2,2)\n",
    "plt.scatter(lgb.feature_importances_, lgb_jerome.feature_importances_)\n",
    "\n",
    "print(np.corrcoef([lgb.feature_importances_, lgb_jerome.feature_importances_])[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "lgb_preds = lgb.predict(df2[features])\n",
    "lgb_jerome_preds = lgb_jerome.predict(df2[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6811530708761623"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# The predictions are about 68% correlated\n",
    "np.corrcoef([lgb_preds, lgb_jerome_preds])[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-114-abbdda99d361>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2[\"preds\"] = lgb_preds\n",
      "<ipython-input-114-abbdda99d361>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2[\"preds_jerome\"] = lgb_jerome_preds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "df2[\"preds\"] = lgb_preds\n",
    "df2[\"preds_jerome\"] = lgb_jerome_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0573\r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-115-867f2bde3bb9>:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2[f\"{preds}_neutralized_riskiest_100\"] = neutralize(df2,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0573"
     ]
    }
   ],
   "source": [
    "# 50% neutralize to riskest features for each model as above\n",
    "for preds in [\"preds\", \"preds_jerome\"]:\n",
    "    feature_exposure_list = []\n",
    "    for feature in features:\n",
    "        feature_exposure_list.append(np.corrcoef(df2[feature], df2[preds])[0,1])\n",
    "    feature_exposure_list = pd.Series(feature_exposure_list, index=features)\n",
    "\n",
    "    riskiest_features = (feature_exposure_list.abs()*feature_corr_volatility).sort_values()[-100:].index.tolist()\n",
    "    \n",
    "    df2[f\"{preds}_neutralized_riskiest_100\"] = neutralize(df2, \n",
    "                                                  columns=[f\"{preds}\"], \n",
    "                                                  neutralizers=riskiest_features, \n",
    "                                                  proportion=0.5, # 50% neutralize only those 100 features\n",
    "                                                  normalize=True, \n",
    "                                                  era_col='era')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-116-c7a741c556d2>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2[\"preds_ens\"] = (df2[\"preds\"] + df2[\"preds_jerome\"])/2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# create ensemble\n",
    "df2[\"preds_ens\"] = (df2[\"preds\"] + df2[\"preds_jerome\"])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score for target: 0.053302201866559894\n",
      "score for target_jerome: 0.04631473760685722\n",
      "\n",
      "sharpe for target: 2.196529164044827\n",
      "sharpe for target_jerome: 1.7773731363781036\n",
      "\n",
      "score for target after neutralization: 0.048994273179504645\n",
      "score for target_jerome after neutralization: 0.046103602161045906\n",
      "\n",
      "sharpe for target after neutralization: 2.3455529321467865\n",
      "sharpe for target_jerome after neutralization: 2.0660947986712968\n",
      "\n",
      "score for target + target_jerome ensemble: 0.0539668529140479\n",
      "sharpe for target + target_jerome ensemble: 2.0552035503075716\n",
      "\r"
     ]
    }
   ],
   "source": [
    "scores = df2.groupby(\"era\").apply(lambda d: np.corrcoef(d[\"preds\"], d[target])[0,1])\n",
    "scores_jerome = df2.groupby(\"era\").apply(lambda d: np.corrcoef(d[\"preds_jerome\"], d[target])[0,1])\n",
    "\n",
    "scores_derisked = df2.groupby(\"era\").apply(lambda d: np.corrcoef(d[\"preds_neutralized_riskiest_100\"], d[target])[0,1])\n",
    "scores_jerome_derisked = df2.groupby(\"era\").apply(lambda d: np.corrcoef(d[\"preds_jerome_neutralized_riskiest_100\"], d[target])[0,1])\n",
    "\n",
    "scores_ens = df2.groupby(\"era\").apply(lambda d: np.corrcoef(d[\"preds_ens\"], d[target])[0,1])\n",
    "\n",
    "print(f\"score for target: {scores.mean()}\")\n",
    "print(f\"score for target_jerome: {scores_jerome.mean()}\")\n",
    "print()\n",
    "print(f\"sharpe for target: {scores.mean()/scores.std(ddof=0)}\")\n",
    "print(f\"sharpe for target_jerome: {scores_jerome.mean()/scores_jerome.std(ddof=0)}\")\n",
    "print()\n",
    "print(f\"score for target after neutralization: {scores_derisked.mean()}\")\n",
    "print(f\"score for target_jerome after neutralization: {scores_jerome_derisked.mean()}\")\n",
    "print()\n",
    "print(f\"sharpe for target after neutralization: {scores_derisked.mean()/scores_derisked.std(ddof=0)}\")\n",
    "print(f\"sharpe for target_jerome after neutralization: {scores_jerome_derisked.mean()/scores_jerome_derisked.std(ddof=0)}\")\n",
    "print()\n",
    "print(f\"score for target + target_jerome ensemble: {scores_ens.mean()}\")\n",
    "print(f\"sharpe for target + target_jerome ensemble: {scores_ens.mean()/scores_ens.std(ddof=0)}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We see here that the model trained on `target_jerome` actually predicts `target` slightly better than the model trained on `target` (albeit with a lower Sharpe) both before and after neutralization of the riskiest features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>preds_neutralized_riskiest_100</th>\n",
       "      <th>preds_jerome_neutralized_riskiest_100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>preds_neutralized_riskiest_100</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.632838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>preds_jerome_neutralized_riskiest_100</th>\n",
       "      <td>0.632838</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                       preds_neutralized_riskiest_100  \\\n",
       "preds_neutralized_riskiest_100                               1.000000   \n",
       "preds_jerome_neutralized_riskiest_100                        0.632838   \n",
       "\n",
       "                                       preds_jerome_neutralized_riskiest_100  \n",
       "preds_neutralized_riskiest_100                                      0.632838  \n",
       "preds_jerome_neutralized_riskiest_100                               1.000000  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# calculate correlation of neutralized predictions\n",
    "df2[[f\"preds_neutralized_riskiest_100\", f\"preds_jerome_neutralized_riskiest_100\"]].corr()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Given that the neutralized version trained on `target_jerome` gets slightly better average score, but is only about 63% correlated with the neutralized version trained on `target`, it is an excellent candidate for MMC\n",
    "## However, ensembling the two neutralized predictions gives a model with the highest average score and a great Sharpe value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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