analysis_and_tips.ipynb

{
 "cells": [
  {
   "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, download_data\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": "stdout",
     "output_type": "stream",
     "text": [
      "✔ Downloading numerai_training_data.parquet⠙ Downloading numerai_training_data.parquet⠹ Downloading numerai_training_data.parquet⠸ Downloading numerai_training_data.parquet⠼ Downloading numerai_training_data.parquet Downloading numerai_training_data.parquet\n"
     ]
    },
    {
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       "<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_dichasial_hammier_spawner</th>\n",
       "      <th>feature_rheumy_epistemic_prancer</th>\n",
       "      <th>feature_pert_performative_hormuz</th>\n",
       "      <th>feature_hillier_unpitied_theobromine</th>\n",
       "      <th>feature_perigean_bewitching_thruster</th>\n",
       "      <th>feature_renegade_undomestic_milord</th>\n",
       "      <th>feature_koranic_rude_corf</th>\n",
       "      <th>feature_demisable_expiring_millepede</th>\n",
       "      <th>...</th>\n",
       "      <th>target_paul_20</th>\n",
       "      <th>target_paul_60</th>\n",
       "      <th>target_george_20</th>\n",
       "      <th>target_george_60</th>\n",
       "      <th>target_william_20</th>\n",
       "      <th>target_william_60</th>\n",
       "      <th>target_arthur_20</th>\n",
       "      <th>target_arthur_60</th>\n",
       "      <th>target_thomas_20</th>\n",
       "      <th>target_thomas_60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
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       "      <th>n003bba8a98662e4</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.25</td>\n",
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       "      <td>0.166667</td>\n",
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       "    <tr>\n",
       "      <th>n003bee128c2fcfc</th>\n",
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       "      <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>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.833333</td>\n",
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       "      <td>0.666667</td>\n",
       "      <td>0.833333</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>n0048ac83aff7194</th>\n",
       "      <td>0001</td>\n",
       "      <td>train</td>\n",
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       "      <td>0.75</td>\n",
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       "      <td>0.500000</td>\n",
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       "    <tr>\n",
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       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
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       "      <td>1.00</td>\n",
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       "      <td>1.00</td>\n",
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       "    <tr>\n",
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      "text/plain": [
       "                   era data_type  feature_dichasial_hammier_spawner  \\\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_rheumy_epistemic_prancer  \\\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_pert_performative_hormuz  \\\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_hillier_unpitied_theobromine  \\\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_perigean_bewitching_thruster  \\\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_renegade_undomestic_milord  \\\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_koranic_rude_corf  \\\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_demisable_expiring_millepede  ...  target_paul_20  \\\n",
       "id                                                      ...                   \n",
       "n003bba8a98662e4                                  1.00  ...            0.25   \n",
       "n003bee128c2fcfc                                  0.75  ...            1.00   \n",
       "n0048ac83aff7194                                  0.75  ...            0.50   \n",
       "n00691bec80d3e02                                  1.00  ...            0.50   \n",
       "n00b8720a2fdc4f2                                  0.50  ...            0.50   \n",
       "\n",
       "                  target_paul_60  target_george_20  target_george_60  \\\n",
       "id                                                                     \n",
       "n003bba8a98662e4            0.25              0.25              0.00   \n",
       "n003bee128c2fcfc            1.00              1.00              1.00   \n",
       "n0048ac83aff7194            0.25              0.25              0.25   \n",
       "n00691bec80d3e02            0.50              0.50              0.50   \n",
       "n00b8720a2fdc4f2            0.50              0.50              0.50   \n",
       "\n",
       "                  target_william_20  target_william_60  target_arthur_20  \\\n",
       "id                                                                         \n",
       "n003bba8a98662e4           0.166667           0.000000          0.166667   \n",
       "n003bee128c2fcfc           0.833333           0.666667          0.833333   \n",
       "n0048ac83aff7194           0.500000           0.333333          0.500000   \n",
       "n00691bec80d3e02           0.666667           0.500000          0.500000   \n",
       "n00b8720a2fdc4f2           0.666667           0.500000          0.500000   \n",
       "\n",
       "                  target_arthur_60  target_thomas_20  target_thomas_60  \n",
       "id                                                                      \n",
       "n003bba8a98662e4          0.000000          0.166667          0.000000  \n",
       "n003bee128c2fcfc          0.666667          0.833333          0.666667  \n",
       "n0048ac83aff7194          0.333333          0.500000          0.333333  \n",
       "n00691bec80d3e02          0.500000          0.666667          0.500000  \n",
       "n00b8720a2fdc4f2          0.500000          0.666667          0.500000  \n",
       "\n",
       "[5 rows x 1073 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(tournament=8)  # tournament 8 is the primary Numerai Tournament\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(\"numerai_training_data.parquet\", \"numerai_training_data.parquet\")\n",
    "df = pd.read_parquet('numerai_training_data.parquet')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2412105, 1073)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# There are 2412105 rows grouped into 574 weekly eras\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1050\n",
      "['feature_dichasial_hammier_spawner', 'feature_rheumy_epistemic_prancer', 'feature_pert_performative_hormuz', 'feature_hillier_unpitied_theobromine', 'feature_perigean_bewitching_thruster']\n",
      "\r"
     ]
    }
   ],
   "source": [
    "# There's 1050 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])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# There are now 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": 6,
   "metadata": {},
   "outputs": [
    {
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       "</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_20</th>\n",
       "      <th>target_nomi_60</th>\n",
       "      <th>target_jerome_20</th>\n",
       "      <th>target_jerome_60</th>\n",
       "      <th>target_janet_20</th>\n",
       "      <th>target_janet_60</th>\n",
       "      <th>target_ben_20</th>\n",
       "      <th>target_ben_60</th>\n",
       "      <th>target_alan_20</th>\n",
       "      <th>...</th>\n",
       "      <th>target_paul_20</th>\n",
       "      <th>target_paul_60</th>\n",
       "      <th>target_george_20</th>\n",
       "      <th>target_george_60</th>\n",
       "      <th>target_william_20</th>\n",
       "      <th>target_william_60</th>\n",
       "      <th>target_arthur_20</th>\n",
       "      <th>target_arthur_60</th>\n",
       "      <th>target_thomas_20</th>\n",
       "      <th>target_thomas_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>0.503147</td>\n",
       "      <td>0.769365</td>\n",
       "      <td>0.455448</td>\n",
       "      <td>0.659012</td>\n",
       "      <td>0.392255</td>\n",
       "      <td>0.819540</td>\n",
       "      <td>0.455063</td>\n",
       "      <td>0.678464</td>\n",
       "      <td>...</td>\n",
       "      <td>0.751827</td>\n",
       "      <td>0.441127</td>\n",
       "      <td>0.722665</td>\n",
       "      <td>0.401227</td>\n",
       "      <td>0.819724</td>\n",
       "      <td>0.484425</td>\n",
       "      <td>0.829850</td>\n",
       "      <td>0.490358</td>\n",
       "      <td>0.880602</td>\n",
       "      <td>0.486349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_nomi_20</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.503147</td>\n",
       "      <td>0.769365</td>\n",
       "      <td>0.455448</td>\n",
       "      <td>0.659012</td>\n",
       "      <td>0.392255</td>\n",
       "      <td>0.819540</td>\n",
       "      <td>0.455063</td>\n",
       "      <td>0.678464</td>\n",
       "      <td>...</td>\n",
       "      <td>0.751827</td>\n",
       "      <td>0.441127</td>\n",
       "      <td>0.722665</td>\n",
       "      <td>0.401227</td>\n",
       "      <td>0.819724</td>\n",
       "      <td>0.484425</td>\n",
       "      <td>0.829850</td>\n",
       "      <td>0.490358</td>\n",
       "      <td>0.880602</td>\n",
       "      <td>0.486349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_nomi_60</th>\n",
       "      <td>0.503147</td>\n",
       "      <td>0.503147</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.433388</td>\n",
       "      <td>0.817732</td>\n",
       "      <td>0.373979</td>\n",
       "      <td>0.689912</td>\n",
       "      <td>0.456754</td>\n",
       "      <td>0.813261</td>\n",
       "      <td>0.384636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.423820</td>\n",
       "      <td>0.791360</td>\n",
       "      <td>0.409423</td>\n",
       "      <td>0.705469</td>\n",
       "      <td>0.464266</td>\n",
       "      <td>0.873005</td>\n",
       "      <td>0.468459</td>\n",
       "      <td>0.879774</td>\n",
       "      <td>0.492558</td>\n",
       "      <td>0.877989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_jerome_20</th>\n",
       "      <td>0.769365</td>\n",
       "      <td>0.769365</td>\n",
       "      <td>0.433388</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.463756</td>\n",
       "      <td>0.698353</td>\n",
       "      <td>0.382269</td>\n",
       "      <td>0.768737</td>\n",
       "      <td>0.426895</td>\n",
       "      <td>0.628114</td>\n",
       "      <td>...</td>\n",
       "      <td>0.905954</td>\n",
       "      <td>0.446691</td>\n",
       "      <td>0.689335</td>\n",
       "      <td>0.379908</td>\n",
       "      <td>0.865786</td>\n",
       "      <td>0.453824</td>\n",
       "      <td>0.871040</td>\n",
       "      <td>0.457756</td>\n",
       "      <td>0.748965</td>\n",
       "      <td>0.421251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_jerome_60</th>\n",
       "      <td>0.455448</td>\n",
       "      <td>0.455448</td>\n",
       "      <td>0.817732</td>\n",
       "      <td>0.463756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.382460</td>\n",
       "      <td>0.693168</td>\n",
       "      <td>0.450810</td>\n",
       "      <td>0.821748</td>\n",
       "      <td>0.372656</td>\n",
       "      <td>...</td>\n",
       "      <td>0.451384</td>\n",
       "      <td>0.884963</td>\n",
       "      <td>0.408582</td>\n",
       "      <td>0.719172</td>\n",
       "      <td>0.457941</td>\n",
       "      <td>0.862257</td>\n",
       "      <td>0.459407</td>\n",
       "      <td>0.865918</td>\n",
       "      <td>0.447718</td>\n",
       "      <td>0.789250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_janet_20</th>\n",
       "      <td>0.659012</td>\n",
       "      <td>0.659012</td>\n",
       "      <td>0.373979</td>\n",
       "      <td>0.698353</td>\n",
       "      <td>0.382460</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.422820</td>\n",
       "      <td>0.641273</td>\n",
       "      <td>0.358757</td>\n",
       "      <td>0.718364</td>\n",
       "      <td>...</td>\n",
       "      <td>0.703169</td>\n",
       "      <td>0.370593</td>\n",
       "      <td>0.576267</td>\n",
       "      <td>0.318308</td>\n",
       "      <td>0.724738</td>\n",
       "      <td>0.385911</td>\n",
       "      <td>0.747625</td>\n",
       "      <td>0.395341</td>\n",
       "      <td>0.648114</td>\n",
       "      <td>0.364063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_janet_60</th>\n",
       "      <td>0.392255</td>\n",
       "      <td>0.392255</td>\n",
       "      <td>0.689912</td>\n",
       "      <td>0.382269</td>\n",
       "      <td>0.693168</td>\n",
       "      <td>0.422820</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.378579</td>\n",
       "      <td>0.672070</td>\n",
       "      <td>0.414573</td>\n",
       "      <td>...</td>\n",
       "      <td>0.374259</td>\n",
       "      <td>0.695526</td>\n",
       "      <td>0.341069</td>\n",
       "      <td>0.596787</td>\n",
       "      <td>0.388464</td>\n",
       "      <td>0.720954</td>\n",
       "      <td>0.395379</td>\n",
       "      <td>0.742915</td>\n",
       "      <td>0.386067</td>\n",
       "      <td>0.677794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_ben_20</th>\n",
       "      <td>0.819540</td>\n",
       "      <td>0.819540</td>\n",
       "      <td>0.456754</td>\n",
       "      <td>0.768737</td>\n",
       "      <td>0.450810</td>\n",
       "      <td>0.641273</td>\n",
       "      <td>0.378579</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.485953</td>\n",
       "      <td>0.655087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.745765</td>\n",
       "      <td>0.433137</td>\n",
       "      <td>0.757761</td>\n",
       "      <td>0.418228</td>\n",
       "      <td>0.746513</td>\n",
       "      <td>0.445281</td>\n",
       "      <td>0.755789</td>\n",
       "      <td>0.449744</td>\n",
       "      <td>0.872847</td>\n",
       "      <td>0.476992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_ben_60</th>\n",
       "      <td>0.455063</td>\n",
       "      <td>0.455063</td>\n",
       "      <td>0.813261</td>\n",
       "      <td>0.426895</td>\n",
       "      <td>0.821748</td>\n",
       "      <td>0.358757</td>\n",
       "      <td>0.672070</td>\n",
       "      <td>0.485953</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.373185</td>\n",
       "      <td>...</td>\n",
       "      <td>0.416077</td>\n",
       "      <td>0.786051</td>\n",
       "      <td>0.425653</td>\n",
       "      <td>0.747294</td>\n",
       "      <td>0.424105</td>\n",
       "      <td>0.787089</td>\n",
       "      <td>0.427728</td>\n",
       "      <td>0.795083</td>\n",
       "      <td>0.480622</td>\n",
       "      <td>0.867900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_alan_20</th>\n",
       "      <td>0.678464</td>\n",
       "      <td>0.678464</td>\n",
       "      <td>0.384636</td>\n",
       "      <td>0.628114</td>\n",
       "      <td>0.372656</td>\n",
       "      <td>0.718364</td>\n",
       "      <td>0.414573</td>\n",
       "      <td>0.655087</td>\n",
       "      <td>0.373185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.614438</td>\n",
       "      <td>0.361734</td>\n",
       "      <td>0.581200</td>\n",
       "      <td>0.328936</td>\n",
       "      <td>0.642762</td>\n",
       "      <td>0.381904</td>\n",
       "      <td>0.661255</td>\n",
       "      <td>0.392130</td>\n",
       "      <td>0.702886</td>\n",
       "      <td>0.385541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_alan_60</th>\n",
       "      <td>0.382948</td>\n",
       "      <td>0.382948</td>\n",
       "      <td>0.673598</td>\n",
       "      <td>0.353125</td>\n",
       "      <td>0.659323</td>\n",
       "      <td>0.392251</td>\n",
       "      <td>0.769956</td>\n",
       "      <td>0.373347</td>\n",
       "      <td>0.646430</td>\n",
       "      <td>0.419483</td>\n",
       "      <td>...</td>\n",
       "      <td>0.346406</td>\n",
       "      <td>0.640502</td>\n",
       "      <td>0.335088</td>\n",
       "      <td>0.568619</td>\n",
       "      <td>0.363717</td>\n",
       "      <td>0.677427</td>\n",
       "      <td>0.372271</td>\n",
       "      <td>0.696971</td>\n",
       "      <td>0.388124</td>\n",
       "      <td>0.695771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_paul_20</th>\n",
       "      <td>0.751827</td>\n",
       "      <td>0.751827</td>\n",
       "      <td>0.423820</td>\n",
       "      <td>0.905954</td>\n",
       "      <td>0.451384</td>\n",
       "      <td>0.703169</td>\n",
       "      <td>0.374259</td>\n",
       "      <td>0.745765</td>\n",
       "      <td>0.416077</td>\n",
       "      <td>0.614438</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.473859</td>\n",
       "      <td>0.696137</td>\n",
       "      <td>0.399401</td>\n",
       "      <td>0.847479</td>\n",
       "      <td>0.445068</td>\n",
       "      <td>0.840886</td>\n",
       "      <td>0.447869</td>\n",
       "      <td>0.730521</td>\n",
       "      <td>0.412473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_paul_60</th>\n",
       "      <td>0.441127</td>\n",
       "      <td>0.441127</td>\n",
       "      <td>0.791360</td>\n",
       "      <td>0.446691</td>\n",
       "      <td>0.884963</td>\n",
       "      <td>0.370593</td>\n",
       "      <td>0.695526</td>\n",
       "      <td>0.433137</td>\n",
       "      <td>0.786051</td>\n",
       "      <td>0.361734</td>\n",
       "      <td>...</td>\n",
       "      <td>0.473859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.417146</td>\n",
       "      <td>0.741728</td>\n",
       "      <td>0.444607</td>\n",
       "      <td>0.835873</td>\n",
       "      <td>0.444850</td>\n",
       "      <td>0.826746</td>\n",
       "      <td>0.432580</td>\n",
       "      <td>0.763650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_george_20</th>\n",
       "      <td>0.722665</td>\n",
       "      <td>0.722665</td>\n",
       "      <td>0.409423</td>\n",
       "      <td>0.689335</td>\n",
       "      <td>0.408582</td>\n",
       "      <td>0.576267</td>\n",
       "      <td>0.341069</td>\n",
       "      <td>0.757761</td>\n",
       "      <td>0.425653</td>\n",
       "      <td>0.581200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.696137</td>\n",
       "      <td>0.417146</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.482188</td>\n",
       "      <td>0.676223</td>\n",
       "      <td>0.406019</td>\n",
       "      <td>0.683647</td>\n",
       "      <td>0.409641</td>\n",
       "      <td>0.731028</td>\n",
       "      <td>0.417317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_george_60</th>\n",
       "      <td>0.401227</td>\n",
       "      <td>0.401227</td>\n",
       "      <td>0.705469</td>\n",
       "      <td>0.379908</td>\n",
       "      <td>0.719172</td>\n",
       "      <td>0.318308</td>\n",
       "      <td>0.596787</td>\n",
       "      <td>0.418228</td>\n",
       "      <td>0.747294</td>\n",
       "      <td>0.328936</td>\n",
       "      <td>...</td>\n",
       "      <td>0.399401</td>\n",
       "      <td>0.741728</td>\n",
       "      <td>0.482188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.380864</td>\n",
       "      <td>0.701942</td>\n",
       "      <td>0.382972</td>\n",
       "      <td>0.707987</td>\n",
       "      <td>0.413474</td>\n",
       "      <td>0.718592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_william_20</th>\n",
       "      <td>0.819724</td>\n",
       "      <td>0.819724</td>\n",
       "      <td>0.464266</td>\n",
       "      <td>0.865786</td>\n",
       "      <td>0.457941</td>\n",
       "      <td>0.724738</td>\n",
       "      <td>0.388464</td>\n",
       "      <td>0.746513</td>\n",
       "      <td>0.424105</td>\n",
       "      <td>0.642762</td>\n",
       "      <td>...</td>\n",
       "      <td>0.847479</td>\n",
       "      <td>0.444607</td>\n",
       "      <td>0.676223</td>\n",
       "      <td>0.380864</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.497816</td>\n",
       "      <td>0.934503</td>\n",
       "      <td>0.490892</td>\n",
       "      <td>0.801328</td>\n",
       "      <td>0.452694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_william_60</th>\n",
       "      <td>0.484425</td>\n",
       "      <td>0.484425</td>\n",
       "      <td>0.873005</td>\n",
       "      <td>0.453824</td>\n",
       "      <td>0.862257</td>\n",
       "      <td>0.385911</td>\n",
       "      <td>0.720954</td>\n",
       "      <td>0.445281</td>\n",
       "      <td>0.787089</td>\n",
       "      <td>0.381904</td>\n",
       "      <td>...</td>\n",
       "      <td>0.445068</td>\n",
       "      <td>0.835873</td>\n",
       "      <td>0.406019</td>\n",
       "      <td>0.701942</td>\n",
       "      <td>0.497816</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.488486</td>\n",
       "      <td>0.934996</td>\n",
       "      <td>0.479017</td>\n",
       "      <td>0.848806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_arthur_20</th>\n",
       "      <td>0.829850</td>\n",
       "      <td>0.829850</td>\n",
       "      <td>0.468459</td>\n",
       "      <td>0.871040</td>\n",
       "      <td>0.459407</td>\n",
       "      <td>0.747625</td>\n",
       "      <td>0.395379</td>\n",
       "      <td>0.755789</td>\n",
       "      <td>0.427728</td>\n",
       "      <td>0.661255</td>\n",
       "      <td>...</td>\n",
       "      <td>0.840886</td>\n",
       "      <td>0.444850</td>\n",
       "      <td>0.683647</td>\n",
       "      <td>0.382972</td>\n",
       "      <td>0.934503</td>\n",
       "      <td>0.488486</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.496131</td>\n",
       "      <td>0.816706</td>\n",
       "      <td>0.459133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_arthur_60</th>\n",
       "      <td>0.490358</td>\n",
       "      <td>0.490358</td>\n",
       "      <td>0.879774</td>\n",
       "      <td>0.457756</td>\n",
       "      <td>0.865918</td>\n",
       "      <td>0.395341</td>\n",
       "      <td>0.742915</td>\n",
       "      <td>0.449744</td>\n",
       "      <td>0.795083</td>\n",
       "      <td>0.392130</td>\n",
       "      <td>...</td>\n",
       "      <td>0.447869</td>\n",
       "      <td>0.826746</td>\n",
       "      <td>0.409641</td>\n",
       "      <td>0.707987</td>\n",
       "      <td>0.490892</td>\n",
       "      <td>0.934996</td>\n",
       "      <td>0.496131</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.486803</td>\n",
       "      <td>0.863319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_thomas_20</th>\n",
       "      <td>0.880602</td>\n",
       "      <td>0.880602</td>\n",
       "      <td>0.492558</td>\n",
       "      <td>0.748965</td>\n",
       "      <td>0.447718</td>\n",
       "      <td>0.648114</td>\n",
       "      <td>0.386067</td>\n",
       "      <td>0.872847</td>\n",
       "      <td>0.480622</td>\n",
       "      <td>0.702886</td>\n",
       "      <td>...</td>\n",
       "      <td>0.730521</td>\n",
       "      <td>0.432580</td>\n",
       "      <td>0.731028</td>\n",
       "      <td>0.413474</td>\n",
       "      <td>0.801328</td>\n",
       "      <td>0.479017</td>\n",
       "      <td>0.816706</td>\n",
       "      <td>0.486803</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.519180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_thomas_60</th>\n",
       "      <td>0.486349</td>\n",
       "      <td>0.486349</td>\n",
       "      <td>0.877989</td>\n",
       "      <td>0.421251</td>\n",
       "      <td>0.789250</td>\n",
       "      <td>0.364063</td>\n",
       "      <td>0.677794</td>\n",
       "      <td>0.476992</td>\n",
       "      <td>0.867900</td>\n",
       "      <td>0.385541</td>\n",
       "      <td>...</td>\n",
       "      <td>0.412473</td>\n",
       "      <td>0.763650</td>\n",
       "      <td>0.417317</td>\n",
       "      <td>0.718592</td>\n",
       "      <td>0.452694</td>\n",
       "      <td>0.848806</td>\n",
       "      <td>0.459133</td>\n",
       "      <td>0.863319</td>\n",
       "      <td>0.519180</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_20  target_nomi_60  target_jerome_20  \\\n",
       "target             1.000000        1.000000        0.503147          0.769365   \n",
       "target_nomi_20     1.000000        1.000000        0.503147          0.769365   \n",
       "target_nomi_60     0.503147        0.503147        1.000000          0.433388   \n",
       "target_jerome_20   0.769365        0.769365        0.433388          1.000000   \n",
       "target_jerome_60   0.455448        0.455448        0.817732          0.463756   \n",
       "target_janet_20    0.659012        0.659012        0.373979          0.698353   \n",
       "target_janet_60    0.392255        0.392255        0.689912          0.382269   \n",
       "target_ben_20      0.819540        0.819540        0.456754          0.768737   \n",
       "target_ben_60      0.455063        0.455063        0.813261          0.426895   \n",
       "target_alan_20     0.678464        0.678464        0.384636          0.628114   \n",
       "target_alan_60     0.382948        0.382948        0.673598          0.353125   \n",
       "target_paul_20     0.751827        0.751827        0.423820          0.905954   \n",
       "target_paul_60     0.441127        0.441127        0.791360          0.446691   \n",
       "target_george_20   0.722665        0.722665        0.409423          0.689335   \n",
       "target_george_60   0.401227        0.401227        0.705469          0.379908   \n",
       "target_william_20  0.819724        0.819724        0.464266          0.865786   \n",
       "target_william_60  0.484425        0.484425        0.873005          0.453824   \n",
       "target_arthur_20   0.829850        0.829850        0.468459          0.871040   \n",
       "target_arthur_60   0.490358        0.490358        0.879774          0.457756   \n",
       "target_thomas_20   0.880602        0.880602        0.492558          0.748965   \n",
       "target_thomas_60   0.486349        0.486349        0.877989          0.421251   \n",
       "\n",
       "                   target_jerome_60  target_janet_20  target_janet_60  \\\n",
       "target                     0.455448         0.659012         0.392255   \n",
       "target_nomi_20             0.455448         0.659012         0.392255   \n",
       "target_nomi_60             0.817732         0.373979         0.689912   \n",
       "target_jerome_20           0.463756         0.698353         0.382269   \n",
       "target_jerome_60           1.000000         0.382460         0.693168   \n",
       "target_janet_20            0.382460         1.000000         0.422820   \n",
       "target_janet_60            0.693168         0.422820         1.000000   \n",
       "target_ben_20              0.450810         0.641273         0.378579   \n",
       "target_ben_60              0.821748         0.358757         0.672070   \n",
       "target_alan_20             0.372656         0.718364         0.414573   \n",
       "target_alan_60             0.659323         0.392251         0.769956   \n",
       "target_paul_20             0.451384         0.703169         0.374259   \n",
       "target_paul_60             0.884963         0.370593         0.695526   \n",
       "target_george_20           0.408582         0.576267         0.341069   \n",
       "target_george_60           0.719172         0.318308         0.596787   \n",
       "target_william_20          0.457941         0.724738         0.388464   \n",
       "target_william_60          0.862257         0.385911         0.720954   \n",
       "target_arthur_20           0.459407         0.747625         0.395379   \n",
       "target_arthur_60           0.865918         0.395341         0.742915   \n",
       "target_thomas_20           0.447718         0.648114         0.386067   \n",
       "target_thomas_60           0.789250         0.364063         0.677794   \n",
       "\n",
       "                   target_ben_20  target_ben_60  target_alan_20  ...  \\\n",
       "target                  0.819540       0.455063        0.678464  ...   \n",
       "target_nomi_20          0.819540       0.455063        0.678464  ...   \n",
       "target_nomi_60          0.456754       0.813261        0.384636  ...   \n",
       "target_jerome_20        0.768737       0.426895        0.628114  ...   \n",
       "target_jerome_60        0.450810       0.821748        0.372656  ...   \n",
       "target_janet_20         0.641273       0.358757        0.718364  ...   \n",
       "target_janet_60         0.378579       0.672070        0.414573  ...   \n",
       "target_ben_20           1.000000       0.485953        0.655087  ...   \n",
       "target_ben_60           0.485953       1.000000        0.373185  ...   \n",
       "target_alan_20          0.655087       0.373185        1.000000  ...   \n",
       "target_alan_60          0.373347       0.646430        0.419483  ...   \n",
       "target_paul_20          0.745765       0.416077        0.614438  ...   \n",
       "target_paul_60          0.433137       0.786051        0.361734  ...   \n",
       "target_george_20        0.757761       0.425653        0.581200  ...   \n",
       "target_george_60        0.418228       0.747294        0.328936  ...   \n",
       "target_william_20       0.746513       0.424105        0.642762  ...   \n",
       "target_william_60       0.445281       0.787089        0.381904  ...   \n",
       "target_arthur_20        0.755789       0.427728        0.661255  ...   \n",
       "target_arthur_60        0.449744       0.795083        0.392130  ...   \n",
       "target_thomas_20        0.872847       0.480622        0.702886  ...   \n",
       "target_thomas_60        0.476992       0.867900        0.385541  ...   \n",
       "\n",
       "                   target_paul_20  target_paul_60  target_george_20  \\\n",
       "target                   0.751827        0.441127          0.722665   \n",
       "target_nomi_20           0.751827        0.441127          0.722665   \n",
       "target_nomi_60           0.423820        0.791360          0.409423   \n",
       "target_jerome_20         0.905954        0.446691          0.689335   \n",
       "target_jerome_60         0.451384        0.884963          0.408582   \n",
       "target_janet_20          0.703169        0.370593          0.576267   \n",
       "target_janet_60          0.374259        0.695526          0.341069   \n",
       "target_ben_20            0.745765        0.433137          0.757761   \n",
       "target_ben_60            0.416077        0.786051          0.425653   \n",
       "target_alan_20           0.614438        0.361734          0.581200   \n",
       "target_alan_60           0.346406        0.640502          0.335088   \n",
       "target_paul_20           1.000000        0.473859          0.696137   \n",
       "target_paul_60           0.473859        1.000000          0.417146   \n",
       "target_george_20         0.696137        0.417146          1.000000   \n",
       "target_george_60         0.399401        0.741728          0.482188   \n",
       "target_william_20        0.847479        0.444607          0.676223   \n",
       "target_william_60        0.445068        0.835873          0.406019   \n",
       "target_arthur_20         0.840886        0.444850          0.683647   \n",
       "target_arthur_60         0.447869        0.826746          0.409641   \n",
       "target_thomas_20         0.730521        0.432580          0.731028   \n",
       "target_thomas_60         0.412473        0.763650          0.417317   \n",
       "\n",
       "                   target_george_60  target_william_20  target_william_60  \\\n",
       "target                     0.401227           0.819724           0.484425   \n",
       "target_nomi_20             0.401227           0.819724           0.484425   \n",
       "target_nomi_60             0.705469           0.464266           0.873005   \n",
       "target_jerome_20           0.379908           0.865786           0.453824   \n",
       "target_jerome_60           0.719172           0.457941           0.862257   \n",
       "target_janet_20            0.318308           0.724738           0.385911   \n",
       "target_janet_60            0.596787           0.388464           0.720954   \n",
       "target_ben_20              0.418228           0.746513           0.445281   \n",
       "target_ben_60              0.747294           0.424105           0.787089   \n",
       "target_alan_20             0.328936           0.642762           0.381904   \n",
       "target_alan_60             0.568619           0.363717           0.677427   \n",
       "target_paul_20             0.399401           0.847479           0.445068   \n",
       "target_paul_60             0.741728           0.444607           0.835873   \n",
       "target_george_20           0.482188           0.676223           0.406019   \n",
       "target_george_60           1.000000           0.380864           0.701942   \n",
       "target_william_20          0.380864           1.000000           0.497816   \n",
       "target_william_60          0.701942           0.497816           1.000000   \n",
       "target_arthur_20           0.382972           0.934503           0.488486   \n",
       "target_arthur_60           0.707987           0.490892           0.934996   \n",
       "target_thomas_20           0.413474           0.801328           0.479017   \n",
       "target_thomas_60           0.718592           0.452694           0.848806   \n",
       "\n",
       "                   target_arthur_20  target_arthur_60  target_thomas_20  \\\n",
       "target                     0.829850          0.490358          0.880602   \n",
       "target_nomi_20             0.829850          0.490358          0.880602   \n",
       "target_nomi_60             0.468459          0.879774          0.492558   \n",
       "target_jerome_20           0.871040          0.457756          0.748965   \n",
       "target_jerome_60           0.459407          0.865918          0.447718   \n",
       "target_janet_20            0.747625          0.395341          0.648114   \n",
       "target_janet_60            0.395379          0.742915          0.386067   \n",
       "target_ben_20              0.755789          0.449744          0.872847   \n",
       "target_ben_60              0.427728          0.795083          0.480622   \n",
       "target_alan_20             0.661255          0.392130          0.702886   \n",
       "target_alan_60             0.372271          0.696971          0.388124   \n",
       "target_paul_20             0.840886          0.447869          0.730521   \n",
       "target_paul_60             0.444850          0.826746          0.432580   \n",
       "target_george_20           0.683647          0.409641          0.731028   \n",
       "target_george_60           0.382972          0.707987          0.413474   \n",
       "target_william_20          0.934503          0.490892          0.801328   \n",
       "target_william_60          0.488486          0.934996          0.479017   \n",
       "target_arthur_20           1.000000          0.496131          0.816706   \n",
       "target_arthur_60           0.496131          1.000000          0.486803   \n",
       "target_thomas_20           0.816706          0.486803          1.000000   \n",
       "target_thomas_60           0.459133          0.863319          0.519180   \n",
       "\n",
       "                   target_thomas_60  \n",
       "target                     0.486349  \n",
       "target_nomi_20             0.486349  \n",
       "target_nomi_60             0.877989  \n",
       "target_jerome_20           0.421251  \n",
       "target_jerome_60           0.789250  \n",
       "target_janet_20            0.364063  \n",
       "target_janet_60            0.677794  \n",
       "target_ben_20              0.476992  \n",
       "target_ben_60              0.867900  \n",
       "target_alan_20             0.385541  \n",
       "target_alan_60             0.695771  \n",
       "target_paul_20             0.412473  \n",
       "target_paul_60             0.763650  \n",
       "target_george_20           0.417317  \n",
       "target_george_60           0.718592  \n",
       "target_william_20          0.452694  \n",
       "target_william_60          0.848806  \n",
       "target_arthur_20           0.459133  \n",
       "target_arthur_60           0.863319  \n",
       "target_thomas_20           0.519180  \n",
       "target_thomas_60           1.000000  \n",
       "\n",
       "[21 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1050 features, no feature groups!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2f446a54f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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": 8,
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.412105e+06\n",
       "mean     3.043023e+02\n",
       "std      1.598982e+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": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='erano'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0.00    0.050003\n",
       "0.25    0.199996\n",
       "0.50    0.499993\n",
       "0.75    0.200015\n",
       "1.00    0.049992\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target_william_20\n",
      "0.000000    0.029735\n",
      "0.166667    0.059873\n",
      "0.333333    0.211150\n",
      "0.500000    0.391798\n",
      "0.666667    0.203550\n",
      "0.833333    0.065247\n",
      "1.000000    0.038511\n",
      "dtype: float64\n",
      "\n",
      "target_arthur_20\n",
      "0.000000    0.028859\n",
      "0.166667    0.073267\n",
      "0.333333    0.258668\n",
      "0.500000    0.384931\n",
      "0.666667    0.173287\n",
      "0.833333    0.052486\n",
      "1.000000    0.028367\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_20').size()/len(df))\n",
    "print()\n",
    "print(df.groupby('target_arthur_20').size()/len(df))"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "feature_corrs = df[df.era=='0001'][features].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "feature_dichasial_hammier_spawner  feature_dichasial_hammier_spawner       1.000000\n",
       "                                   feature_rheumy_epistemic_prancer        0.001690\n",
       "                                   feature_pert_performative_hormuz        0.141253\n",
       "                                   feature_hillier_unpitied_theobromine    0.170891\n",
       "                                   feature_perigean_bewitching_thruster   -0.151098\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "feature_corrs.stack().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "feature_calycled_living_birmingham            feature_dentilingual_removed_osmometer             -0.947381\n",
       "feature_criticisable_authentical_deprecation  feature_invalid_extortionary_titillation           -0.927235\n",
       "feature_hierologic_assault_candida            feature_multicostate_undescended_monoacid          -0.926623\n",
       "feature_apogamic_obeisant_pondicherry         feature_splitting_sexagesimal_teutonisation        -0.923430\n",
       "feature_earthlier_parian_pistachio            feature_grandmotherly_circumnavigable_homonymity   -0.913526\n",
       "                                                                                                    ...   \n",
       "feature_escutcheoned_timocratic_kotwal        feature_horizontal_snug_description                 1.000000\n",
       "                                              feature_neptunian_supersensitive_stapler            1.000000\n",
       "                                              feature_interoceptive_fifteenth_trey                1.000000\n",
       "                                              feature_savory_engrained_undervest                  1.000000\n",
       "feature_brittle_chautauqua_elite              feature_steepled_transistorized_shelter             1.000000\n",
       "Length: 550725, dtype: float64"
      ]
     },
     "execution_count": 15,
     "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()"
   ]
  },
  {
   "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": 16,
   "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": 17,
   "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": 18,
   "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": 19,
   "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_brushed_psychiatrical_lubbock</th>\n",
       "      <th>feature_savory_engrained_undervest</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.159888</td>\n",
       "      <td>-1.159888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_interpenetrative_enabling_elm</th>\n",
       "      <th>feature_savory_engrained_undervest</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.159262</td>\n",
       "      <td>-1.159262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_horizontal_snug_description</th>\n",
       "      <th>feature_savory_engrained_undervest</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.158691</td>\n",
       "      <td>-1.158691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_abstersive_emotional_misinterpreter</th>\n",
       "      <th>feature_horizontal_snug_description</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.156998</td>\n",
       "      <td>-1.156998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_opposable_argumentative_mesoblast</th>\n",
       "      <th>feature_savory_engrained_undervest</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.156654</td>\n",
       "      <td>-1.156654</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_descendent_decanal_hon</th>\n",
       "      <th>feature_petitionary_evanescent_diallage</th>\n",
       "      <td>0.128122</td>\n",
       "      <td>0.944659</td>\n",
       "      <td>0.816537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_indirect_concrete_canaille</th>\n",
       "      <th>feature_juvenalian_paunchy_uniformitarianism</th>\n",
       "      <td>-0.098432</td>\n",
       "      <td>0.727993</td>\n",
       "      <td>0.826425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_draconic_contractible_romper</th>\n",
       "      <th>feature_indirect_concrete_canaille</th>\n",
       "      <td>-0.098432</td>\n",
       "      <td>0.737243</td>\n",
       "      <td>0.835675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_bleeding_arabesque_pneuma</th>\n",
       "      <th>feature_instrumentalist_extrovert_cassini</th>\n",
       "      <td>-0.135409</td>\n",
       "      <td>0.720442</td>\n",
       "      <td>0.855851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_juvenalian_paunchy_uniformitarianism</th>\n",
       "      <th>feature_lipogrammatic_blowsier_seismometry</th>\n",
       "      <td>-0.089265</td>\n",
       "      <td>0.783615</td>\n",
       "      <td>0.872879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>550725 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                              corr1  \\\n",
       "feature_brushed_psychiatrical_lubbock        feature_savory_engrained_undervest            1.000000   \n",
       "feature_interpenetrative_enabling_elm        feature_savory_engrained_undervest            1.000000   \n",
       "feature_horizontal_snug_description          feature_savory_engrained_undervest            1.000000   \n",
       "feature_abstersive_emotional_misinterpreter  feature_horizontal_snug_description           1.000000   \n",
       "feature_opposable_argumentative_mesoblast    feature_savory_engrained_undervest            1.000000   \n",
       "...                                                                                             ...   \n",
       "feature_descendent_decanal_hon               feature_petitionary_evanescent_diallage       0.128122   \n",
       "feature_indirect_concrete_canaille           feature_juvenalian_paunchy_uniformitarianism -0.098432   \n",
       "feature_draconic_contractible_romper         feature_indirect_concrete_canaille           -0.098432   \n",
       "feature_bleeding_arabesque_pneuma            feature_instrumentalist_extrovert_cassini    -0.135409   \n",
       "feature_juvenalian_paunchy_uniformitarianism feature_lipogrammatic_blowsier_seismometry   -0.089265   \n",
       "\n",
       "                                                                                              corr2  \\\n",
       "feature_brushed_psychiatrical_lubbock        feature_savory_engrained_undervest           -0.159888   \n",
       "feature_interpenetrative_enabling_elm        feature_savory_engrained_undervest           -0.159262   \n",
       "feature_horizontal_snug_description          feature_savory_engrained_undervest           -0.158691   \n",
       "feature_abstersive_emotional_misinterpreter  feature_horizontal_snug_description          -0.156998   \n",
       "feature_opposable_argumentative_mesoblast    feature_savory_engrained_undervest           -0.156654   \n",
       "...                                                                                             ...   \n",
       "feature_descendent_decanal_hon               feature_petitionary_evanescent_diallage       0.944659   \n",
       "feature_indirect_concrete_canaille           feature_juvenalian_paunchy_uniformitarianism  0.727993   \n",
       "feature_draconic_contractible_romper         feature_indirect_concrete_canaille            0.737243   \n",
       "feature_bleeding_arabesque_pneuma            feature_instrumentalist_extrovert_cassini     0.720442   \n",
       "feature_juvenalian_paunchy_uniformitarianism feature_lipogrammatic_blowsier_seismometry    0.783615   \n",
       "\n",
       "                                                                                           corr_diff  \n",
       "feature_brushed_psychiatrical_lubbock        feature_savory_engrained_undervest            -1.159888  \n",
       "feature_interpenetrative_enabling_elm        feature_savory_engrained_undervest            -1.159262  \n",
       "feature_horizontal_snug_description          feature_savory_engrained_undervest            -1.158691  \n",
       "feature_abstersive_emotional_misinterpreter  feature_horizontal_snug_description           -1.156998  \n",
       "feature_opposable_argumentative_mesoblast    feature_savory_engrained_undervest            -1.156654  \n",
       "...                                                                                              ...  \n",
       "feature_descendent_decanal_hon               feature_petitionary_evanescent_diallage        0.816537  \n",
       "feature_indirect_concrete_canaille           feature_juvenalian_paunchy_uniformitarianism   0.826425  \n",
       "feature_draconic_contractible_romper         feature_indirect_concrete_canaille             0.835675  \n",
       "feature_bleeding_arabesque_pneuma            feature_instrumentalist_extrovert_cassini      0.855851  \n",
       "feature_juvenalian_paunchy_uniformitarianism feature_lipogrammatic_blowsier_seismometry     0.872879  \n",
       "\n",
       "[550725 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "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\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some features are predictive on their own"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "feature_lofty_acceptable_challenge           -0.082876\n",
       "feature_unvaried_social_bangkok              -0.082876\n",
       "feature_undivorced_unsatisfying_praetorium   -0.072182\n",
       "feature_literate_treacly_altercation         -0.070247\n",
       "feature_commensurable_industrial_jungfrau    -0.069840\n",
       "                                                ...   \n",
       "feature_apomictical_motorized_vaporisation    0.055358\n",
       "feature_delimited_doable_jim                  0.056142\n",
       "feature_durative_cuboidal_mirepoix            0.056497\n",
       "feature_airborne_genitival_cathouse           0.058023\n",
       "feature_alloyed_copyright_protium             0.059169\n",
       "Length: 1050, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "pd.Series(feature_scores).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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_grandmotherly_circumnavigable_homonymity\"])[0,1]\n",
    "    for era, tdf in df.groupby(eras)\n",
    "})\n",
    "by_era_correlation.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The feature exposures of different targets can be very different, even orthogonal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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_20']+features].T)[1:,0])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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_60']+features].fillna(0.5).T)[1:,0])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6713159328865955\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": 27,
   "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_20']+features].T)[1:,0])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "feature_scores_janet = {feature: score for feature, score in zip(features, np.corrcoef(df[df.era=='0001'][['target_janet_20']+features].T)[1:,0])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05603328047427541\n"
     ]
    },
    {
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\n",
      "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_janet.values())\n",
    "print(np.corrcoef(list(feature_scores_jerome.values()), list(feature_scores_janet.values()))[0,1])"
   ]
  },
  {
   "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": 30,
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "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>0.007108</td>\n",
       "      <td>-0.000476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_on_2</th>\n",
       "      <td>-0.000780</td>\n",
       "      <td>0.006694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            eval_on_1  eval_on_2\n",
       "train_on_1   0.007108  -0.000476\n",
       "train_on_2  -0.000780   0.006694"
      ]
     },
     "execution_count": 32,
     "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": 33,
   "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>0.081889</td>\n",
       "      <td>0.037630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_on_2</th>\n",
       "      <td>0.038557</td>\n",
       "      <td>0.078762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            eval_on_1  eval_on_2\n",
       "train_on_1   0.081889   0.037630\n",
       "train_on_2   0.038557   0.078762"
      ]
     },
     "execution_count": 33,
     "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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# This can be be run with LightGBM as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 35,
     "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": 36,
   "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>0.025232</td>\n",
       "      <td>0.002172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_on_2</th>\n",
       "      <td>0.002142</td>\n",
       "      <td>0.025901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            eval_on_1  eval_on_2\n",
       "train_on_1   0.025232   0.002172\n",
       "train_on_2   0.002142   0.025901"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "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>0.192827</td>\n",
       "      <td>0.047101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train_on_2</th>\n",
       "      <td>0.048331</td>\n",
       "      <td>0.197548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            eval_on_1  eval_on_2\n",
       "train_on_1   0.192827   0.047101\n",
       "train_on_2   0.048331   0.197548"
      ]
     },
     "execution_count": 37,
     "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\"])"
   ]
  },
  {
   "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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 38,
     "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['feature_dichasial_hammier_spawner',\n",
       " 'feature_rheumy_epistemic_prancer',\n",
       " 'feature_pert_performative_hormuz',\n",
       " 'feature_hillier_unpitied_theobromine',\n",
       " 'feature_perigean_bewitching_thruster',\n",
       " 'feature_renegade_undomestic_milord',\n",
       " 'feature_koranic_rude_corf',\n",
       " 'feature_demisable_expiring_millepede',\n",
       " 'feature_unscheduled_malignant_shingling',\n",
       " 'feature_clawed_unwept_adaptability',\n",
       " 'feature_rubblier_chlorotic_stogy',\n",
       " 'feature_untumbled_histologic_inion',\n",
       " 'feature_piffling_inflamed_jupiter',\n",
       " 'feature_abstersive_emotional_misinterpreter',\n",
       " 'feature_unluckiest_mulley_benzyl',\n",
       " 'feature_escutcheoned_timocratic_kotwal',\n",
       " 'feature_integrated_extroversive_ambivalence',\n",
       " 'feature_vedic_mitral_swiz',\n",
       " 'feature_reclaimed_fallibilist_turpentine',\n",
       " 'feature_gone_honduran_worshipper',\n",
       " 'feature_insociable_exultant_tatum',\n",
       " 'feature_outdated_tapered_speciation',\n",
       " 'feature_leggiest_slaggiest_inez',\n",
       " 'feature_chaldean_vixenly_propylite',\n",
       " 'feature_hysteric_mechanized_recklinghausen',\n",
       " 'feature_glare_factional_assessment',\n",
       " 'feature_highland_eocene_berean',\n",
       " 'feature_seemlier_reorient_monandry',\n",
       " 'feature_expressed_abhominable_pruning',\n",
       " 'feature_castrated_presented_quizzer',\n",
       " 'feature_restricted_aggregately_workmanship',\n",
       " 'feature_scorbutic_intellectualism_mongoloid',\n",
       " 'feature_telephonic_shakable_bollock',\n",
       " 'feature_subglobular_unsalable_patzer',\n",
       " 'feature_syrian_coital_counterproof',\n",
       " 'feature_supergene_legible_antarthritic',\n",
       " 'feature_hypothetic_distressing_endemic',\n",
       " 'feature_torturesome_estimable_preferrer',\n",
       " 'feature_greedier_favorable_enthymeme',\n",
       " 'feature_decent_solo_stickup',\n",
       " 'feature_unmalleable_resistant_kingston',\n",
       " 'feature_seamier_jansenism_inflator',\n",
       " 'feature_lordly_lamellicorn_buxtehude',\n",
       " 'feature_mattery_past_moro',\n",
       " 'feature_helpable_chanciest_fractionisation',\n",
       " 'feature_spookiest_expedite_overnighter',\n",
       " 'feature_culinary_pro_offering',\n",
       " 'feature_ganoid_osiered_mineralogy',\n",
       " 'feature_hotfoot_behaviorist_terylene',\n",
       " 'feature_severe_tricky_pinochle',\n",
       " 'feature_maximal_unobserving_desalinisation',\n",
       " 'feature_voltairean_consolidative_parallel',\n",
       " 'feature_unmoved_alt_spoonerism',\n",
       " 'feature_tittering_virgilian_decliner',\n",
       " 'feature_methylated_necrophilic_serendipity',\n",
       " 'feature_calceolate_pudgy_armure',\n",
       " 'feature_unsparing_moralistic_commissary',\n",
       " 'feature_criticisable_authentical_deprecation',\n",
       " 'feature_undetermined_idle_aftergrowth',\n",
       " 'feature_antipathetical_terrorful_ife',\n",
       " 'feature_caressive_cognate_cubature',\n",
       " 'feature_crablike_panniered_gloating',\n",
       " 'feature_exacerbating_presentationism_apagoge',\n",
       " 'feature_epicurean_fetal_seising',\n",
       " 'feature_casuistic_barbarian_monochromy',\n",
       " 'feature_haematoid_runaway_nightjar',\n",
       " 'feature_croupiest_shaded_thermotropism',\n",
       " 'feature_zarathustrian_albigensian_itch',\n",
       " 'feature_unsparred_scarabaeid_anthologist',\n",
       " 'feature_unco_terefah_thirster',\n",
       " 'feature_sallowish_cognisant_romaunt',\n",
       " 'feature_grazed_blameful_desiderative',\n",
       " 'feature_whitened_remanent_blast',\n",
       " 'feature_introvert_symphysial_assegai',\n",
       " 'feature_casemated_ibsenian_grantee',\n",
       " 'feature_intertwined_leeriest_suffragette',\n",
       " 'feature_built_reincarnate_sherbet',\n",
       " 'feature_axillary_reluctant_shorty',\n",
       " 'feature_descendent_decanal_hon',\n",
       " 'feature_untrimmed_monaxial_accompanist',\n",
       " 'feature_desiderative_commiserative_epizoa',\n",
       " 'feature_subdued_spiffier_kano',\n",
       " 'feature_affricative_bromic_raftsman',\n",
       " 'feature_doggish_whacking_headscarf',\n",
       " 'feature_congenital_conched_perithecium',\n",
       " 'feature_terrific_epigamic_affectivity',\n",
       " 'feature_enlightening_mirthful_laurencin',\n",
       " 'feature_continuate_unprocurable_haversine',\n",
       " 'feature_fustiest_voiced_janet',\n",
       " 'feature_ovular_powered_neckar',\n",
       " 'feature_questionable_diplex_caesarist',\n",
       " 'feature_nubblier_plosive_deepening',\n",
       " 'feature_revitalizing_dashing_photomultiplier',\n",
       " 'feature_chafed_undenominational_backstitch',\n",
       " 'feature_assenting_darn_arthropod',\n",
       " 'feature_consecrate_legislative_cavitation',\n",
       " 'feature_inhabited_pettier_veinlet',\n",
       " 'feature_voltairean_dyslogistic_epagoge',\n",
       " 'feature_log_unregenerate_babel',\n",
       " 'feature_agile_unrespited_gaucho',\n",
       " 'feature_whopping_eminent_attempter',\n",
       " 'feature_travelled_semipermeable_perruquier',\n",
       " 'feature_horizontal_snug_description',\n",
       " 'feature_inhibited_snowiest_drawing',\n",
       " 'feature_salian_suggested_ephemeron',\n",
       " 'feature_diverticular_punjabi_matronship',\n",
       " 'feature_reminiscent_unpained_ukulele',\n",
       " 'feature_unsurveyed_chopped_feldspathoid',\n",
       " 'feature_degenerate_officinal_feasibility',\n",
       " 'feature_undrossy_serpentiform_sack',\n",
       " 'feature_festering_controvertible_hostler',\n",
       " 'feature_leukemic_paler_millikan',\n",
       " 'feature_subapostolic_dungy_fermion',\n",
       " 'feature_wale_planned_tolstoy',\n",
       " 'feature_bifacial_hexastyle_hemialgia',\n",
       " 'feature_revealable_aeonian_elvira',\n",
       " 'feature_perceivable_gasiform_psammite',\n",
       " 'feature_smugger_hydroponic_farnesol',\n",
       " 'feature_apostate_impercipient_knighthood',\n",
       " 'feature_unperfect_implemental_cellarage',\n",
       " 'feature_stereotypic_ebracteate_louise',\n",
       " 'feature_offshore_defamatory_catalog',\n",
       " 'feature_tribal_germinable_yarraman',\n",
       " 'feature_dispiriting_araeostyle_jersey',\n",
       " 'feature_clerkish_flowing_chapati',\n",
       " 'feature_venatic_intermetallic_darling',\n",
       " 'feature_nebule_barmier_bibliomania',\n",
       " 'feature_unknown_reusable_cabbage',\n",
       " 'feature_planar_unessential_bride',\n",
       " 'feature_irritant_euphuistic_weka',\n",
       " 'feature_whistleable_unbedimmed_chokey',\n",
       " 'feature_roiling_trimeric_kurosawa',\n",
       " 'feature_loyal_fishy_pith',\n",
       " 'feature_unlivable_morbific_traveling',\n",
       " 'feature_untellable_penal_allegorization',\n",
       " 'feature_coordinated_undecipherable_gag',\n",
       " 'feature_congealed_lee_steek',\n",
       " 'feature_intrusive_effluent_hokkaido',\n",
       " 'feature_epidermic_scruffiest_prosperity',\n",
       " 'feature_quadratic_untouched_liberty',\n",
       " 'feature_hypogastric_effectual_sunlight',\n",
       " 'feature_contused_festal_geochemistry',\n",
       " 'feature_hexametric_ventricose_limnology',\n",
       " 'feature_paramagnetic_complex_gish',\n",
       " 'feature_chuffier_analectic_conchiolin',\n",
       " 'feature_indefatigable_enterprising_calf',\n",
       " 'feature_untouchable_unsolvable_agouti',\n",
       " 'feature_reduplicate_conoid_albite',\n",
       " 'feature_sixteen_inbreed_are',\n",
       " 'feature_dovetailed_winy_hanaper',\n",
       " 'feature_bushwhacking_unaligned_imperturbability',\n",
       " 'feature_undescribed_methylic_friday',\n",
       " 'feature_inflexed_lamaism_crit',\n",
       " 'feature_calefactive_anapaestic_jerome',\n",
       " 'feature_agronomic_cryptal_advisor',\n",
       " 'feature_unbreakable_nosological_comedian',\n",
       " 'feature_unmodernized_vasodilator_galenist',\n",
       " 'feature_paraffinoid_irreplevisable_ombu',\n",
       " 'feature_invalid_extortionary_titillation',\n",
       " 'feature_fake_trident_agitator',\n",
       " 'feature_wieldable_defiled_aperitive',\n",
       " 'feature_contaminative_intrusive_tagrag',\n",
       " 'feature_himyarite_tetragonal_deceit',\n",
       " 'feature_tossing_denominative_threshing',\n",
       " 'feature_dendritic_prothallium_sweeper',\n",
       " 'feature_ruffianly_uncommercial_anatole',\n",
       " 'feature_hellenistic_scraggly_comfort',\n",
       " 'feature_squishiest_unsectarian_support',\n",
       " 'feature_myographic_gawkier_timbale',\n",
       " 'feature_faltering_tergal_tip',\n",
       " 'feature_antisubmarine_foregoing_cryosurgery',\n",
       " 'feature_altern_unnoticed_impregnation',\n",
       " 'feature_foamy_undrilled_glaciology',\n",
       " 'feature_appraisive_anagrammatical_tentacle',\n",
       " 'feature_emmetropic_heraclitean_conducting',\n",
       " 'feature_collective_stigmatic_handfasting',\n",
       " 'feature_iridic_unpropertied_spline',\n",
       " 'feature_farcical_spinal_samantha',\n",
       " 'feature_urochordal_swallowed_curn',\n",
       " 'feature_wombed_reverberatory_colourer',\n",
       " 'feature_merovingian_tenebrism_hartshorn',\n",
       " 'feature_stylistic_honduran_comprador',\n",
       " 'feature_caecilian_unexperienced_ova',\n",
       " 'feature_fumarolic_known_sharkskin',\n",
       " 'feature_belgravian_salopian_sheugh',\n",
       " 'feature_zymotic_varnished_mulga',\n",
       " 'feature_learned_claustral_quiddity',\n",
       " 'feature_brickier_heterostyled_scrutiny',\n",
       " 'feature_unnetted_bay_premillennialist',\n",
       " 'feature_uncurtailed_translucid_coccid',\n",
       " 'feature_jiggish_tritheist_probity',\n",
       " 'feature_groggy_undescried_geosphere',\n",
       " 'feature_hawkish_domiciliary_duramen',\n",
       " 'feature_base_ingrain_calligrapher',\n",
       " 'feature_accessorial_aroused_crochet',\n",
       " 'feature_fierier_goofier_follicle',\n",
       " 'feature_unburied_exponent_pace',\n",
       " 'feature_chelonian_pyknic_delphi',\n",
       " 'feature_tarry_meet_chapel',\n",
       " 'feature_generative_honorific_tughrik',\n",
       " 'feature_smoggy_niftiest_lunch',\n",
       " 'feature_coalier_typhoid_muntin',\n",
       " 'feature_chopfallen_fasciate_orchidologist',\n",
       " 'feature_euterpean_frazzled_williamsburg',\n",
       " 'feature_quinsied_increased_braincase',\n",
       " 'feature_unlawful_superintendent_brunet',\n",
       " 'feature_naval_edified_decarbonize',\n",
       " 'feature_scenic_cormophytic_bilirubin',\n",
       " 'feature_unvaried_social_bangkok',\n",
       " 'feature_fissirostral_multifoliate_chillon',\n",
       " 'feature_citified_certified_progestin',\n",
       " 'feature_otherworldly_hallstatt_tolerator',\n",
       " 'feature_unawakening_escapism_totemist',\n",
       " 'feature_chillier_consultatory_latest',\n",
       " 'feature_fustier_crossing_pharyngology',\n",
       " 'feature_nobiliary_nativist_pull',\n",
       " 'feature_hieroglyphic_scalar_synchroflash',\n",
       " 'feature_tricksy_dithyrambic_pallet',\n",
       " 'feature_unfearing_phraseological_adder',\n",
       " 'feature_symphysial_cossack_bonaparte',\n",
       " 'feature_dextrogyrate_permitted_mystagogy',\n",
       " 'feature_interfertile_neozoic_solder',\n",
       " 'feature_theocratic_discriminate_subchloride',\n",
       " 'feature_neptunian_supersensitive_stapler',\n",
       " 'feature_auroral_glary_quamash',\n",
       " 'feature_interoceptive_fifteenth_trey',\n",
       " 'feature_embryo_sloping_albertine',\n",
       " 'feature_beery_somatologic_elimination',\n",
       " 'feature_gustier_maladjusted_bargeman',\n",
       " 'feature_septarian_vapory_thatching',\n",
       " 'feature_seismographic_matronly_dimeter',\n",
       " 'feature_burning_incunabular_clodhopper',\n",
       " 'feature_poachier_unsufferable_bile',\n",
       " 'feature_incidental_unveiled_bobbysoxer',\n",
       " 'feature_prevailing_thrifty_organicism',\n",
       " \"feature_dirtiest_possessory_nuku'alofa\",\n",
       " 'feature_chinked_preborn_hideaway',\n",
       " 'feature_caudated_judicial_crossette',\n",
       " 'feature_hyperbaric_populated_optimum',\n",
       " 'feature_petalled_backmost_crimplene',\n",
       " 'feature_capitate_plaguy_hooper',\n",
       " 'feature_faced_moline_gavotte',\n",
       " 'feature_russety_boustrophedon_dactylogram',\n",
       " 'feature_inherited_accelerated_astaire',\n",
       " 'feature_waving_unmourned_bloke',\n",
       " 'feature_sidearm_pitchiest_line',\n",
       " 'feature_eating_lanose_linguini',\n",
       " 'feature_foliated_psychokinetic_vivian',\n",
       " 'feature_emissive_perinephric_conveniency',\n",
       " 'feature_unforced_atwitter_nailer',\n",
       " 'feature_provisionary_unfostered_subadar',\n",
       " 'feature_liverpudlian_multicellular_torsk',\n",
       " 'feature_frowzy_berserk_lag',\n",
       " 'feature_screwy_asianic_balanchine',\n",
       " 'feature_unmeaning_ungyved_outboard',\n",
       " 'feature_unexceptionable_saprozoic_culturist',\n",
       " 'feature_unheaded_unmated_behaviour',\n",
       " 'feature_unblunted_titubant_sequoia',\n",
       " 'feature_perturbed_dry_grosvenor',\n",
       " 'feature_mortal_sovran_powerboat',\n",
       " 'feature_geodic_corky_clinic',\n",
       " 'feature_latin_throwback_lactoscope',\n",
       " 'feature_spicier_caller_billfold',\n",
       " 'feature_bathypelagic_companionate_disenchanter',\n",
       " 'feature_adept_unsensitive_hieroglyph',\n",
       " 'feature_hotshot_undefied_aten',\n",
       " 'feature_innumerate_alated_soliped',\n",
       " 'feature_apogamic_obeisant_pondicherry',\n",
       " 'feature_shabbiest_bewildering_editorialization',\n",
       " 'feature_irreplaceable_regurgitate_ctenophore',\n",
       " 'feature_socialized_obconical_itemization',\n",
       " 'feature_amphictyonic_secretarial_induline',\n",
       " 'feature_unsatisfied_mesopotamian_woodruff',\n",
       " 'feature_backswept_cryptogenic_baku',\n",
       " 'feature_healthiest_glial_cliquism',\n",
       " 'feature_absent_herpetological_genitor',\n",
       " 'feature_accountable_ethnographic_counterexample',\n",
       " 'feature_unseparable_cedarn_orcadian',\n",
       " 'feature_inhabited_collectable_soda',\n",
       " 'feature_arizonan_endurable_daw',\n",
       " 'feature_tanagrine_balconied_shut',\n",
       " 'feature_powdered_incased_project',\n",
       " 'feature_unassimilated_waxed_vesicle',\n",
       " 'feature_conjunctional_maned_banian',\n",
       " 'feature_contorted_weaponed_piaffe',\n",
       " 'feature_leadiest_unliveable_macadamia',\n",
       " 'feature_ignored_splay_management',\n",
       " 'feature_aggrieved_rammish_tommy',\n",
       " 'feature_coelomate_orbiculate_osteology',\n",
       " 'feature_amoebic_scarabaeid_gypsum',\n",
       " 'feature_unforbidden_flaming_settlement',\n",
       " 'feature_edge_hanoverian_cockneyfication',\n",
       " 'feature_picked_inferrible_lanark',\n",
       " 'feature_geographical_ecstatic_geta',\n",
       " 'feature_gymnastic_neap_thoracostomy',\n",
       " 'feature_nonclinical_stewed_disreputability',\n",
       " 'feature_tensed_cinnamonic_league',\n",
       " 'feature_pongid_agentive_rhomboid',\n",
       " 'feature_alloyed_copyright_protium',\n",
       " 'feature_afloat_brickiest_supernationalism',\n",
       " 'feature_healthful_mitigated_magnesium',\n",
       " 'feature_hypertrophied_embryologic_forfeiter',\n",
       " 'feature_few_lagoonal_alcyonarian',\n",
       " 'feature_temporary_dreamful_aldershot',\n",
       " 'feature_midnightly_falser_replacement',\n",
       " 'feature_nomothetic_autistic_ilk',\n",
       " 'feature_tyrian_wretched_frangipane',\n",
       " 'feature_waxiest_orthogonal_hiroshima',\n",
       " 'feature_raglan_gouty_twiddler',\n",
       " 'feature_cleanly_kickable_miriam',\n",
       " 'feature_necrophiliac_unsublimated_rudd',\n",
       " 'feature_delimited_doable_jim',\n",
       " 'feature_brushed_psychiatrical_lubbock',\n",
       " 'feature_exsanguine_ethereal_archon',\n",
       " 'feature_evaporative_largo_aster',\n",
       " 'feature_incendiary_failing_fluff',\n",
       " 'feature_aloetic_presented_scarer',\n",
       " 'feature_ruly_comtist_passerine',\n",
       " 'feature_gymnasial_supremacist_osteopathy',\n",
       " 'feature_protolithic_ontogenic_fusibility',\n",
       " 'feature_representable_ungored_zoffany',\n",
       " 'feature_curvier_antinodal_absolute',\n",
       " 'feature_zionist_enterable_vagabond',\n",
       " 'feature_elegiac_isolationism_lookout',\n",
       " 'feature_toilful_exordial_synchro',\n",
       " 'feature_auxetic_cholagogue_sexton',\n",
       " 'feature_inane_ribald_adrenal',\n",
       " 'feature_sere_hateable_conspicuity',\n",
       " 'feature_capsulate_rival_marcel',\n",
       " 'feature_pressed_animalcular_garden',\n",
       " 'feature_scythian_supposed_fortalice',\n",
       " 'feature_elating_apeak_hoodwinker',\n",
       " 'feature_telangiectatic_unwon_machtpolitik',\n",
       " 'feature_quaggy_constabulary_mismanagement',\n",
       " 'feature_intersidereal_chewy_sinusoid',\n",
       " 'feature_yearning_tippy_taking',\n",
       " 'feature_healthy_preterhuman_conto',\n",
       " 'feature_wispy_reverberating_fyrd',\n",
       " 'feature_inebriant_unhopeful_beecham',\n",
       " 'feature_limbic_pulverizable_hindfoot',\n",
       " 'feature_avowed_cloying_anthropologist',\n",
       " 'feature_deformed_unenslaved_graniteware',\n",
       " 'feature_foudroyant_undersigned_ascendance',\n",
       " 'feature_aberrant_frustrate_drosophila',\n",
       " 'feature_loved_purpure_malthusian',\n",
       " 'feature_unsyllabled_skin_underworkman',\n",
       " 'feature_catercorner_seemliest_prepostor',\n",
       " 'feature_proletary_nonionic_varment',\n",
       " 'feature_concessible_carpal_telesthesia',\n",
       " 'feature_acquirable_helvetic_tercel',\n",
       " 'feature_adynamic_glossier_ghana',\n",
       " 'feature_frizzier_alcoholic_gadolinium',\n",
       " 'feature_dyeline_catechistic_placet',\n",
       " 'feature_unaltered_squeezable_subway',\n",
       " 'feature_eyed_scotistic_diabolism',\n",
       " 'feature_imperviable_surprised_running',\n",
       " 'feature_overfraught_chinked_jinx',\n",
       " 'feature_nonfunctional_cnemial_illocution',\n",
       " 'feature_sunk_pyrrho_allah',\n",
       " 'feature_marxian_subminiature_minx',\n",
       " 'feature_telling_collinear_estanciero',\n",
       " 'feature_battled_beating_radiotelephony',\n",
       " 'feature_messy_glottic_ciborium',\n",
       " 'feature_adducent_ailing_gene',\n",
       " 'feature_inoperable_monegasque_infuriation',\n",
       " 'feature_suggestive_sexological_massasauga',\n",
       " 'feature_radiculose_perfectionistic_swing',\n",
       " 'feature_deliquescent_lintiest_protamine',\n",
       " 'feature_splitting_sexagesimal_teutonisation',\n",
       " 'feature_jauntier_talc_noreen',\n",
       " 'feature_yankee_nonary_okavango',\n",
       " 'feature_divulsive_cordial_bremsstrahlung',\n",
       " 'feature_widespread_mannish_seaquake',\n",
       " 'feature_registered_arrayed_galactagogue',\n",
       " 'feature_dropping_naevoid_swag',\n",
       " 'feature_unmown_circumventive_drayage',\n",
       " 'feature_inelastic_verist_graf',\n",
       " 'feature_streamlined_constricting_boz',\n",
       " 'feature_sole_soapy_tangible',\n",
       " 'feature_gnathic_terminological_defector',\n",
       " 'feature_bucktooth_stockish_sideropenia',\n",
       " 'feature_affable_yellow_terne',\n",
       " 'feature_bedded_neutral_relishing',\n",
       " 'feature_olid_reunionistic_malachi',\n",
       " 'feature_imitable_artificial_titration',\n",
       " 'feature_viverrine_lockable_quitch',\n",
       " 'feature_mephitic_televisionary_breeder',\n",
       " 'feature_talcose_patelliform_cana',\n",
       " 'feature_muddier_besieged_escarp',\n",
       " 'feature_protanopic_reptant_nimiety',\n",
       " 'feature_wicker_crescent_slavocrat',\n",
       " 'feature_fictile_shadowy_esse',\n",
       " 'feature_guttering_liberating_conurbation',\n",
       " 'feature_transpiring_subsolar_biggy',\n",
       " 'feature_rascal_unprintable_lansing',\n",
       " 'feature_carnassial_undecided_suspensor',\n",
       " 'feature_unkindled_apterygial_contributory',\n",
       " 'feature_equipotent_undefeated_anointer',\n",
       " 'feature_gregorian_electromagnetic_hepar',\n",
       " 'feature_dyspeptic_disseminative_anglophobia',\n",
       " 'feature_durative_cuboidal_mirepoix',\n",
       " 'feature_gyral_extrapolated_serotherapy',\n",
       " 'feature_literary_live_taxon',\n",
       " 'feature_deliberative_connatural_kinetoscope',\n",
       " 'feature_dozier_mad_outrush',\n",
       " 'feature_polybasic_unbreathed_magdeburg',\n",
       " 'feature_attainable_solonian_impropriator',\n",
       " 'feature_underlying_pegmatitic_disperser',\n",
       " 'feature_counter_arbitral_gyration',\n",
       " 'feature_biased_puzzling_cessation',\n",
       " 'feature_stylar_denudate_intonation',\n",
       " 'feature_bold_brindle_playground',\n",
       " 'feature_brachydactylic_azonic_strife',\n",
       " 'feature_saving_inhabitable_lighthouse',\n",
       " 'feature_totemic_unshaded_encore',\n",
       " 'feature_echoic_dustproof_indiscernibility',\n",
       " 'feature_passable_premed_independent',\n",
       " 'feature_dreary_penal_acroterium',\n",
       " 'feature_haziest_lifelike_horseback',\n",
       " 'feature_prevailing_nubile_hearst',\n",
       " 'feature_iraqi_gentled_hector',\n",
       " 'feature_theban_byronic_prolation',\n",
       " 'feature_resolutive_betrothed_recession',\n",
       " 'feature_motherly_chalkier_woking',\n",
       " 'feature_untinged_crystallographic_succulent',\n",
       " 'feature_plutonian_unsegregated_meringue',\n",
       " 'feature_myological_adducent_puebla',\n",
       " 'feature_cromwellian_imbecilic_algonkian',\n",
       " 'feature_exterminated_grumbling_lawing',\n",
       " 'feature_bhutan_imagism_dolerite',\n",
       " 'feature_overt_smart_stendhal',\n",
       " 'feature_bipinnate_oriented_earmuff',\n",
       " 'feature_telial_gnarliest_darmstadt',\n",
       " 'feature_savory_engrained_undervest',\n",
       " 'feature_vibrational_schooled_quadrivalence',\n",
       " 'feature_steepled_transistorized_shelter',\n",
       " 'feature_heliocentric_stealthy_waning',\n",
       " 'feature_aberdeen_tangled_providence',\n",
       " 'feature_quarrelsome_sneakiest_misadventure',\n",
       " 'feature_haunted_fussier_vaunt',\n",
       " 'feature_astronomic_gripple_culverin',\n",
       " 'feature_reproving_aurorean_parlour',\n",
       " 'feature_baldish_cognitional_naha',\n",
       " 'feature_grummest_confounding_quinquagesima',\n",
       " 'feature_frowsiest_bonapartean_income',\n",
       " 'feature_subcordate_hypsometric_matriarchate',\n",
       " 'feature_cymose_prima_guardhouse',\n",
       " 'feature_deceased_peppiest_hitlerism',\n",
       " 'feature_unstitched_peppercorny_value',\n",
       " 'feature_slack_calefacient_tableau',\n",
       " 'feature_mesoblastic_anatolian_deodand',\n",
       " 'feature_undissociated_fuzziest_monoamine',\n",
       " 'feature_whitewashed_aliunde_mesopotamia',\n",
       " 'feature_unsolvable_majuscular_caique',\n",
       " 'feature_unwinding_prettyish_megger',\n",
       " 'feature_estuarine_candescent_podunk',\n",
       " 'feature_plaguy_preceding_hosteller',\n",
       " 'feature_septic_cuticular_cape',\n",
       " 'feature_fertilized_quartile_roll',\n",
       " 'feature_elating_diluvian_telepathist',\n",
       " 'feature_ginger_unexplored_sadduceeism',\n",
       " 'feature_satanic_hominoid_lingo',\n",
       " 'feature_bristly_encysted_vendue',\n",
       " 'feature_semibold_disparaging_turnip',\n",
       " 'feature_scythian_lithographical_whigmaleerie',\n",
       " 'feature_store_apteral_isocheim',\n",
       " 'feature_overmerry_quinary_raisin',\n",
       " 'feature_vedic_laryngitic_battuta',\n",
       " 'feature_lotic_anticorrosive_acetone',\n",
       " 'feature_hungry_thermoluminescent_shareholding',\n",
       " 'feature_multiplicate_taunt_vena',\n",
       " 'feature_rationalist_crippled_dreadnaught',\n",
       " 'feature_blue_swedenborgianism_squinter',\n",
       " 'feature_pictographic_epithetic_pleiocene',\n",
       " 'feature_rival_leninist_philippine',\n",
       " 'feature_unsafe_perspectivist_dairyman',\n",
       " 'feature_verism_awing_countrywoman',\n",
       " 'feature_grandmotherly_circumnavigable_homonymity',\n",
       " 'feature_twinning_inconsequent_quercetin',\n",
       " 'feature_dustiest_algonkian_plasticity',\n",
       " 'feature_formational_preventive_capp',\n",
       " 'feature_ruptured_activist_twill',\n",
       " 'feature_inapproachable_aging_lully',\n",
       " 'feature_tidied_tinnier_sacker',\n",
       " 'feature_crookbacked_irreplaceable_heresiography',\n",
       " 'feature_twentieth_pampered_statocyst',\n",
       " 'feature_burled_zinky_verdin',\n",
       " 'feature_unhygienic_brevipennate_viol',\n",
       " 'feature_glued_antemundane_eschscholtzia',\n",
       " 'feature_rockier_overdelicate_barostat',\n",
       " 'feature_persnickety_schmalziest_dolichocephaly',\n",
       " 'feature_phenomenal_photosynthetic_berg',\n",
       " 'feature_peritectic_medallic_leaper',\n",
       " 'feature_gripple_bracteal_huckster',\n",
       " 'feature_holophytic_fiercest_ruining',\n",
       " 'feature_octave_tingly_flitch',\n",
       " 'feature_beauish_seismal_lobe',\n",
       " 'feature_campestral_salted_romanism',\n",
       " 'feature_eclectic_citified_testator',\n",
       " 'feature_unministerial_albinotic_salish',\n",
       " 'feature_tercentenary_triliteral_formosa',\n",
       " 'feature_porky_gyratory_whitewall',\n",
       " 'feature_dissilient_stunning_ribwort',\n",
       " 'feature_ovidian_compelling_popsy',\n",
       " 'feature_semestral_unapplausive_warfarin',\n",
       " 'feature_newish_vortical_upthrow',\n",
       " 'feature_compurgatorial_demeaning_purser',\n",
       " 'feature_antiphrastical_tipsy_chinquapin',\n",
       " 'feature_provable_downstair_titicaca',\n",
       " 'feature_dorsolumbar_dolesome_marshmallow',\n",
       " 'feature_trochaic_metonymic_dichogamy',\n",
       " 'feature_antiscorbutic_visored_convalescent',\n",
       " 'feature_unblamed_mammoth_commie',\n",
       " 'feature_wiliest_specialistic_azrael',\n",
       " 'feature_kenotic_hispanic_yulan',\n",
       " 'feature_fine_aberrational_deducibility',\n",
       " 'feature_reachable_angled_irrevocability',\n",
       " 'feature_cachectical_metaphysic_patency',\n",
       " 'feature_gory_ratite_santir',\n",
       " 'feature_derogative_sawdusty_photolithography',\n",
       " 'feature_conciliable_fleeting_equestrienne',\n",
       " 'feature_newish_eosinophilic_sciaenoid',\n",
       " 'feature_opposable_argumentative_mesoblast',\n",
       " 'feature_guileful_congenerical_literation',\n",
       " 'feature_liquefacient_premium_pyxidium',\n",
       " 'feature_sleaziest_neediest_yawn',\n",
       " 'feature_unforeboding_cometic_billionaire',\n",
       " 'feature_jubilant_ghostlier_pedestrianism',\n",
       " 'feature_doctrinal_popular_disqualifying',\n",
       " 'feature_pseudo_ungeared_dullard',\n",
       " 'feature_sclerotic_resupinate_hard',\n",
       " 'feature_encased_jutting_gasometer',\n",
       " 'feature_calycled_necrologic_naboth',\n",
       " 'feature_teenage_tapetal_infector',\n",
       " 'feature_undebauched_conservatory_doronicum',\n",
       " 'feature_fescennine_intramundane_flamen',\n",
       " 'feature_gymnastic_erective_tranche',\n",
       " 'feature_phantasmal_stomachal_peperoni',\n",
       " 'feature_pulverulent_tarnal_sitting',\n",
       " 'feature_hetero_overweary_hylotheist',\n",
       " 'feature_imponderable_seasoned_kinetograph',\n",
       " 'feature_anoxic_froward_europe',\n",
       " 'feature_antiperspirant_degradable_voltaire',\n",
       " 'feature_stockier_ghastlier_spectrogram',\n",
       " 'feature_appalachian_realistic_butyrate',\n",
       " 'feature_mobile_savoyard_maidenhead',\n",
       " 'feature_destructive_winterweight_towbar',\n",
       " 'feature_jammed_morphotic_destruction',\n",
       " 'feature_vaunting_offshore_bogey',\n",
       " 'feature_floricultural_unimpeded_nomen',\n",
       " 'feature_unwonted_trusted_fixative',\n",
       " 'feature_affined_pushier_haranguer',\n",
       " 'feature_unadjusted_citified_tupelo',\n",
       " 'feature_down_delinquent_madagascar',\n",
       " 'feature_nicer_fernier_banderilla',\n",
       " 'feature_laggard_mincing_gemologist',\n",
       " 'feature_cauterant_paly_discant',\n",
       " 'feature_wastable_nobler_peeling',\n",
       " 'feature_preparatory_sown_rehearser',\n",
       " 'feature_articulated_shamefaced_orzo',\n",
       " 'feature_scholiastic_dreamful_wardship',\n",
       " 'feature_resoluble_shortened_albumin',\n",
       " 'feature_crowning_frustrate_kampala',\n",
       " 'feature_unforbidden_highbrow_kafir',\n",
       " 'feature_equiprobable_unsisterly_mississauga',\n",
       " 'feature_jumping_homemaking_shaveling',\n",
       " 'feature_gleetier_unpared_pentecost',\n",
       " 'feature_fishier_groggier_corporeity',\n",
       " 'feature_lashed_dirigible_scrubber',\n",
       " 'feature_unstaying_contrary_ichor',\n",
       " 'feature_conventionalized_volitant_citadel',\n",
       " 'feature_tepidity_endocrinal_altaic',\n",
       " 'feature_servomechanical_wally_picul',\n",
       " 'feature_resonant_elusive_monetisation',\n",
       " 'feature_thalamencephalic_derisory_carte',\n",
       " 'feature_ratable_wrapround_epitome',\n",
       " 'feature_often_awry_chyme',\n",
       " 'feature_limitative_traditional_yodeller',\n",
       " 'feature_earthlier_parian_pistachio',\n",
       " 'feature_clashing_disheartening_rattler',\n",
       " 'feature_monocarpic_grey_hanger',\n",
       " 'feature_macrocephalic_tiresome_piroshki',\n",
       " 'feature_unshaped_compound_trunkfish',\n",
       " 'feature_malarian_multinuclear_averroist',\n",
       " 'feature_thousandfold_cairned_commutator',\n",
       " 'feature_incog_decolorant_refluence',\n",
       " 'feature_gay_deceased_reen',\n",
       " 'feature_fortunate_unorthodoxy_trish',\n",
       " 'feature_impartable_unpeaceful_pennsylvanian',\n",
       " 'feature_maintained_childbearing_isagoge',\n",
       " 'feature_illegible_punishing_lunacy',\n",
       " 'feature_craziest_cordate_ravishment',\n",
       " 'feature_voltairian_blackened_lodge',\n",
       " 'feature_renowned_phthisic_ferrate',\n",
       " 'feature_jeffersonian_torrent_cobaltite',\n",
       " 'feature_reflected_vast_rhetor',\n",
       " 'feature_arithmetic_delicate_pantagruelist',\n",
       " 'feature_sooth_declinatory_chaton',\n",
       " 'feature_qualifiable_dragging_mankind',\n",
       " \"feature_whatsoe'er_discredited_faerie\",\n",
       " 'feature_prepotent_swedish_superexcellence',\n",
       " 'feature_principled_inspiratory_loupe',\n",
       " 'feature_aesthetical_daltonian_anemology',\n",
       " 'feature_ectodermal_unfeigning_chester',\n",
       " 'feature_continental_archival_katmandu',\n",
       " 'feature_fraternal_whackiest_poppy',\n",
       " 'feature_polymorphic_climactic_corslet',\n",
       " 'feature_contraband_pyorrhoeal_vernalisation',\n",
       " 'feature_sainted_nosiest_covenant',\n",
       " 'feature_subcultural_overbearing_assiduity',\n",
       " 'feature_airborne_genitival_cathouse',\n",
       " 'feature_adventive_eristic_wedlock',\n",
       " 'feature_triadic_vitreum_gelding',\n",
       " 'feature_accusatory_disinfectant_deportment',\n",
       " 'feature_gainly_tritheism_syndactyl',\n",
       " 'feature_canalicular_peeling_lilienthal',\n",
       " 'feature_enslaved_fattest_radiotelegraph',\n",
       " 'feature_childing_funest_turbidity',\n",
       " 'feature_pycnostyle_unenjoyable_balladry',\n",
       " 'feature_sludgiest_telltale_mutant',\n",
       " 'feature_tested_kinkiest_levigation',\n",
       " 'feature_mailed_harried_grant',\n",
       " 'feature_diabasic_prescribed_autograft',\n",
       " 'feature_persuasible_ablest_menology',\n",
       " 'feature_budgetary_surrounded_formicary',\n",
       " 'feature_neurophysiological_runcinate_yardang',\n",
       " 'feature_antichristian_slangiest_idyllist',\n",
       " 'feature_flintier_enslaved_borsch',\n",
       " 'feature_exorbitant_myeloid_crinkle',\n",
       " 'feature_moralistic_heartier_typhoid',\n",
       " 'feature_gutta_exploitive_simpson',\n",
       " 'feature_assertive_worsened_scarper',\n",
       " 'feature_intersubjective_juristic_sagebrush',\n",
       " 'feature_rowable_unshod_noise',\n",
       " 'feature_volitional_ascensive_selfhood',\n",
       " 'feature_barest_kempt_crowd',\n",
       " 'feature_curtained_gushier_tranquilizer',\n",
       " 'feature_intermontane_vertical_moo',\n",
       " 'feature_isotopic_hymenial_starwort',\n",
       " 'feature_inseminated_filarial_mesoderm',\n",
       " 'feature_passerine_ultraist_neon',\n",
       " 'feature_westering_immunosuppressive_crapaud',\n",
       " 'feature_reported_slimy_rhapsody',\n",
       " 'feature_juvenalian_paunchy_uniformitarianism',\n",
       " 'feature_lipogrammatic_blowsier_seismometry',\n",
       " 'feature_draconic_contractible_romper',\n",
       " 'feature_indirect_concrete_canaille',\n",
       " 'feature_puberulent_nondescript_laparoscope',\n",
       " 'feature_unsurveyed_boyish_aleph',\n",
       " 'feature_dismaying_chaldean_tallith',\n",
       " 'feature_epitaxial_loathsome_essen',\n",
       " 'feature_malagasy_abounding_circumciser',\n",
       " 'feature_tortured_arsenical_arable',\n",
       " 'feature_sludgy_implemental_sicily',\n",
       " 'feature_uretic_seral_decoding',\n",
       " 'feature_roasting_slaked_reposition',\n",
       " 'feature_peculiar_sheenier_quintal',\n",
       " 'feature_publishable_apiarian_rollick',\n",
       " 'feature_interdental_mongolian_anarchism',\n",
       " 'feature_reserved_cleanable_soldan',\n",
       " 'feature_eruptive_seasoned_pharmacognosy',\n",
       " 'feature_softish_unseparated_caudex',\n",
       " 'feature_univalve_abdicant_distrail',\n",
       " 'feature_levigate_kindly_dyspareunia',\n",
       " 'feature_intercalative_helvetian_infirmarian',\n",
       " 'feature_centric_shaggier_cranko',\n",
       " 'feature_unliving_bit_bengaline',\n",
       " 'feature_misanthropic_knurliest_freebooty',\n",
       " 'feature_confiscatory_triennial_pelting',\n",
       " 'feature_peltate_okay_info',\n",
       " 'feature_reconciling_dauby_database',\n",
       " 'feature_unspotted_practiced_gland',\n",
       " 'feature_iconomatic_boozier_age',\n",
       " 'feature_congenial_transmigrant_isobel',\n",
       " 'feature_impractical_endorsed_tide',\n",
       " 'feature_huskiest_compartmental_jacquerie',\n",
       " 'feature_cairned_fumiest_ordaining',\n",
       " 'feature_calculating_unenchanted_microscopium',\n",
       " 'feature_mucky_loanable_gastrostomy',\n",
       " 'feature_loricate_cryptocrystalline_ethnology',\n",
       " 'feature_ctenoid_moaning_fontainebleau',\n",
       " 'feature_armoured_finable_skywriter',\n",
       " 'feature_intended_involute_highbinder',\n",
       " 'feature_commensurable_industrial_jungfrau',\n",
       " 'feature_spagyric_echt_alum',\n",
       " 'feature_illiterate_stomachal_terpene',\n",
       " 'feature_demure_groutiest_housedog',\n",
       " 'feature_dentilingual_removed_osmometer',\n",
       " 'feature_plexiform_won_elk',\n",
       " 'feature_induplicate_hoarse_disbursement',\n",
       " 'feature_vizierial_courtlier_hampton',\n",
       " 'feature_lost_quirky_botel',\n",
       " 'feature_hydrologic_cymric_nyctophobia',\n",
       " 'feature_atlantic_uveal_incommunicability',\n",
       " 'feature_incommensurable_diffused_curability',\n",
       " 'feature_midget_noncognizable_plenary',\n",
       " 'feature_unapplicable_jerkiest_klemperer',\n",
       " 'feature_unamazed_tumular_photomicrograph',\n",
       " 'feature_nucleophilic_uremic_endogen',\n",
       " 'feature_unnourishing_indiscreet_occiput',\n",
       " 'feature_hypersonic_volcanological_footwear',\n",
       " 'feature_uncomplimentary_malignant_scoff',\n",
       " 'feature_interrogatory_isohyetal_atacamite',\n",
       " 'feature_invalid_chromatographic_cornishman',\n",
       " 'feature_encompassing_skeptical_salience',\n",
       " 'feature_beady_unkind_barret',\n",
       " 'feature_gossamer_placable_wycliffite',\n",
       " 'feature_flavourful_seismic_erica',\n",
       " 'feature_petitionary_evanescent_diallage',\n",
       " 'feature_leisurable_dehortatory_pretoria',\n",
       " 'feature_chaotic_granitoid_theist',\n",
       " 'feature_cerebrovascular_weeny_advocate',\n",
       " 'feature_cyrenaic_unschooled_silurian',\n",
       " 'feature_uninclosed_handcrafted_springing',\n",
       " 'feature_indentured_communicant_tulipomania',\n",
       " 'feature_synoptic_botryose_earthwork',\n",
       " 'feature_acerb_venusian_piety',\n",
       " 'feature_vestmental_hoofed_transpose',\n",
       " 'feature_unrated_intact_balmoral',\n",
       " 'feature_tonal_graptolitic_corsac',\n",
       " 'feature_refreshed_untombed_skinhead',\n",
       " 'feature_communicatory_unrecommended_velure',\n",
       " 'feature_sudsy_polymeric_posteriority',\n",
       " 'feature_inexpugnable_gleg_candelilla',\n",
       " 'feature_headhunting_unsatisfied_phenomena',\n",
       " 'feature_scenographical_dissentient_trek',\n",
       " 'feature_trim_axial_suffocation',\n",
       " 'feature_buxom_curtained_sienna',\n",
       " 'feature_recidivism_petitory_methyltestosterone',\n",
       " 'feature_irresponsive_compositive_ramson',\n",
       " 'feature_more_hindoo_diageotropism',\n",
       " 'feature_apomictical_motorized_vaporisation',\n",
       " 'feature_seclusive_emendatory_plangency',\n",
       " 'feature_discrepant_ventral_shicker',\n",
       " 'feature_obeisant_vicarial_passibility',\n",
       " 'feature_unrelieved_rawish_cement',\n",
       " 'feature_strychnic_structuralist_chital',\n",
       " 'feature_unaimed_yonder_filmland',\n",
       " 'feature_designer_notchy_epiploon',\n",
       " 'feature_favoring_prescript_unorthodoxy',\n",
       " 'feature_cheering_protonemal_herd',\n",
       " 'feature_conjugal_postvocalic_rowe',\n",
       " 'feature_palmy_superfluid_argyrodite',\n",
       " 'feature_outsized_admonishing_errantry',\n",
       " 'feature_brawny_confocal_frail',\n",
       " 'feature_arillate_nickelic_hemorrhage',\n",
       " 'feature_bloodied_twinkling_andante',\n",
       " 'feature_rusted_unassisting_menaquinone',\n",
       " 'feature_biannual_maleficent_thack',\n",
       " 'feature_extractable_serrulate_swing',\n",
       " 'feature_covalent_methodological_brash',\n",
       " 'feature_periscopic_thirteenth_cartage',\n",
       " 'feature_tranquilizing_abashed_glyceria',\n",
       " 'feature_faustian_unventilated_lackluster',\n",
       " 'feature_frequentative_participial_waft',\n",
       " 'feature_flakiest_fleecy_novelese',\n",
       " 'feature_liege_unexercised_ennoblement',\n",
       " 'feature_amygdaloidal_intersectional_canonry',\n",
       " 'feature_rural_inquisitional_trotline',\n",
       " 'feature_caespitose_unverifiable_intent',\n",
       " 'feature_precooled_inoperable_credence',\n",
       " 'feature_ruthenian_uncluttered_vocalizing',\n",
       " 'feature_camphorated_spry_freemartin',\n",
       " 'feature_mined_game_curse',\n",
       " 'feature_amoebaean_wolfish_heeler',\n",
       " 'feature_peaty_vulgar_branchia',\n",
       " 'feature_ratlike_matrilinear_collapsability',\n",
       " 'feature_attuned_southward_heckle',\n",
       " 'feature_substandard_permissible_paresthesia',\n",
       " 'feature_curling_aurorean_iseult',\n",
       " 'feature_fleshly_bedimmed_enfacement',\n",
       " 'feature_hendecagonal_deathly_stiver',\n",
       " 'feature_massed_nonracial_ecclesiologist',\n",
       " 'feature_instrumentalist_extrovert_cassini',\n",
       " 'feature_patristical_analysable_langouste',\n",
       " 'feature_gullable_sanguine_incongruity',\n",
       " 'feature_phellogenetic_vibrational_jocelyn',\n",
       " 'feature_autodidactic_gnarlier_pericardium',\n",
       " 'feature_unbeaten_orological_dentin',\n",
       " 'feature_resuscitative_communicable_brede',\n",
       " 'feature_limitable_astable_physiology',\n",
       " 'feature_chartered_conceptual_spitting',\n",
       " 'feature_oversea_permed_insulter',\n",
       " 'feature_busty_unfitted_keratotomy',\n",
       " 'feature_alkaline_pistachio_sunstone',\n",
       " 'feature_incitant_trochoidal_oculist',\n",
       " 'feature_stelar_balmiest_pellitory',\n",
       " 'feature_hypermetropic_unsighted_forsyth',\n",
       " 'feature_calycled_living_birmingham',\n",
       " 'feature_transmontane_clerkly_value',\n",
       " 'feature_together_suppositive_aster',\n",
       " 'feature_planned_superimposed_bend',\n",
       " 'feature_undirected_perdu_ylem',\n",
       " 'feature_antipodal_unable_thievery',\n",
       " 'feature_scrobiculate_unexcitable_alder',\n",
       " 'feature_apophthegmatical_catechetical_millet',\n",
       " 'feature_fragrant_fifteen_brian',\n",
       " 'feature_churrigueresque_talc_archaicism',\n",
       " 'feature_uncompromising_fancy_kyle',\n",
       " 'feature_retinoscopy_flinty_wool',\n",
       " 'feature_pansophic_merino_pintado',\n",
       " 'feature_galvanometric_sturdied_billingsgate',\n",
       " 'feature_mazy_superrefined_punishment',\n",
       " 'feature_aztecan_encomiastic_pitcherful',\n",
       " 'feature_sorted_ignitable_sagitta',\n",
       " 'feature_padded_peripteral_pericranium',\n",
       " 'feature_trabeate_eutherian_valedictory',\n",
       " 'feature_undisguised_whatever_gaul',\n",
       " 'feature_migrant_reliable_chirurgery',\n",
       " 'feature_permanent_cottony_ballpen',\n",
       " 'feature_conceding_ingrate_tablespoonful',\n",
       " 'feature_vulcanological_sepulchral_spean',\n",
       " 'feature_unrequired_waxing_skeptic',\n",
       " 'feature_hibernating_soritic_croupe',\n",
       " 'feature_unstacked_trackable_blizzard',\n",
       " 'feature_multilinear_sharpened_mouse',\n",
       " 'feature_autarkic_constabulary_dukedom',\n",
       " 'feature_christadelphian_euclidean_boon',\n",
       " 'feature_covalent_unreformed_frogbit',\n",
       " 'feature_basaltic_arid_scallion',\n",
       " 'feature_uncharged_unovercome_smolder',\n",
       " 'feature_leaky_maroon_pyrometry',\n",
       " 'feature_polaroid_squalliest_applause',\n",
       " 'feature_jerkwater_eustatic_electrocardiograph',\n",
       " 'feature_malacological_differential_defeated',\n",
       " 'feature_ambisexual_boiled_blunderer',\n",
       " 'feature_endangered_unthreaded_firebrick',\n",
       " 'feature_unextinct_smectic_isa',\n",
       " 'feature_kerygmatic_splashed_ziegfeld',\n",
       " 'feature_palatalized_unsucceeded_induration',\n",
       " 'feature_springlike_crackjaw_bheesty',\n",
       " 'feature_unweary_congolese_captain',\n",
       " 'feature_uncertified_myrmecological_nagger',\n",
       " 'feature_hemispherical_unabsolved_aeolipile',\n",
       " 'feature_glyptic_unrubbed_holloway',\n",
       " 'feature_rimmed_conditional_archipelago',\n",
       " 'feature_bleeding_arabesque_pneuma',\n",
       " 'feature_dipped_sent_giuseppe',\n",
       " 'feature_undivorced_unsatisfying_praetorium',\n",
       " 'feature_reclinate_cruciform_lilo',\n",
       " 'feature_dermal_extortive_inversion',\n",
       " 'feature_tiniest_rangiest_investment',\n",
       " 'feature_creedal_greediest_vibrant',\n",
       " 'feature_plausive_carboxyl_elaborator',\n",
       " 'feature_undemocratic_unrhymed_subarea',\n",
       " 'feature_theocratical_shredded_categorist',\n",
       " 'feature_hedonistic_entertaining_pelting',\n",
       " 'feature_placable_conscionable_mickey',\n",
       " 'feature_crawlier_residuary_biracialism',\n",
       " 'feature_close_abloom_boomlet',\n",
       " 'feature_virtual_standard_instigator',\n",
       " 'feature_hagiographical_swelling_homager',\n",
       " 'feature_stereo_orthotone_infernal',\n",
       " 'feature_brittle_chautauqua_elite',\n",
       " 'feature_antinomical_swart_coo',\n",
       " 'feature_thumblike_aniconic_plantagenet',\n",
       " 'feature_stentorian_contraband_swarajism',\n",
       " 'feature_coveted_finniest_lipogram',\n",
       " 'feature_cut_rouged_defilement',\n",
       " 'feature_nude_revived_unpredictability',\n",
       " 'feature_subject_shaggier_relapse',\n",
       " 'feature_fraternal_manx_tambourine',\n",
       " 'feature_clarified_mimic_slash',\n",
       " 'feature_invincible_unroofed_tetrachord',\n",
       " 'feature_phantasmagorial_lyophilized_chippendale',\n",
       " 'feature_deathy_hylomorphic_christine',\n",
       " 'feature_antediluvial_contributive_statecraft',\n",
       " 'feature_equalized_statuary_more',\n",
       " 'feature_difficile_oxalic_spheroid',\n",
       " 'feature_macrobiotic_benignant_eurasia',\n",
       " 'feature_secernent_condensed_lynchet',\n",
       " 'feature_ternary_plump_floridity',\n",
       " 'feature_sumatran_lobose_novice',\n",
       " 'feature_twilled_eliminable_orthoepy',\n",
       " 'feature_photolithographic_attenuate_serapeum',\n",
       " 'feature_umbonate_haired_grisette',\n",
       " 'feature_gobony_configured_judicatory',\n",
       " 'feature_ruthenic_pinchbeck_limitarian',\n",
       " 'feature_acoustical_infirm_ascent',\n",
       " 'feature_saclike_pulverizable_doornail',\n",
       " 'feature_shielded_specialized_goalie',\n",
       " 'feature_institutionalized_individualized_worcestershire',\n",
       " 'feature_unmentioned_peddling_arboretum',\n",
       " 'feature_gabby_glimmery_azalea',\n",
       " 'feature_saturate_unsentimental_allograph',\n",
       " 'feature_interdigital_sewn_glucagon',\n",
       " 'feature_watery_behaviorist_incitation',\n",
       " 'feature_unshuttered_curly_uvular',\n",
       " 'feature_bareheaded_satisfied_copartner',\n",
       " 'feature_unseasonable_weathered_med',\n",
       " 'feature_undersexed_agone_metaplasm',\n",
       " 'feature_septicemic_telangiectatic_flake',\n",
       " 'feature_correlative_nociceptive_development',\n",
       " 'feature_literate_treacly_altercation',\n",
       " 'feature_nipping_pinnate_attempter',\n",
       " 'feature_eradicable_douce_teleconference',\n",
       " 'feature_prettyish_lapsed_manifest',\n",
       " 'feature_hierologic_assault_candida',\n",
       " 'feature_pellicular_comeliest_charlatan',\n",
       " 'feature_cottony_ischaemic_venery',\n",
       " 'feature_undersealed_waxiest_floriculturist',\n",
       " 'feature_upturned_epicanthic_galilee',\n",
       " 'feature_tweedy_noncontroversial_poisoner',\n",
       " 'feature_extrorse_idling_vinificator',\n",
       " 'feature_wavier_estranged_handstand',\n",
       " 'feature_interjaculatory_anhedonic_lindisfarne',\n",
       " 'feature_unmechanized_completed_specializer',\n",
       " 'feature_residentiary_rollable_boaz',\n",
       " 'feature_hypertensive_aroused_urquhart',\n",
       " 'feature_seven_entomological_boothose',\n",
       " 'feature_lageniform_theodicean_terylene',\n",
       " 'feature_unattached_flowing_longfellow',\n",
       " 'feature_hypertensive_seared_adenauer',\n",
       " 'feature_cambial_bigoted_bacterioid',\n",
       " 'feature_attributable_shagged_nourishment',\n",
       " 'feature_randomized_dystonic_knar',\n",
       " 'feature_pedal_dissident_thummim',\n",
       " 'feature_submediant_serbonian_rangefinder',\n",
       " 'feature_philologic_errhine_nubbin',\n",
       " 'feature_unlike_guelfic_sculp',\n",
       " 'feature_theophanic_winded_passacaglia',\n",
       " 'feature_haploid_occluded_moral',\n",
       " 'feature_livelong_sounded_squirm',\n",
       " 'feature_mandibulate_whining_localizer',\n",
       " 'feature_heliotropic_inane_papistry',\n",
       " 'feature_treed_nomographic_trivet',\n",
       " 'feature_sulkier_inebriated_sadduceeism',\n",
       " 'feature_distorted_bit_frilling',\n",
       " 'feature_subovate_deicidal_brett',\n",
       " 'feature_devoted_straggly_charlotte',\n",
       " 'feature_unlike_creeping_mammock',\n",
       " 'feature_antarthritic_rhinocerotic_marquette',\n",
       " 'feature_resupine_pectic_marathonian',\n",
       " 'feature_madrigalian_distichal_otranto',\n",
       " 'feature_systematic_nappiest_bruiser',\n",
       " 'feature_pinioned_desiccated_renegade',\n",
       " 'feature_aliped_subclavicular_nasopharynx',\n",
       " 'feature_branched_dilatory_sunbelt',\n",
       " 'feature_biochemical_searching_sodalite',\n",
       " 'feature_unsealed_suffixal_babar',\n",
       " 'feature_forficate_martensitic_huckaback',\n",
       " 'feature_silver_handworked_scauper',\n",
       " 'feature_interpenetrative_enabling_elm',\n",
       " 'feature_wholesale_contrived_caracole',\n",
       " 'feature_wry_bursal_treadler',\n",
       " 'feature_obverse_plumy_ultramontanism',\n",
       " 'feature_zoochemical_brimful_hector',\n",
       " 'feature_mouldiest_clostridial_paper',\n",
       " 'feature_unabolished_monographical_egestion',\n",
       " 'feature_hawaiian_adrenocorticotropic_right',\n",
       " 'feature_preservable_panchromatic_roadhouse',\n",
       " 'feature_pozzolanic_keltic_nymphomania',\n",
       " 'feature_spiroid_chuffy_dachshund',\n",
       " 'feature_prefab_stabbing_declivity',\n",
       " 'feature_lethal_stringy_substantial',\n",
       " 'feature_lowermost_tetrastichic_transvestism',\n",
       " 'feature_huge_neoclassic_crossbencher',\n",
       " 'feature_unsensualized_classier_professionalisation',\n",
       " 'feature_weldable_beribboned_margate',\n",
       " 'feature_alleviatory_novercal_semivowel',\n",
       " 'feature_dwarfish_equestrian_vaporizer',\n",
       " 'feature_limnological_proboscidean_nomologist',\n",
       " 'feature_unlocked_tetraethyl_mobility',\n",
       " 'feature_contralto_unblushing_beowulf',\n",
       " 'feature_shouted_weary_hagberry',\n",
       " 'feature_unwarrantable_egotistic_guayule',\n",
       " 'feature_canonical_dermatographic_mellowing',\n",
       " 'feature_unaired_operose_lactoprotein',\n",
       " 'feature_halting_disguisable_syllabicity',\n",
       " 'feature_aristotelian_eurasian_nooky',\n",
       " 'feature_titillative_pledged_breccia',\n",
       " 'feature_parallelism_later_triennial',\n",
       " 'feature_cupulate_mettlesome_extensimeter',\n",
       " 'feature_lowliest_oblatory_sprattle',\n",
       " 'feature_fluorometric_guarded_dioxin',\n",
       " 'feature_enorm_taboo_abysm',\n",
       " 'feature_compliable_buccaneerish_lyddite',\n",
       " 'feature_sulpha_wiring_ambiance',\n",
       " 'feature_faeroese_inapposite_plum',\n",
       " 'feature_fake_lentoid_helotism',\n",
       " 'feature_gardant_irreverent_inferiority',\n",
       " 'feature_munificent_stupid_hitlerism',\n",
       " 'feature_driving_neurasthenic_perimysium',\n",
       " 'feature_arborescent_fungal_despiser',\n",
       " 'feature_unbenign_unpromising_aglet',\n",
       " 'feature_conscionable_insouciant_pariah',\n",
       " 'feature_inconsequential_scincoid_sextant',\n",
       " 'feature_boyish_ductile_ninety',\n",
       " 'feature_spongiest_sunbeamed_connolly',\n",
       " 'feature_unbagged_parliamentary_plough',\n",
       " 'feature_unshingled_dotier_bose',\n",
       " 'feature_unfathered_roofed_taskmaster',\n",
       " 'feature_unwatered_sightable_hardtack',\n",
       " 'feature_teriyaki_splashy_inaptitude',\n",
       " 'feature_frumpy_squishiest_burley',\n",
       " 'feature_archaean_seething_hoyle',\n",
       " 'feature_subsidiary_enzymatic_hedonist',\n",
       " 'feature_oblate_quivery_bottrop',\n",
       " 'feature_multicostate_undescended_monoacid',\n",
       " 'feature_gradely_dippy_gaol',\n",
       " ...]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "lgb_all.feature_name_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-5fc463007f8b>: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-40-5fc463007f8b>: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-40-5fc463007f8b>: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-40-5fc463007f8b>: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": 40,
     "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": 41,
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.047100803265479066"
      ]
     },
     "execution_count": 42,
     "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05025499943392698"
      ]
     },
     "execution_count": 43,
     "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))"
   ]
  },
  {
   "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%"
   ]
  },
  {
   "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": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-44-73ffccb7a00f>:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df1 = df[eras<=eras.median()]\n",
      "<ipython-input-44-73ffccb7a00f>: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": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "eras = eras[eras.isin(np.arange(1, 575, 4))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": 47,
   "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": 48,
   "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": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold(n_splits=5, random_state=None, shuffle=False)\n",
      "0.04081357263615691\n",
      "\n",
      "KFold(n_splits=5, random_state=None, shuffle=True)\n",
      "0.05205511367759082\n",
      "\n",
      "GroupKFold(n_splits=5)\n",
      "0.04341132164952086\n",
      "\n",
      "TimeSeriesSplitGroups(n_splits=5)\n",
      "0.02598605396562758\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()"
   ]
  },
  {
   "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": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4997459543647697"
      ]
     },
     "execution_count": 50,
     "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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.34062985, -0.83573069,  0.28275667,  0.75927678],\n",
       "       [ 0.34062985,  1.        , -0.58813758,  0.17450404,  0.22026688],\n",
       "       [-0.83573069, -0.58813758,  1.        , -0.62462003, -0.8344854 ],\n",
       "       [ 0.28275667,  0.17450404, -0.62462003,  1.        ,  0.38212384],\n",
       "       [ 0.75927678,  0.22026688, -0.8344854 ,  0.38212384,  1.        ]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03504082804703481"
      ]
     },
     "execution_count": 52,
     "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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03469854472021291"
      ]
     },
     "execution_count": 53,
     "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)"
   ]
  },
  {
   "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": 54,
   "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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60     71000\n",
       "105    69251\n",
       "120    67610\n",
       "45     67268\n",
       "90     66300\n",
       "75     61933\n",
       "30     61333\n",
       "15     54214\n",
       "0      42920\n",
       "135    42282\n",
       "Name: erano, dtype: int64"
      ]
     },
     "execution_count": 55,
     "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": 56,
   "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": 57,
   "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>0.194921</td>\n",
       "      <td>0.043216</td>\n",
       "      <td>0.030027</td>\n",
       "      <td>0.016123</td>\n",
       "      <td>0.009126</td>\n",
       "      <td>0.023706</td>\n",
       "      <td>0.033789</td>\n",
       "      <td>0.022836</td>\n",
       "      <td>0.024734</td>\n",
       "      <td>0.020147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.046587</td>\n",
       "      <td>0.170537</td>\n",
       "      <td>0.029796</td>\n",
       "      <td>-0.007569</td>\n",
       "      <td>0.002888</td>\n",
       "      <td>0.001372</td>\n",
       "      <td>0.000648</td>\n",
       "      <td>0.001539</td>\n",
       "      <td>-0.007086</td>\n",
       "      <td>-0.005150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.039467</td>\n",
       "      <td>0.032860</td>\n",
       "      <td>0.156099</td>\n",
       "      <td>0.002623</td>\n",
       "      <td>0.003846</td>\n",
       "      <td>0.006211</td>\n",
       "      <td>0.005686</td>\n",
       "      <td>-0.001824</td>\n",
       "      <td>-0.000756</td>\n",
       "      <td>-0.002574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.023706</td>\n",
       "      <td>0.030027</td>\n",
       "      <td>0.035148</td>\n",
       "      <td>0.148862</td>\n",
       "      <td>0.010267</td>\n",
       "      <td>0.021071</td>\n",
       "      <td>0.027580</td>\n",
       "      <td>0.015904</td>\n",
       "      <td>0.031235</td>\n",
       "      <td>0.023599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.012483</td>\n",
       "      <td>0.006499</td>\n",
       "      <td>0.009025</td>\n",
       "      <td>0.016631</td>\n",
       "      <td>0.149721</td>\n",
       "      <td>0.019196</td>\n",
       "      <td>0.010640</td>\n",
       "      <td>0.013178</td>\n",
       "      <td>0.010896</td>\n",
       "      <td>0.011191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.025632</td>\n",
       "      <td>0.018699</td>\n",
       "      <td>0.031269</td>\n",
       "      <td>0.016043</td>\n",
       "      <td>0.018465</td>\n",
       "      <td>0.166712</td>\n",
       "      <td>0.026355</td>\n",
       "      <td>0.017058</td>\n",
       "      <td>0.021476</td>\n",
       "      <td>0.011319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.044772</td>\n",
       "      <td>0.025483</td>\n",
       "      <td>0.024666</td>\n",
       "      <td>0.024288</td>\n",
       "      <td>0.005159</td>\n",
       "      <td>0.029021</td>\n",
       "      <td>0.153414</td>\n",
       "      <td>0.024905</td>\n",
       "      <td>0.021045</td>\n",
       "      <td>0.016773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0.029213</td>\n",
       "      <td>0.027908</td>\n",
       "      <td>0.018279</td>\n",
       "      <td>0.012079</td>\n",
       "      <td>0.008206</td>\n",
       "      <td>0.016061</td>\n",
       "      <td>0.027251</td>\n",
       "      <td>0.143886</td>\n",
       "      <td>0.023365</td>\n",
       "      <td>0.009489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.033693</td>\n",
       "      <td>0.036589</td>\n",
       "      <td>0.018515</td>\n",
       "      <td>0.030186</td>\n",
       "      <td>0.007597</td>\n",
       "      <td>0.026739</td>\n",
       "      <td>0.023073</td>\n",
       "      <td>0.028707</td>\n",
       "      <td>0.144769</td>\n",
       "      <td>0.030703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>0.024917</td>\n",
       "      <td>0.012725</td>\n",
       "      <td>0.011641</td>\n",
       "      <td>0.019961</td>\n",
       "      <td>0.004650</td>\n",
       "      <td>0.011544</td>\n",
       "      <td>0.013249</td>\n",
       "      <td>0.010797</td>\n",
       "      <td>0.025249</td>\n",
       "      <td>0.184200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "test_era        0         15        30        45        60        75   \\\n",
       "train_era                                                               \n",
       "0          0.194921  0.043216  0.030027  0.016123  0.009126  0.023706   \n",
       "15         0.046587  0.170537  0.029796 -0.007569  0.002888  0.001372   \n",
       "30         0.039467  0.032860  0.156099  0.002623  0.003846  0.006211   \n",
       "45         0.023706  0.030027  0.035148  0.148862  0.010267  0.021071   \n",
       "60         0.012483  0.006499  0.009025  0.016631  0.149721  0.019196   \n",
       "75         0.025632  0.018699  0.031269  0.016043  0.018465  0.166712   \n",
       "90         0.044772  0.025483  0.024666  0.024288  0.005159  0.029021   \n",
       "105        0.029213  0.027908  0.018279  0.012079  0.008206  0.016061   \n",
       "120        0.033693  0.036589  0.018515  0.030186  0.007597  0.026739   \n",
       "135        0.024917  0.012725  0.011641  0.019961  0.004650  0.011544   \n",
       "\n",
       "test_era        90        105       120       135  \n",
       "train_era                                          \n",
       "0          0.033789  0.022836  0.024734  0.020147  \n",
       "15         0.000648  0.001539 -0.007086 -0.005150  \n",
       "30         0.005686 -0.001824 -0.000756 -0.002574  \n",
       "45         0.027580  0.015904  0.031235  0.023599  \n",
       "60         0.010640  0.013178  0.010896  0.011191  \n",
       "75         0.026355  0.017058  0.021476  0.011319  \n",
       "90         0.153414  0.024905  0.021045  0.016773  \n",
       "105        0.027251  0.143886  0.023365  0.009489  \n",
       "120        0.023073  0.028707  0.144769  0.030703  \n",
       "135        0.013249  0.010797  0.025249  0.184200  "
      ]
     },
     "execution_count": 57,
     "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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2ab6141a60>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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\")"
   ]
  },
  {
   "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"
   ]
  },
  {
   "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": 59,
   "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": 60,
   "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": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.395800 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 5250\n",
      "[LightGBM] [Info] Number of data points in the train set: 305247, number of used features: 1050\n",
      "[LightGBM] [Info] Start training from score 0.499998\n",
      "[20]\ttrain's l2: 0.0498386\ttrain's score: -0.0696572\ttest's l2: 0.0499211\ttest's score: -0.042272\n",
      "[40]\ttrain's l2: 0.0497466\ttrain's score: -0.0845498\ttest's l2: 0.0499021\ttest's score: -0.0449487\n",
      "[60]\ttrain's l2: 0.0496736\ttrain's score: -0.0950992\ttest's l2: 0.049894\ttest's score: -0.0462429\n",
      "[80]\ttrain's l2: 0.0496094\ttrain's score: -0.103748\ttest's l2: 0.0498883\ttest's score: -0.0474028\n",
      "[100]\ttrain's l2: 0.0495503\ttrain's score: -0.111415\ttest's l2: 0.049883\ttest's score: -0.0485958\n",
      "[120]\ttrain's l2: 0.0494963\ttrain's score: -0.118318\ttest's l2: 0.0498832\ttest's score: -0.0487773\n",
      "[140]\ttrain's l2: 0.0494427\ttrain's score: -0.124419\ttest's l2: 0.0498826\ttest's score: -0.0492182\n",
      "[160]\ttrain's l2: 0.0493929\ttrain's score: -0.12975\ttest's l2: 0.0498849\ttest's score: -0.0492456\n",
      "[180]\ttrain's l2: 0.0493438\ttrain's score: -0.135098\ttest's l2: 0.0498857\ttest's score: -0.0495008\n",
      "[200]\ttrain's l2: 0.0492992\ttrain's score: -0.139802\ttest's l2: 0.0498864\ttest's score: -0.0497836\n",
      "[220]\ttrain's l2: 0.0492556\ttrain's score: -0.144227\ttest's l2: 0.049888\ttest's score: -0.0499265\n",
      "[240]\ttrain's l2: 0.0492132\ttrain's score: -0.148465\ttest's l2: 0.0498893\ttest's score: -0.0501424\n",
      "[260]\ttrain's l2: 0.0491713\ttrain's score: -0.152511\ttest's l2: 0.0498917\ttest's score: -0.0502095\n",
      "[280]\ttrain's l2: 0.0491288\ttrain's score: -0.156449\ttest's l2: 0.0498947\ttest's score: -0.0502408\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[300]\ttrain's l2: 0.0490897\ttrain's score: -0.160258\ttest's l2: 0.0498965\ttest's score: -0.0503664\n",
      "[320]\ttrain's l2: 0.0490502\ttrain's score: -0.163728\ttest's l2: 0.0498975\ttest's score: -0.0507012\n",
      "[340]\ttrain's l2: 0.0490115\ttrain's score: -0.167201\ttest's l2: 0.0499019\ttest's score: -0.0505273\n",
      "[360]\ttrain's l2: 0.0489723\ttrain's score: -0.170573\ttest's l2: 0.0499006\ttest's score: -0.051169\n",
      "[380]\ttrain's l2: 0.0489356\ttrain's score: -0.173804\ttest's l2: 0.0499038\ttest's score: -0.0510981\n",
      "[400]\ttrain's l2: 0.0488994\ttrain's score: -0.177039\ttest's l2: 0.0499082\ttest's score: -0.0509207\n",
      "[420]\ttrain's l2: 0.0488637\ttrain's score: -0.180099\ttest's l2: 0.0499135\ttest's score: -0.0505648\n",
      "[440]\ttrain's l2: 0.0488284\ttrain's score: -0.182793\ttest's l2: 0.0499169\ttest's score: -0.0505659\n",
      "[460]\ttrain's l2: 0.0487914\ttrain's score: -0.186067\ttest's l2: 0.0499181\ttest's score: -0.0508391\n",
      "[480]\ttrain's l2: 0.0487565\ttrain's score: -0.18906\ttest's l2: 0.0499207\ttest's score: -0.0508337\n",
      "[500]\ttrain's l2: 0.048721\ttrain's score: -0.192284\ttest's l2: 0.0499246\ttest's score: -0.0506581\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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])"
   ]
  },
  {
   "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": 62,
   "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": 63,
   "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": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --  LinearRegression()\n",
      "outsample: 0.03449532642406725, insample: 0.08811577962731876\n",
      "\n",
      " --  ElasticNet(alpha=0.01)\n",
      "outsample: -0.0005817296572705758, insample: 0.0011047675627246087\n",
      "\n",
      " --  ElasticNet(alpha=0.005)\n",
      "outsample: -0.0005817296572705758, insample: 0.0011047675627246087\n",
      "\n",
      " --  ElasticNet(alpha=0.002)\n",
      "outsample: 0.028356554996391864, insample: 0.036860395887039446\n",
      "\n",
      " --  ElasticNet(alpha=0.001)\n",
      "outsample: 0.03991189570044001, insample: 0.05365003421490132\n",
      "\n",
      " --  ElasticNet(alpha=0.0005)\n",
      "outsample: 0.04275518297522411, insample: 0.06223910243312713\n",
      "\n",
      " --  ElasticNet(alpha=0.0002)\n",
      "outsample: 0.04408886141392253, insample: 0.07101617648855875\n",
      "\n",
      " --  ElasticNet(alpha=0.0001)\n",
      "outsample: 0.042497050882822206, insample: 0.07683662591705001\n",
      "\n",
      " --  ElasticNet(alpha=5e-05)\n",
      "outsample: 0.04031370910547076, insample: 0.0826422949678376\n",
      "\n",
      " --  ElasticNet(alpha=2e-05)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/michael/anaconda3/envs/xgb/lib/python3.8/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: 187.98899841308594, tolerance: 1.5261499881744385\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outsample: 0.03723017817413682, insample: 0.08677474150444352\n",
      "\n",
      " --  ElasticNet(alpha=1e-05)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/michael/anaconda3/envs/xgb/lib/python3.8/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: 759.37939453125, tolerance: 1.5261499881744385\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outsample: 0.03591553345108072, insample: 0.08774837309606072\n",
      "\n",
      " --  LGBMRegressor()\n",
      "outsample: 0.04163519344042179, insample: 0.2413164724798182\n",
      "\n",
      " --  LGBMRegressor(learning_rate=0.01, n_estimators=1000)\n",
      "outsample: 0.04716206072784002, insample: 0.27980942641432893\n",
      "\n",
      " --  LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, n_estimators=1000)\n",
      "outsample: 0.049906177599943716, insample: 0.2486898095456977\n",
      "\n",
      " --  LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
      "              n_estimators=1000)\n",
      "outsample: 0.051584782947410084, insample: 0.20542516882601236\n",
      "\n",
      " --  LGBMRegressor(colsample_bytree=0.1, learning_rate=0.001, max_depth=5,\n",
      "              n_estimators=10000)\n",
      "outsample: 0.05228707325079221, insample: 0.2077346244025685\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()"
   ]
  },
  {
   "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": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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])"
   ]
  },
  {
   "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": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1050.000000\n",
       "mean        0.007197\n",
       "std         0.084903\n",
       "min        -0.431369\n",
       "25%        -0.048999\n",
       "50%         0.008305\n",
       "75%         0.065700\n",
       "max         0.195975\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "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": 68,
   "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": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-69-4aa87b479082>: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-69-4aa87b479082>: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-69-4aa87b479082>: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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now our single feature exposures are much smaller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1050.000000\n",
       "mean        0.005275\n",
       "std         0.062161\n",
       "min        -0.314072\n",
       "25%        -0.035640\n",
       "50%         0.006457\n",
       "75%         0.047849\n",
       "max         0.144363\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "corr_series2.describe()"
   ]
  },
  {
   "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": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score for high feature exposure: 0.05233661570835508\n",
      "score for balanced feature expo: 0.04681538564864233\n",
      "std for high feature exposure: 0.022859528419203096\n",
      "std for balanced feature expo: 0.019806139929550184\n",
      "sharpe for high feature exposure: 2.2894879871795526\n",
      "sharpe for balanced feature expo: 2.3636804453145936\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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    67.000000\n",
       "mean      0.046815\n",
       "std       0.019956\n",
       "min       0.002495\n",
       "25%       0.032911\n",
       "50%       0.047356\n",
       "75%       0.058174\n",
       "max       0.105836\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "balanced_scores_per_era.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    67.000000\n",
       "mean      0.052337\n",
       "std       0.023032\n",
       "min       0.004386\n",
       "25%       0.035233\n",
       "50%       0.055631\n",
       "75%       0.065317\n",
       "max       0.116914\n",
       "dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "unbalanced_scores_per_era.describe()"
   ]
  },
  {
   "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": 75,
   "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": 76,
   "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": 77,
   "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": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0573\r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-78-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-78-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"
   ]
  },
  {
   "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": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score for high feature exposure: 0.05233661570835508\n",
      "score for balanced feature expo: 0.04784620200240574\n",
      "std for high feature exposure: 0.022859528419203096\n",
      "std for balanced feature expo: 0.01865889557683214\n",
      "sharpe for high feature exposure: 2.2894879871795526\n",
      "sharpe for balanced feature expo: 2.5642569146382965\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)}\")"
   ]
  },
  {
   "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": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1050.000000\n",
       "mean        0.010188\n",
       "std         0.058860\n",
       "min        -0.269052\n",
       "25%        -0.029323\n",
       "50%         0.012539\n",
       "75%         0.050637\n",
       "max         0.137796\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "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": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1050.000000\n",
       "mean        0.010188\n",
       "std         0.058860\n",
       "min        -0.269052\n",
       "25%        -0.029323\n",
       "50%         0.012539\n",
       "75%         0.050637\n",
       "max         0.137796\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "corr_series3.describe()"
   ]
  },
  {
   "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": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(learning_rate=0.01, max_depth=5, n_estimators=1000)"
      ]
     },
     "execution_count": 82,
     "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": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(learning_rate=0.01, max_depth=5, n_estimators=1000)"
      ]
     },
     "execution_count": 83,
     "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_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 63% correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6270969845588339\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "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": 86,
   "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": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7510865174862386"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# The predictions are about 75% correlated\n",
    "np.corrcoef([lgb_preds, lgb_jerome_preds])[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-88-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-88-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": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0573\r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-89-867f2bde3bb9>: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": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-90-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": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score for target: 0.05233661570835508\n",
      "score for target_jerome: 0.05293083310575564\n",
      "\n",
      "sharpe for target: 2.2894879871795526\n",
      "sharpe for target_jerome: 1.9501765858045648\n",
      "\n",
      "score for target after neutralization: 0.04784620200240574\n",
      "score for target_jerome after neutralization: 0.050838690600204337\n",
      "\n",
      "sharpe for target after neutralization: 2.5642569146382965\n",
      "sharpe for target_jerome after neutralization: 2.2734387549255732\n",
      "\n",
      "score for target + target_jerome ensemble: 0.05627261521740282\n",
      "sharpe for target + target_jerome ensemble: 2.1496760967783635\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"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate correlation of neutralized predictions\n",
    "df2[[f\"preds_neutralized_riskiest_100\", f\"preds_jerome_neutralized_riskiest_100\"]].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Given that the neutralized version trained on `target_jerome` gets slightly better average score, but is only about 67% 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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