Final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0f561c72",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import Binarizer\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a01a843f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>size</th>\n",
       "      <th>type</th>\n",
       "      <th>alignment</th>\n",
       "      <th>languages</th>\n",
       "      <th>ac</th>\n",
       "      <th>hp</th>\n",
       "      <th>cr</th>\n",
       "      <th>speed</th>\n",
       "      <th>swim</th>\n",
       "      <th>...</th>\n",
       "      <th>religion</th>\n",
       "      <th>acrobatics</th>\n",
       "      <th>athletics</th>\n",
       "      <th>intimidation</th>\n",
       "      <th>senses</th>\n",
       "      <th>attributes</th>\n",
       "      <th>actions</th>\n",
       "      <th>legendary_actions</th>\n",
       "      <th>legendary</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aarakocra</td>\n",
       "      <td>medium</td>\n",
       "      <td>humanoid (aarakocra)</td>\n",
       "      <td>neutral good</td>\n",
       "      <td>Auran, Aarakocra</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.25</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>passive Perception 15</td>\n",
       "      <td>Dive Attack. If the aarakocra is flying and di...</td>\n",
       "      <td>Talon. Melee Weapon Attack: +4 to hit, reach 5...</td>\n",
       "      <td>none</td>\n",
       "      <td>0</td>\n",
       "      <td>Monster Manual (BR)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abjurer</td>\n",
       "      <td>medium</td>\n",
       "      <td>humanoid (any race)</td>\n",
       "      <td>any alignment</td>\n",
       "      <td>any four languages</td>\n",
       "      <td>12.0</td>\n",
       "      <td>84</td>\n",
       "      <td>9.00</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>passive Perception 11</td>\n",
       "      <td>unavailable</td>\n",
       "      <td>unavailable</td>\n",
       "      <td>unavailable</td>\n",
       "      <td>0</td>\n",
       "      <td>Volo's Guide to Monsters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aboleth</td>\n",
       "      <td>large</td>\n",
       "      <td>aberration</td>\n",
       "      <td>lawful evil</td>\n",
       "      <td>Deep Speech, telepathy 120 ft.</td>\n",
       "      <td>17.0</td>\n",
       "      <td>135</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>darkvision 120 ft., passive Perception 20</td>\n",
       "      <td>Amphibious. The aboleth can breathe air and wa...</td>\n",
       "      <td>Multiattack. The aboleth makes three tentacle ...</td>\n",
       "      <td>The aboleth can take 3 legendary actions, choo...</td>\n",
       "      <td>1</td>\n",
       "      <td>Monster Manual (SRD)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abominable-yeti</td>\n",
       "      <td>huge</td>\n",
       "      <td>monstrosity</td>\n",
       "      <td>chaotic evil</td>\n",
       "      <td>Yeti</td>\n",
       "      <td>15.0</td>\n",
       "      <td>137</td>\n",
       "      <td>9.00</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>darkvision 60 ft., passive Perception 15</td>\n",
       "      <td>unavailable</td>\n",
       "      <td>unavailable</td>\n",
       "      <td>unavailable</td>\n",
       "      <td>0</td>\n",
       "      <td>Monster Manual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>acererak</td>\n",
       "      <td>medium</td>\n",
       "      <td>undead</td>\n",
       "      <td>neutral evil</td>\n",
       "      <td>Abyssal, Common, Draconic, Dwarvish, Elvish, G...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>285</td>\n",
       "      <td>23.00</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>truesight 120 ft., passive Perception 22</td>\n",
       "      <td>unavailable</td>\n",
       "      <td>unavailable</td>\n",
       "      <td>unavailable</td>\n",
       "      <td>0</td>\n",
       "      <td>Adventures (Tomb of Annihilation)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name    size                  type      alignment  \\\n",
       "0        aarakocra  medium  humanoid (aarakocra)   neutral good   \n",
       "1          abjurer  medium   humanoid (any race)  any alignment   \n",
       "2          aboleth   large            aberration    lawful evil   \n",
       "3  abominable-yeti    huge           monstrosity   chaotic evil   \n",
       "4         acererak  medium                undead   neutral evil   \n",
       "\n",
       "                                           languages    ac   hp     cr  speed  \\\n",
       "0                                   Auran, Aarakocra  12.0   13   0.25     20   \n",
       "1                                 any four languages  12.0   84   9.00     30   \n",
       "2                     Deep Speech, telepathy 120 ft.  17.0  135  10.00     10   \n",
       "3                                               Yeti  15.0  137   9.00     40   \n",
       "4  Abyssal, Common, Draconic, Dwarvish, Elvish, G...  21.0  285  23.00     30   \n",
       "\n",
       "   swim  ...  religion  acrobatics  athletics  intimidation  \\\n",
       "0     0  ...         0           0          0             0   \n",
       "1     0  ...         0           0          0             0   \n",
       "2    40  ...         0           0          0             0   \n",
       "3     0  ...         0           0          0             0   \n",
       "4     0  ...        15           0          0             0   \n",
       "\n",
       "                                      senses  \\\n",
       "0                      passive Perception 15   \n",
       "1                      passive Perception 11   \n",
       "2  darkvision 120 ft., passive Perception 20   \n",
       "3   darkvision 60 ft., passive Perception 15   \n",
       "4   truesight 120 ft., passive Perception 22   \n",
       "\n",
       "                                          attributes  \\\n",
       "0  Dive Attack. If the aarakocra is flying and di...   \n",
       "1                                        unavailable   \n",
       "2  Amphibious. The aboleth can breathe air and wa...   \n",
       "3                                        unavailable   \n",
       "4                                        unavailable   \n",
       "\n",
       "                                             actions  \\\n",
       "0  Talon. Melee Weapon Attack: +4 to hit, reach 5...   \n",
       "1                                        unavailable   \n",
       "2  Multiattack. The aboleth makes three tentacle ...   \n",
       "3                                        unavailable   \n",
       "4                                        unavailable   \n",
       "\n",
       "                                   legendary_actions  legendary  \\\n",
       "0                                               none          0   \n",
       "1                                        unavailable          0   \n",
       "2  The aboleth can take 3 legendary actions, choo...          1   \n",
       "3                                        unavailable          0   \n",
       "4                                        unavailable          0   \n",
       "\n",
       "                              source  \n",
       "0                Monster Manual (BR)  \n",
       "1           Volo's Guide to Monsters  \n",
       "2               Monster Manual (SRD)  \n",
       "3                     Monster Manual  \n",
       "4  Adventures (Tomb of Annihilation)  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dnd_data = pd.read_csv(\"dnd.csv\")\n",
    "\n",
    "dnd_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "35d83514",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>size</th>\n",
       "      <th>type</th>\n",
       "      <th>align</th>\n",
       "      <th>ac</th>\n",
       "      <th>hp</th>\n",
       "      <th>cr</th>\n",
       "      <th>str</th>\n",
       "      <th>dex</th>\n",
       "      <th>con</th>\n",
       "      <th>int</th>\n",
       "      <th>wis</th>\n",
       "      <th>cha</th>\n",
       "      <th>legen</th>\n",
       "      <th>law</th>\n",
       "      <th>moral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aarakocra</td>\n",
       "      <td>medium</td>\n",
       "      <td>humanoid</td>\n",
       "      <td>neutral good</td>\n",
       "      <td>12</td>\n",
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       "      <td>good</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abjurer</td>\n",
       "      <td>medium</td>\n",
       "      <td>humanoid</td>\n",
       "      <td>any any</td>\n",
       "      <td>12</td>\n",
       "      <td>84</td>\n",
       "      <td>9</td>\n",
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       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>any</td>\n",
       "      <td>any</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aboleth</td>\n",
       "      <td>large</td>\n",
       "      <td>aberration</td>\n",
       "      <td>lawful evil</td>\n",
       "      <td>17</td>\n",
       "      <td>135</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>lawful</td>\n",
       "      <td>evil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abominable-yeti</td>\n",
       "      <td>huge</td>\n",
       "      <td>monstrosity</td>\n",
       "      <td>chaotic evil</td>\n",
       "      <td>15</td>\n",
       "      <td>137</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>chaotic</td>\n",
       "      <td>evil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>acererak</td>\n",
       "      <td>medium</td>\n",
       "      <td>undead</td>\n",
       "      <td>neutral evil</td>\n",
       "      <td>21</td>\n",
       "      <td>285</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>evil</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name    size         type         align  ac   hp  cr  str  dex  \\\n",
       "0        aarakocra  medium     humanoid  neutral good  12   13   0   10   14   \n",
       "1          abjurer  medium     humanoid       any any  12   84   9    9   14   \n",
       "2          aboleth   large   aberration   lawful evil  17  135  10   21    9   \n",
       "3  abominable-yeti    huge  monstrosity  chaotic evil  15  137   9   24   10   \n",
       "4         acererak  medium       undead  neutral evil  21  285  23   13   16   \n",
       "\n",
       "   con  int  wis  cha  legen      law moral  \n",
       "0   10   11   12   11      0  neutral  good  \n",
       "1   14   18   12   11      0      any   any  \n",
       "2   15   18   15   18      1   lawful  evil  \n",
       "3   22    9   13    9      0  chaotic  evil  \n",
       "4   20   27   21   20      0  neutral  evil  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove extra columns.\n",
    "to_drop = [\n",
    "    \"languages\", \"speed\", \"swim\", \"fly\", \"climb\", \"burrow\",\n",
    "    \"passive_perception\", \"darkvision\", \"truesight\", \"tremorsense\", \"blindsight\",\n",
    "    \"str_mod\", \"dex_mod\", \"con_mod\", \"int_mod\", \"wis_mod\", \"cha_mod\",\n",
    "    \"str_save\", \"dex_save\", \"con_save\", \"int_save\", \"wis_save\", \"cha_save\",\n",
    "    \"history\", \"perception\", \"stealth\", \"persuasion\", \"insight\", \"deception\", \"arcana\", \"religion\", \"acrobatics\", \"athletics\", \"intimidation\",\n",
    "    \"senses\", \"attributes\", \"actions\", \"legendary_actions\", \"source\",\n",
    "]\n",
    "dnd_clean = dnd_data.drop(to_drop, axis=1)\n",
    "\n",
    "# Clean the creature types.\n",
    "for index_itr in dnd_clean.index:\n",
    "    test = dnd_clean.loc[index_itr, \"type\"].split(\" (\", 1)[0]\n",
    "    dnd_clean.loc[index_itr, \"type\"] = test\n",
    "\n",
    "# Turn the CR floats to ints.\n",
    "for index_itr in dnd_clean.index:\n",
    "    dnd_clean.loc[index_itr, \"cr\"] = math.floor(dnd_clean.loc[index_itr, \"cr\"])\n",
    "dnd_clean[\"cr\"] = dnd_clean[\"cr\"].astype(int)\n",
    "\n",
    "# Split the alignments.\n",
    "algn_map = {\n",
    "    \"any alignment\": (\"any\", \"any\"),\n",
    "    \"any chaotic alignment\": (\"chaotic\", \"any\"),\n",
    "    \"any evil alignment\": (\"any\", \"evil\"),\n",
    "    \"any non-good alignment\": (\"any\", \"non-good\"),\n",
    "    \"any non-lawful alignment\": (\"non-lawful\", \"any\"),\n",
    "    \"chaotic good (75 %) or neutral evil (25 %)\": (\"non-lawful\", \"non-neutral\"),\n",
    "    \"neutral good (50 %) or neutral evil (50 %)\": (\"neutral\", \"non-neutral\"),\n",
    "}\n",
    "for index_itr in dnd_clean.index:\n",
    "    if dnd_clean.loc[index_itr, \"alignment\"] in algn_map.keys():\n",
    "        dnd_clean.loc[index_itr, \"law\"] = algn_map[dnd_clean.loc[index_itr, \"alignment\"]][0]\n",
    "        dnd_clean.loc[index_itr, \"moral\"] = algn_map[dnd_clean.loc[index_itr, \"alignment\"]][1]\n",
    "    elif \" \" in dnd_clean.loc[index_itr, \"alignment\"]:\n",
    "        dnd_clean.loc[index_itr, \"law\"] = dnd_clean.loc[index_itr, \"alignment\"].split(\" \")[0]\n",
    "        dnd_clean.loc[index_itr, \"moral\"] = dnd_clean.loc[index_itr, \"alignment\"].split(\" \")[1]\n",
    "    else:\n",
    "        dnd_clean.loc[index_itr, \"law\"] = dnd_clean.loc[index_itr, \"alignment\"]\n",
    "        dnd_clean.loc[index_itr, \"moral\"] = dnd_clean.loc[index_itr, \"alignment\"]\n",
    "        \n",
    "# Reset alignment to new alignment types.\n",
    "for index_itr in dnd_clean.index:\n",
    "    dnd_clean.loc[index_itr, \"alignment\"] = dnd_clean.loc[index_itr, \"law\"] + \" \" + dnd_clean.loc[index_itr, \"moral\"]\n",
    "\n",
    "# Turn the AC floats to ints.\n",
    "dnd_clean[\"ac\"] = dnd_clean[\"ac\"].astype(int)\n",
    "\n",
    "# Rename columns.\n",
    "dnd_clean = dnd_clean.rename(columns={\"alignment\": \"align\", \"intel\": \"int\", \"legendary\": \"legen\", \"strength\": \"str\"})    \n",
    "\n",
    "dnd_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21e52500",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>name</th>\n",
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       "      <th>moral</th>\n",
       "      <th>nalign</th>\n",
       "      <th>nsize</th>\n",
       "      <th>ntype</th>\n",
       "      <th>nlaw</th>\n",
       "      <th>nmoral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aarakocra</td>\n",
       "      <td>medium</td>\n",
       "      <td>humanoid</td>\n",
       "      <td>neutral good</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>good</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abjurer</td>\n",
       "      <td>medium</td>\n",
       "      <td>humanoid</td>\n",
       "      <td>any any</td>\n",
       "      <td>12</td>\n",
       "      <td>84</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>any</td>\n",
       "      <td>any</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aboleth</td>\n",
       "      <td>large</td>\n",
       "      <td>aberration</td>\n",
       "      <td>lawful evil</td>\n",
       "      <td>17</td>\n",
       "      <td>135</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>lawful</td>\n",
       "      <td>evil</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abominable-yeti</td>\n",
       "      <td>huge</td>\n",
       "      <td>monstrosity</td>\n",
       "      <td>chaotic evil</td>\n",
       "      <td>15</td>\n",
       "      <td>137</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>chaotic</td>\n",
       "      <td>evil</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>acererak</td>\n",
       "      <td>medium</td>\n",
       "      <td>undead</td>\n",
       "      <td>neutral evil</td>\n",
       "      <td>21</td>\n",
       "      <td>285</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>evil</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name    size         type         align  ac   hp  cr  str  dex  \\\n",
       "0        aarakocra  medium     humanoid  neutral good  12   13   0   10   14   \n",
       "1          abjurer  medium     humanoid       any any  12   84   9    9   14   \n",
       "2          aboleth   large   aberration   lawful evil  17  135  10   21    9   \n",
       "3  abominable-yeti    huge  monstrosity  chaotic evil  15  137   9   24   10   \n",
       "4         acererak  medium       undead  neutral evil  21  285  23   13   16   \n",
       "\n",
       "   con  ...  wis  cha  legen      law moral nalign  nsize  ntype  nlaw  nmoral  \n",
       "0   10  ...   12   11      0  neutral  good     10      0      6     3       4  \n",
       "1   14  ...   12   11      0      any   any      1      0      6     2       0  \n",
       "2   15  ...   15   18      1   lawful  evil      5      4      5     5       6  \n",
       "3   22  ...   13    9      0  chaotic  evil      4      3      9     0       6  \n",
       "4   20  ...   21   20      0  neutral  evil      6      0      8     3       6  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def uniq_list(target_col):\n",
    "    return list(set(list(dnd_clean[target_col])))\n",
    "\n",
    "# Create an ordinal size column for size and type.\n",
    "uniq_align = uniq_list(\"align\")\n",
    "uniq_size = uniq_list(\"size\")\n",
    "uniq_type = uniq_list(\"type\")\n",
    "uniq_law = uniq_list(\"law\")\n",
    "uniq_moral = uniq_list(\"moral\")\n",
    "\n",
    "for index_itr in dnd_clean.index:\n",
    "    dnd_clean.loc[index_itr, \"nalign\"] = uniq_align.index(dnd_clean.loc[index_itr, \"align\"])\n",
    "    dnd_clean.loc[index_itr, \"nsize\"] = uniq_size.index(dnd_clean.loc[index_itr, \"size\"])\n",
    "    dnd_clean.loc[index_itr, \"ntype\"] = uniq_type.index(dnd_clean.loc[index_itr, \"type\"])\n",
    "    dnd_clean.loc[index_itr, \"nlaw\"] = uniq_law.index(dnd_clean.loc[index_itr, \"law\"])\n",
    "    dnd_clean.loc[index_itr, \"nmoral\"] = uniq_moral.index(dnd_clean.loc[index_itr, \"moral\"])\n",
    "\n",
    "dnd_clean[\"nalign\"] = dnd_clean[\"nalign\"].astype(np.uint8)\n",
    "dnd_clean[\"nsize\"] = dnd_clean[\"nsize\"].astype(np.uint8)\n",
    "dnd_clean[\"ntype\"] = dnd_clean[\"ntype\"].astype(np.uint8)\n",
    "dnd_clean[\"nlaw\"] = dnd_clean[\"nlaw\"].astype(np.uint8)\n",
    "dnd_clean[\"nmoral\"] = dnd_clean[\"nmoral\"].astype(np.uint8)\n",
    "\n",
    "dnd_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9a0616e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('binirizer', Binarizer())\n",
    "])\n",
    "\n",
    "# Get the list without the item.\n",
    "def not_target(not_get):\n",
    "    default_list = [\"ac\", \"hp\", \"cr\", \"str\", \"dex\", \"con\", \"int\", \"wis\", \"cha\", \"legen\", \"nsize\", \"ntype\", \"nlaw\", \"nmoral\"]\n",
    "    # Account for cheaty data.\n",
    "    if not_get == \"nalign\":\n",
    "        default_list.remove(\"nlaw\")\n",
    "        default_list.remove(\"nmoral\")\n",
    "    else:\n",
    "        default_list.remove(not_get)\n",
    "    return default_list\n",
    "\n",
    "# Get all columns not the target, and the target, and split.\n",
    "def dnd_prep(target_get):\n",
    "    dnd_x = dnd_clean[dnd_clean.columns & not_target(target_get)].to_numpy()\n",
    "    dnd_y = dnd_clean[target_get].to_numpy()\n",
    "    dnd_xtrain, dnd_xtest, dnd_ytrain, dnd_ytest = train_test_split(dnd_x, dnd_y, test_size=0.1, random_state=42)\n",
    "    return pipe.fit_transform(dnd_xtrain), pipe.fit_transform(dnd_xtest), dnd_ytrain, dnd_ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "317dcbcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train 4 various models on the target stat and evaluate.\n",
    "def giga_grid(target_get):\n",
    "    dnd_xtrain, dnd_xtest, dnd_ytrain, dnd_ytest = dnd_prep(target_get)\n",
    "    \n",
    "    knc_grid = [{\"weights\":[\"uniform\", \"distance\"], \"metric\":[\"euclidean\", \"manhattan\"], \"algorithm\":[\"ball_tree\", \"kd_tree\", \"brute\"], \"leaf_size\":[30, 40, 50, 60]}]\n",
    "    knc_model = KNeighborsClassifier()\n",
    "    knc_search = GridSearchCV(knc_model, knc_grid, cv=5, scoring=\"accuracy\")\n",
    "    \n",
    "    rfc_grid = [{\"criterion\":[\"gini\", \"entropy\"], \"n_estimators\":[25, 50, 75, 100], \"max_depth\":[10, 15, 20, 25], \"min_samples_split\":[4, 6, 8, 10]}]\n",
    "    rfc_model = RandomForestClassifier()\n",
    "    rfc_search = GridSearchCV(rfc_model, rfc_grid, cv=5, scoring=\"accuracy\")\n",
    "    \n",
    "    sgd_grid = [{\"penalty\":[\"l2\", \"l1\", \"elasticnet\"], \"loss\":[\"log\", \"modified_huber\"], \"alpha\":[1e-4, 1e-3, 1e-2, 1e-1, 1e0]}]\n",
    "    sgd_model = SGDClassifier()\n",
    "    sgd_search = GridSearchCV(sgd_model, sgd_grid, cv=5, scoring=\"accuracy\")\n",
    "    \n",
    "    svc_grid = [{\"kernel\":[\"linear\", \"rbf\", \"sigmoid\"], \"C\":[0.1, 1, 10, 100, 1000], \"gamma\":[1, 0.1, 0.01, 0.001, 0.0001]}]\n",
    "    svc_model = SVC(probability=True)\n",
    "    svc_search = GridSearchCV(svc_model, svc_grid, cv=5, scoring=\"accuracy\")\n",
    "    \n",
    "    model_list = [rfc_search, knc_search, sgd_search, svc_search]\n",
    "    model_best, score_best = [], []\n",
    "    \n",
    "    for itr_model in model_list:\n",
    "        itr_model.fit(dnd_xtrain, dnd_ytrain)\n",
    "        model_best.append(itr_model.best_estimator_)\n",
    "        score_best.append(itr_model.best_score_)\n",
    "        \n",
    "    return model_best, score_best, [dnd_xtrain, dnd_xtest, dnd_ytrain, dnd_ytest]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8fcf582f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##############################   Testing against: ac   ##############################\n",
      "\n",
      "[0.28029197080291973, 0.23649635036496347, 0.2525547445255475, 0.27007299270072993]\n",
      "[RandomForestClassifier(criterion='entropy', max_depth=10, min_samples_split=10,\n",
      "                       n_estimators=50), KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, metric='euclidean'), SGDClassifier(alpha=0.001, loss='log'), SVC(C=1, gamma=0.1, probability=True)]\n",
      "\n",
      "RandomForestClassifier(criterion='entropy', max_depth=10, min_samples_split=10,\n",
      "                       n_estimators=50)\n",
      "Best model accuracy: 28.03%\n",
      "Accuracy against unseen data: 22.08%\n",
      "Top 3 accuracy against unseen data: 24.68%\n",
      "\n",
      "##############################   Testing against: hp   ##############################\n",
      "\n",
      "[0.08759124087591241, 0.06569343065693431, 0.10218978102189782, 0.10510948905109489]\n",
      "[RandomForestClassifier(max_depth=10, min_samples_split=10, n_estimators=75), KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, metric='manhattan',\n",
      "                     weights='distance'), SGDClassifier(alpha=0.001, loss='log', penalty='l1'), SVC(C=1000, gamma=0.001, kernel='sigmoid', probability=True)]\n",
      "\n",
      "SVC(C=1000, gamma=0.001, kernel='sigmoid', probability=True)\n",
      "Best model accuracy: 10.51%\n",
      "Accuracy against unseen data: 12.99%\n",
      "Top 3 accuracy against unseen data: 16.88%\n",
      "\n",
      "##############################   Testing against: cr   ##############################\n",
      "\n",
      "[0.3693430656934307, 0.34160583941605843, 0.33138686131386863, 0.3474452554744526]\n",
      "[RandomForestClassifier(max_depth=10, min_samples_split=8), KNeighborsClassifier(algorithm='kd_tree', metric='euclidean'), SGDClassifier(alpha=0.001, loss='log', penalty='elasticnet'), SVC(C=1, gamma=1, probability=True)]\n",
      "\n",
      "RandomForestClassifier(max_depth=10, min_samples_split=8)\n",
      "Best model accuracy: 36.93%\n",
      "Accuracy against unseen data: 38.96%\n",
      "Top 3 accuracy against unseen data: 33.77%\n",
      "\n",
      "##############################   Testing against: str   ##############################\n",
      "\n",
      "[0.15766423357664236, 0.145985401459854, 0.17226277372262774, 0.1795620437956204]\n",
      "[RandomForestClassifier(max_depth=10, min_samples_split=8, n_estimators=25), KNeighborsClassifier(algorithm='kd_tree', metric='euclidean'), SGDClassifier(alpha=0.01, loss='log', penalty='l1'), SVC(C=1, gamma=0.1, probability=True)]\n",
      "\n",
      "SVC(C=1, gamma=0.1, probability=True)\n",
      "Best model accuracy: 17.96%\n",
      "Accuracy against unseen data: 9.09%\n",
      "Top 3 accuracy against unseen data: 11.69%\n",
      "\n",
      "##############################   Testing against: dex   ##############################\n",
      "\n",
      "[0.21605839416058395, 0.18832116788321168, 0.20875912408759129, 0.2145985401459854]\n",
      "[RandomForestClassifier(criterion='entropy', max_depth=15, min_samples_split=8,\n",
      "                       n_estimators=75), KNeighborsClassifier(algorithm='ball_tree', metric='euclidean'), SGDClassifier(alpha=0.01, loss='log', penalty='elasticnet'), SVC(C=1, gamma=0.1, probability=True)]\n",
      "\n",
      "RandomForestClassifier(criterion='entropy', max_depth=15, min_samples_split=8,\n",
      "                       n_estimators=75)\n",
      "Best model accuracy: 21.61%\n",
      "Accuracy against unseen data: 24.68%\n",
      "Top 3 accuracy against unseen data: 27.27%\n",
      "\n",
      "##############################   Testing against: con   ##############################\n",
      "\n",
      "[0.256934306569343, 0.22335766423357667, 0.2218978102189781, 0.23503649635036497]\n",
      "[RandomForestClassifier(criterion='entropy', max_depth=15, min_samples_split=6), KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, metric='euclidean',\n",
      "                     weights='distance'), SGDClassifier(alpha=0.001, loss='log'), SVC(C=1, gamma=1, probability=True)]\n",
      "\n",
      "RandomForestClassifier(criterion='entropy', max_depth=15, min_samples_split=6)\n",
      "Best model accuracy: 25.69%\n",
      "Accuracy against unseen data: 19.48%\n",
      "Top 3 accuracy against unseen data: 14.29%\n",
      "\n",
      "##############################   Testing against: int   ##############################\n",
      "\n",
      "[0.2408759124087591, 0.21313868613138687, 0.2204379562043796, 0.23065693430656933]\n",
      "[RandomForestClassifier(max_depth=20, min_samples_split=6, n_estimators=50), KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, metric='euclidean'), SGDClassifier(alpha=0.001, loss='log'), SVC(C=1000, gamma=0.1, kernel='sigmoid', probability=True)]\n",
      "\n",
      "RandomForestClassifier(max_depth=20, min_samples_split=6, n_estimators=50)\n",
      "Best model accuracy: 24.09%\n",
      "Accuracy against unseen data: 14.29%\n",
      "Top 3 accuracy against unseen data: 15.58%\n",
      "\n",
      "##############################   Testing against: wis   ##############################\n",
      "\n",
      "[0.31678832116788325, 0.26861313868613135, 0.2788321167883212, 0.3036496350364964]\n",
      "[RandomForestClassifier(max_depth=10, min_samples_split=10, n_estimators=50), KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, metric='euclidean'), SGDClassifier(alpha=0.1, loss='modified_huber'), SVC(C=1, gamma=1, probability=True)]\n",
      "\n",
      "RandomForestClassifier(max_depth=10, min_samples_split=10, n_estimators=50)\n",
      "Best model accuracy: 31.68%\n",
      "Accuracy against unseen data: 27.27%\n",
      "Top 3 accuracy against unseen data: 27.27%\n",
      "\n",
      "##############################   Testing against: cha   ##############################\n",
      "\n",
      "[0.22335766423357667, 0.1854014598540146, 0.19124087591240874, 0.21021897810218979]\n",
      "[RandomForestClassifier(criterion='entropy', max_depth=25, min_samples_split=10,\n",
      "                       n_estimators=25), KNeighborsClassifier(algorithm='brute', metric='manhattan', weights='distance'), SGDClassifier(alpha=0.01, loss='modified_huber'), SVC(C=1, gamma=1, probability=True)]\n",
      "\n",
      "RandomForestClassifier(criterion='entropy', max_depth=25, min_samples_split=10,\n",
      "                       n_estimators=25)\n",
      "Best model accuracy: 22.34%\n",
      "Accuracy against unseen data: 19.48%\n",
      "Top 3 accuracy against unseen data: 22.08%\n",
      "\n",
      "##############################   Testing against: legen   ##############################\n",
      "\n",
      "[0.9518248175182482, 0.9459854014598541, 0.9474452554744526, 0.9489051094890512]\n",
      "[RandomForestClassifier(max_depth=10, min_samples_split=10, n_estimators=25), KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, metric='euclidean'), SGDClassifier(alpha=0.001, loss='log', penalty='l1'), SVC(C=1, gamma=1, probability=True)]\n",
      "\n",
      "RandomForestClassifier(max_depth=10, min_samples_split=10, n_estimators=25)\n",
      "Best model accuracy: 95.18%\n",
      "Accuracy against unseen data: 89.61%\n",
      "Top 3 accuracy against unseen data: 89.61%\n",
      "\n",
      "##############################   Testing against: nalign   ##############################\n",
      "\n",
      "[0.4467153284671532, 0.41459854014598535, 0.4204379562043795, 0.4335766423357665]\n",
      "[RandomForestClassifier(max_depth=20, min_samples_split=8, n_estimators=25), KNeighborsClassifier(algorithm='brute', metric='manhattan', weights='distance'), SGDClassifier(alpha=0.001, loss='log', penalty='l1'), SVC(C=10, gamma=0.1, probability=True)]\n",
      "\n",
      "RandomForestClassifier(max_depth=20, min_samples_split=8, n_estimators=25)\n",
      "Best model accuracy: 44.67%\n",
      "Accuracy against unseen data: 28.57%\n",
      "Top 3 accuracy against unseen data: 33.77%\n",
      "\n",
      "##############################   Testing against: nsize   ##############################\n",
      "\n",
      "[0.6058394160583941, 0.5883211678832116, 0.6233576642335766, 0.6116788321167882]\n",
      "[RandomForestClassifier(criterion='entropy', max_depth=10, min_samples_split=10,\n",
      "                       n_estimators=25), KNeighborsClassifier(algorithm='kd_tree', leaf_size=40, metric='manhattan',\n",
      "                     weights='distance'), SGDClassifier(alpha=0.01, loss='modified_huber'), SVC(C=100, gamma=0.01, probability=True)]\n",
      "\n",
      "SGDClassifier(alpha=0.01, loss='modified_huber')\n",
      "Best model accuracy: 62.34%\n",
      "Accuracy against unseen data: 49.35%\n",
      "Top 3 accuracy against unseen data: 48.05%\n",
      "\n",
      "##############################   Testing against: ntype   ##############################\n",
      "\n",
      "[0.5036496350364964, 0.4569343065693431, 0.4773722627737226, 0.4875912408759124]\n",
      "[RandomForestClassifier(max_depth=15, min_samples_split=10, n_estimators=75), KNeighborsClassifier(algorithm='ball_tree', leaf_size=40, metric='euclidean'), SGDClassifier(alpha=0.001, loss='log'), SVC(C=1, gamma=1, probability=True)]\n",
      "\n",
      "RandomForestClassifier(max_depth=15, min_samples_split=10, n_estimators=75)\n",
      "Best model accuracy: 50.36%\n",
      "Accuracy against unseen data: 54.55%\n",
      "Top 3 accuracy against unseen data: 51.95%\n",
      "\n",
      "##############################   Testing against: nlaw   ##############################\n",
      "\n",
      "[0.6175182481751824, 0.5751824817518247, 0.6131386861313868, 0.6116788321167883]\n",
      "[RandomForestClassifier(max_depth=20, min_samples_split=8), KNeighborsClassifier(algorithm='ball_tree', metric='manhattan',\n",
      "                     weights='distance'), SGDClassifier(alpha=0.001, loss='log', penalty='elasticnet'), SVC(C=1, gamma=1, kernel='linear', probability=True)]\n",
      "\n",
      "RandomForestClassifier(max_depth=20, min_samples_split=8)\n",
      "Best model accuracy: 61.75%\n",
      "Accuracy against unseen data: 50.65%\n",
      "Top 3 accuracy against unseen data: 45.45%\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##############################   Testing against: nmoral   ##############################\n",
      "\n",
      "[0.7124087591240875, 0.6394160583941606, 0.6861313868613138, 0.710948905109489]\n",
      "[RandomForestClassifier(max_depth=10, min_samples_split=8, n_estimators=25), KNeighborsClassifier(algorithm='brute', metric='euclidean'), SGDClassifier(alpha=0.001, loss='log', penalty='l1'), SVC(C=10, gamma=0.1, probability=True)]\n",
      "\n",
      "RandomForestClassifier(max_depth=10, min_samples_split=8, n_estimators=25)\n",
      "Best model accuracy: 71.24%\n",
      "Accuracy against unseen data: 59.74%\n",
      "Top 3 accuracy against unseen data: 62.34%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# BEGIN THE DATA CHUNKING!\n",
    "\n",
    "hit_list = [\"ac\", \"hp\", \"cr\", \"str\", \"dex\", \"con\", \"int\", \"wis\", \"cha\", \"legen\", \"nalign\", \"nsize\", \"ntype\", \"nlaw\", \"nmoral\"]\n",
    "\n",
    "for itr_hit in hit_list:\n",
    "    model_best, score_best, dnd_sets = giga_grid(itr_hit)\n",
    "    print(\"##############################   Testing against:\", itr_hit, \"  ##############################\\n\")\n",
    "    print(str(score_best) + \"\\n\" + str(model_best) + \"\\n\")\n",
    "    target_score = max(score_best)\n",
    "    target_model = model_best[score_best.index(target_score)]\n",
    "    print(str(target_model))\n",
    "    print(\"Best model accuracy:\", \"{:.2f}\".format(target_score*100) + \"%\")\n",
    "    y_hat = target_model.predict(dnd_sets[1])\n",
    "    print(\"Accuracy against unseen data:\", \"{:.2f}\".format(accuracy_score(dnd_sets[3], y_hat)*100) + \"%\")\n",
    "    model_best.remove(model_best[score_best.index(min(score_best))])\n",
    "    vote_setup = [(str(model_best[0]), model_best[0]), (str(model_best[1]), model_best[1]), (str(model_best[2]), model_best[2])]\n",
    "    model_vote = VotingClassifier(vote_setup, voting=\"soft\")\n",
    "    model_vote.fit(dnd_sets[0], dnd_sets[2])\n",
    "    y_hat = model_vote.predict(dnd_sets[1])\n",
    "    print(\"Top 3 accuracy against unseen data:\", \"{:.2f}\".format(accuracy_score(dnd_sets[3], y_hat)*100) + \"%\")\n",
    "    print()"
   ]
  }
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