legendary.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Pokemon Dataset - Predicting Legendaries\n",
    "\n",
    "\n",
    "The goal of this notebook is to create a supervised machine learning classifier that can predict whether a Pokemon is legendary based on its statistics.\n",
    "\n",
    "## Importing Modules and Data"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import pandas as pd\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import seaborn as sns\r\n",
    "import numpy as np\r\n",
    "import itertools\r\n",
    "from pokedex import pokedex\r\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\r\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler      \r\n",
    "from sklearn.pipeline import Pipeline  \r\n",
    "from sklearn.svm import SVC\r\n",
    "from matplotlib.patches import Patch\r\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\r\n",
    "from pandas.api.types import CategoricalDtype\r\n",
    "\r\n",
    "df = pd.read_csv('pokemon.csv')\r\n",
    "\r\n",
    "fig = plt.figure(figsize=(10,10))\r\n",
    "\r\n",
    "legendary_counts = np.unique(df['is_legendary'], return_counts=True)[1]\r\n",
    "\r\n",
    "plt.pie(legendary_counts,labels=['Non-Legendary', 'Legendary'], \r\n",
    "    autopct ='%1.1f%%', \r\n",
    "    shadow = True,                 \r\n",
    "    startangle = 145,\r\n",
    "    explode=(0, 0.2),\r\n",
    "    textprops={'fontsize': 14}\r\n",
    ")\r\n",
    "\r\n",
    "plt.show()\r\n",
    "\r\n",
    "display(df.head())\r\n"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "                     abilities  against_bug  against_dark  against_dragon  \\\n",
       "0  ['Overgrow', 'Chlorophyll']          1.0           1.0             1.0   \n",
       "1  ['Overgrow', 'Chlorophyll']          1.0           1.0             1.0   \n",
       "2  ['Overgrow', 'Chlorophyll']          1.0           1.0             1.0   \n",
       "3     ['Blaze', 'Solar Power']          0.5           1.0             1.0   \n",
       "4     ['Blaze', 'Solar Power']          0.5           1.0             1.0   \n",
       "\n",
       "   against_electric  against_fairy  against_fight  against_fire  \\\n",
       "0               0.5            0.5            0.5           2.0   \n",
       "1               0.5            0.5            0.5           2.0   \n",
       "2               0.5            0.5            0.5           2.0   \n",
       "3               1.0            0.5            1.0           0.5   \n",
       "4               1.0            0.5            1.0           0.5   \n",
       "\n",
       "   against_flying  against_ghost  ...  percentage_male  pokedex_number  \\\n",
       "0             2.0            1.0  ...             88.1               1   \n",
       "1             2.0            1.0  ...             88.1               2   \n",
       "2             2.0            1.0  ...             88.1               3   \n",
       "3             1.0            1.0  ...             88.1               4   \n",
       "4             1.0            1.0  ...             88.1               5   \n",
       "\n",
       "   sp_attack  sp_defense  speed  type1   type2  weight_kg  generation  \\\n",
       "0         65          65     45  grass  poison        6.9           1   \n",
       "1         80          80     60  grass  poison       13.0           1   \n",
       "2        122         120     80  grass  poison      100.0           1   \n",
       "3         60          50     65   fire     NaN        8.5           1   \n",
       "4         80          65     80   fire     NaN       19.0           1   \n",
       "\n",
       "   is_legendary  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ],
      "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>abilities</th>\n",
       "      <th>against_bug</th>\n",
       "      <th>against_dark</th>\n",
       "      <th>against_dragon</th>\n",
       "      <th>against_electric</th>\n",
       "      <th>against_fairy</th>\n",
       "      <th>against_fight</th>\n",
       "      <th>against_fire</th>\n",
       "      <th>against_flying</th>\n",
       "      <th>against_ghost</th>\n",
       "      <th>...</th>\n",
       "      <th>percentage_male</th>\n",
       "      <th>pokedex_number</th>\n",
       "      <th>sp_attack</th>\n",
       "      <th>sp_defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>type1</th>\n",
       "      <th>type2</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>generation</th>\n",
       "      <th>is_legendary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>45</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['Overgrow', 'Chlorophyll']</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['Blaze', 'Solar Power']</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.1</td>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Feature Description\r\n",
    "\r\n",
    "### Unique Identifiers\r\n",
    "- `pokedex_number`: The index of the Pokemon in the Pokedex\r\n",
    "- `name`: The name of the Pokemon in English\r\n",
    "- `japanese_name`: The name of the Pokemon in Japanese\r\n",
    "\r\n",
    "### Categorical Features\r\n",
    "- `abilities`: A list of Pokemon's attacks/abilities\r\n",
    "- `classification`: The classification of the Pokemon (Bird Pokemon, Seed Pokemon etc. )\r\n",
    "- `type1`: The primary type of the Pokemon (fire, water, flying, electric)\r\n",
    "- `type2`: The secondary type of the Pokemon\r\n",
    "- `generation`: The generation in which the Pokemon was introduced\r\n",
    "- `is_legendary`: indicates whether the Pokemon is legendary\r\n",
    "\r\n",
    "### Numerical Features\r\n",
    "- `against_{type}`: 18 features that denote the incoming damage multiplier from Pokemon of type {type}\r\n",
    "- `attack`: The attack damage of the Pokemon\r\n",
    "- `base_egg_steps`: # of steps required to hatch an egg of the Pokemon\r\n",
    "- `base_happiness`: The base happiness of the Pokemon\r\n",
    "- `base_total`: hp + attack + defense + speed + sp_attack + sp_defense\r\n",
    "- `capture_rate`: The base capture rate of the Pokemon (presumably with a regular Pokeball?)\r\n",
    "- `defense`: The defense of the Pokemon\r\n",
    "- `experience_growth`: The experience growth of the Pokemon\r\n",
    "- `height_m`: The height of the Pokemon in metres\r\n",
    "- `hp`: The total health (hit points/health points) of the Pokemon\r\n",
    "- `percentage_male`: The percentage of the species that are male.\r\n",
    "- `sp_attack`: The special attack damage\r\n",
    "- `sp_defense`: The special defense \r\n",
    "- `speed`: The Pokemon's speed\r\n",
    "- `weight_kg`: The weight of the Pokemon in kilograms\r\n",
    "\r\n",
    "## Pre-processing and Exploratory Data Analysis\r\n",
    "\r\n",
    "Let's start by double-checking base_total and searching for missing data."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "#  check that base_total = hp + attack + defense + speed + sp_attack + sp_defense\r\n",
    "print((df.loc[:,['attack','defense','hp','speed','sp_attack','sp_defense']].apply(lambda x: x.sum(), axis=1) == df['base_total']).all())\r\n",
    "\r\n",
    "# check for null values\r\n",
    "display(df.info())"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "True\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 801 entries, 0 to 800\n",
      "Data columns (total 41 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   abilities          801 non-null    object \n",
      " 1   against_bug        801 non-null    float64\n",
      " 2   against_dark       801 non-null    float64\n",
      " 3   against_dragon     801 non-null    float64\n",
      " 4   against_electric   801 non-null    float64\n",
      " 5   against_fairy      801 non-null    float64\n",
      " 6   against_fight      801 non-null    float64\n",
      " 7   against_fire       801 non-null    float64\n",
      " 8   against_flying     801 non-null    float64\n",
      " 9   against_ghost      801 non-null    float64\n",
      " 10  against_grass      801 non-null    float64\n",
      " 11  against_ground     801 non-null    float64\n",
      " 12  against_ice        801 non-null    float64\n",
      " 13  against_normal     801 non-null    float64\n",
      " 14  against_poison     801 non-null    float64\n",
      " 15  against_psychic    801 non-null    float64\n",
      " 16  against_rock       801 non-null    float64\n",
      " 17  against_steel      801 non-null    float64\n",
      " 18  against_water      801 non-null    float64\n",
      " 19  attack             801 non-null    int64  \n",
      " 20  base_egg_steps     801 non-null    int64  \n",
      " 21  base_happiness     801 non-null    int64  \n",
      " 22  base_total         801 non-null    int64  \n",
      " 23  capture_rate       801 non-null    object \n",
      " 24  classfication      801 non-null    object \n",
      " 25  defense            801 non-null    int64  \n",
      " 26  experience_growth  801 non-null    int64  \n",
      " 27  height_m           781 non-null    float64\n",
      " 28  hp                 801 non-null    int64  \n",
      " 29  japanese_name      801 non-null    object \n",
      " 30  name               801 non-null    object \n",
      " 31  percentage_male    703 non-null    float64\n",
      " 32  pokedex_number     801 non-null    int64  \n",
      " 33  sp_attack          801 non-null    int64  \n",
      " 34  sp_defense         801 non-null    int64  \n",
      " 35  speed              801 non-null    int64  \n",
      " 36  type1              801 non-null    object \n",
      " 37  type2              417 non-null    object \n",
      " 38  weight_kg          781 non-null    float64\n",
      " 39  generation         801 non-null    int64  \n",
      " 40  is_legendary       801 non-null    int64  \n",
      "dtypes: float64(21), int64(13), object(7)\n",
      "memory usage: 256.7+ KB\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Notice that `height_m` and `weight_kg` contain several missing values. Luckily, this data is available via the Pokemon API wrapper library: \n",
    "\n",
    "- https://github.com/PokeDevs/pokedex.py. \n",
    "\n",
    "\n",
    "The weights and heights supplied by the API are given in imperial units. The function below handles the conversion to SI units and is used to fill the missing values."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "def get_missing_height_weight(number):\r\n",
    "    \"\"\" \r\n",
    "    A function to query the Pokedex API using the pokedex_number and return the height (m) and weight (kg).\r\n",
    "    \"\"\"\r\n",
    "    info = pokedex.Pokedex().get_pokemon_by_number(number)[0]\r\n",
    "\r\n",
    "    height_feet = info['height'].split(\"\\'\")[0]\r\n",
    "    height_inches = info['height'].split(\"\\'\")[1][:-1]\r\n",
    "    \r\n",
    "    # to account for examples where inches are omitted from the string for being zero e.g. rattata height = 1'\r\n",
    "    height_inches = 0 if height_inches == \"\" else height_inches \r\n",
    "    \r\n",
    "    height_meters = (float(height_feet) * 12 + float(height_inches)) * 0.0254\r\n",
    "\r\n",
    "    weight_lbs = info['weight'][:-5]\r\n",
    "    weight_kg = float(weight_lbs) / 2.2046\r\n",
    "    \r\n",
    "    return round(height_meters,1), round(weight_kg,1)\r\n",
    "\r\n",
    "for pokedex_num in df.loc[df['height_m'].isna(), ['pokedex_number']].values:\r\n",
    "\r\n",
    "    print(f\"Found missing height/weight for Pokemon {pokedex_num[0]}: {df.loc[df['pokedex_number'] == pokedex_num[0], 'name'].values}\")\r\n",
    "    df.loc[df['pokedex_number'] == pokedex_num[0], ['height_m', 'weight_kg']] = get_missing_height_weight(pokedex_num[0])\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Found missing height/weight for Pokemon 19: ['Rattata']\n",
      "Found missing height/weight for Pokemon 20: ['Raticate']\n",
      "Found missing height/weight for Pokemon 26: ['Raichu']\n",
      "Found missing height/weight for Pokemon 27: ['Sandshrew']\n",
      "Found missing height/weight for Pokemon 28: ['Sandslash']\n",
      "Found missing height/weight for Pokemon 37: ['Vulpix']\n",
      "Found missing height/weight for Pokemon 38: ['Ninetales']\n",
      "Found missing height/weight for Pokemon 50: ['Diglett']\n",
      "Found missing height/weight for Pokemon 51: ['Dugtrio']\n",
      "Found missing height/weight for Pokemon 52: ['Meowth']\n",
      "Found missing height/weight for Pokemon 53: ['Persian']\n",
      "Found missing height/weight for Pokemon 74: ['Geodude']\n",
      "Found missing height/weight for Pokemon 75: ['Graveler']\n",
      "Found missing height/weight for Pokemon 76: ['Golem']\n",
      "Found missing height/weight for Pokemon 88: ['Grimer']\n",
      "Found missing height/weight for Pokemon 89: ['Muk']\n",
      "Found missing height/weight for Pokemon 103: ['Exeggutor']\n",
      "Found missing height/weight for Pokemon 105: ['Marowak']\n",
      "Found missing height/weight for Pokemon 720: ['Hoopa']\n",
      "Found missing height/weight for Pokemon 745: ['Lycanroc']\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "We see that `type2` contains some missing values too, this is because some Pokemon have no secondary type. In this instance we can simply replace np.nan with the string 'none'. We also note several examples where `type2` is a duplicate of `type1`; these will also be replaced with 'none'.\n",
    "\n",
    "The `capture_rate` appears to have been imported as an object type, whereas we would expect this to be int64. Further digging reveals that the Pokemon 'Minior' has two capture rates: 30 (Meteorite) and 255 (Core), which are interpretted as string. Based on https://bulbapedia.bulbagarden.net/wiki/Minior_(Pok%C3%A9mon), we will set this to 30 and convert the column to int64."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# fixing secondary type\r\n",
    "df.loc[df['type1']==df['type2'],'type2'] = np.nan\r\n",
    "df.loc[df['type2'].isna(), 'type2'] = \"None\"\r\n",
    "\r\n",
    "# fixing capture rate for minior\r\n",
    "df.loc[773,'capture_rate'] = 30 \r\n",
    "df['capture_rate'] = df['capture_rate'].astype('int64')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "The last missing values relate to gender, which on closer inspection seems to be a good indicator of whether a Pokemon is legendary. The calculation below shows that 90% of legendary pokemon have an undefined gender, compared to ~5% of non-legendary. It may be useful to create a new binary feature to indicate whether the Pokemon has a gender."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# check count of missing percentage_male \r\n",
    "df.groupby('is_legendary')['percentage_male'].apply(lambda x: pd.Series({'No. Missing':x.isna().sum(),'Total': len(x)}))\r\n",
    "\r\n",
    "# add new 'has_gender' feature\r\n",
    "df['has_gender'] = ~df['percentage_male'].isna()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now, let's see if there's any obvious relationship between type1, type2, generation and legendary status."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# convert generation to categorical\r\n",
    "cat_dtype = CategoricalDtype(categories=[1,2,3,4,5,6,7], ordered=True)\r\n",
    "df['generation'] = df['generation'].astype(cat_dtype)\r\n",
    "\r\n",
    "fig, [ax1, ax2, ax3] = plt.subplots(3,1,figsize=(15,15))\r\n",
    "\r\n",
    "for ax, var in zip([ax1, ax2, ax3], ['type1', 'type2', 'generation']): \r\n",
    "    sns.histplot(data=df, x=var, hue='is_legendary', multiple='fill', ax=ax, legend=False)\r\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45)\r\n",
    "\r\n",
    "fig.legend(['Legendary', 'Not Legendary'], bbox_to_anchor=(0.9,0.88), loc=\"upper left\")\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "We see an increased proportion of legendaries amongst the dragon, steel, psychic and flying type Pokemon, but generally they seem to be fairly distributed across all types. In addition, one-hot encoding the type1 and type2 features will increase the dimensionality of the data considerably and will most likely hurt predictive performance. \n",
    "\n",
    "There is an increased proportion of legendary Pokemon in generation 7, but again, there is no clear trend across the remaining generations.\n",
    "\n",
    "Next we will create plots of the numerical features, but omit the `against_{type}` features. This is because they are related to the type of the Pokemon, which we have already identified as not too useful."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "fig, axs = plt.subplots(5, 3, figsize=(12,12))\r\n",
    "\r\n",
    "cols = ['attack', 'base_egg_steps', 'base_happiness', 'base_total', 'capture_rate', 'defense', 'experience_growth', 'height_m', 'hp', 'sp_attack', 'sp_defense', 'speed', 'weight_kg']\r\n",
    "\r\n",
    "\r\n",
    "for var, ax in zip(cols, list(itertools.chain(*axs))):\r\n",
    "    # box & whisker\r\n",
    "    sns.boxplot(data=df, x=\"is_legendary\", y=var, ax=ax)\r\n",
    "    \r\n",
    "#     # histogram w or w/o kde\r\n",
    "#     sns.histplot(data=df, x=var, multiple='stack', kde=True, hue='is_legendary', alpha=0.5, ax=ax, legend=False)\r\n",
    "\r\n",
    "\r\n",
    "legend_elements = [Patch(facecolor='tab:orange', label='Legendary'), Patch(facecolor='tab:blue', label='Not Legendary')]\r\n",
    "\r\n",
    "fig.legend(handles=legend_elements, bbox_to_anchor=(1,0.99), loc=\"upper left\")\r\n",
    "fig.tight_layout(pad=2)\r\n",
    "fig.delaxes(axs[-1][-1]); fig.delaxes(axs[-1][-2])\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 864x864 with 13 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "As one would expect, it seems that Pokemon with higher combat statistics such as `hp`, `attack`, `defense`, `speed` are more likely to be classed as legendary, and a similar split is observed for Pokemon with low capture rate.\n",
    "\n",
    "Interestingly, we can achieve adequate separation with just two features, namely, `caputure_rate` and `base_egg_steps`, as evidenced by the scatter plot below."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(10,5))\r\n",
    "\r\n",
    "sc = sns.scatterplot(x=df['capture_rate'], y=df['base_egg_steps'], hue=df['is_legendary'], ax=ax, alpha=0.5)\r\n",
    "\r\n",
    "# ignore error below, there's some issue with items in the legend appearing without permission\r\n",
    "plt.legend(['_hidden', 'Non-Legendary', 'Legendary'], bbox_to_anchor=(1,1.025), loc=\"upper left\")\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\Harrison\\AppData\\Local\\Temp/ipykernel_4284/3345324444.py:6: UserWarning: The handle <matplotlib.collections.PathCollection object at 0x0000018251E597F0> has a label of '_hidden' which cannot be automatically added to the legend.\n",
      "  plt.legend(['_hidden', 'Non-Legendary', 'Legendary'], bbox_to_anchor=(1,1.025), loc=\"upper left\")\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Many non-linear classifiers should be able to create a decision function which can separate these two classes, but first let's define our input features and target variable, then split the data into training and testing sets. We'll use stratification to ensure that the legendaries are equally distributed across both datasets."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "X = df.loc[:,['capture_rate','base_egg_steps', 'is_legendary']].copy(deep=True)\r\n",
    "y = X.pop('is_legendary')\r\n",
    "\r\n",
    "# split into stratified training (80%) and testing (20%) sets\r\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True, stratify=y, random_state=42)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Our pipeline will consist of a scaler and a classifier, namely, the `MinMaxScaler` and support vector classifier `SVC` from the scikit-learn library.\n",
    "\n",
    "We'll use `GridSearchCV` to find optimal parameters for the SVC. Note the use of the rbf, or Gaussian kernel, which is necessary to create the non-linear decision boundary required to separate the two classes."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "pipe = Pipeline([('scaler', None), ('clf', SVC())])\r\n",
    "\r\n",
    "params = {\r\n",
    "  'scaler': [MinMaxScaler()],\r\n",
    "  'clf__C': [10**x for x in [-2,-1,0,1,2,]],\r\n",
    "  'clf__gamma': [1, 0.5, 0.25, 0.1, 0.05],\r\n",
    "  'clf__kernel': ['rbf'],\r\n",
    "  'clf__class_weight': [None, 'balanced']\r\n",
    "  }\r\n",
    "  \r\n",
    "grid = GridSearchCV(pipe, params)\r\n",
    "grid.fit(X_train, y_train)\r\n",
    "\r\n",
    "y_pred = grid.predict(X_test)\r\n",
    "\r\n",
    "print(f'Best parameters: {grid.best_params_}')\r\n",
    "print(f'Best score: {grid.score(X_test, y_test)}')\r\n",
    "print(f'Precision: {precision_score(y_test, y_pred)}')\r\n",
    "print(f'Recall: {recall_score(y_test, y_pred)}')\r\n",
    "print(f'F1-Score: {f1_score(y_test, y_pred)}')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Best parameters: {'clf__C': 100, 'clf__class_weight': None, 'clf__gamma': 1, 'clf__kernel': 'rbf', 'scaler': MinMaxScaler()}\n",
      "Best score: 1.0\n",
      "Precision: 1.0\n",
      "Recall: 1.0\n",
      "F1-Score: 1.0\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Our classifier achieves a perfect score here, which is not too surprising given how well separated the two classes are on the previous plot. Given the low dimensionality of the data, it is also possible to superimpose the decision boundary from the SVM and see how different regions of the feature space are classified."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "from matplotlib.colors import ListedColormap\r\n",
    "\r\n",
    "cmap = ListedColormap([\"cornflowerblue\", \"darkorange\"])\r\n",
    "\r\n",
    "# decision boundary plot taken from:\r\n",
    "# https://stackoverflow.com/questions/51297423/plot-scikit-learn-sklearn-svm-decision-boundary-surface\r\n",
    "\r\n",
    "def make_meshgrid(x, y, h=.0025):\r\n",
    "    x_min, x_max = x.min() - 1, x.max() + 1\r\n",
    "    y_min, y_max = y.min() - 1, y.max() + 1\r\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\r\n",
    "    return xx, yy\r\n",
    "\r\n",
    "def plot_contours(ax, clf, xx, yy, **params):\r\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\r\n",
    "    Z = Z.reshape(xx.shape)\r\n",
    "    out = ax.contourf(xx, yy, Z, **params)\r\n",
    "    return out\r\n",
    "\r\n",
    "fig, [ax1, ax2] = plt.subplots(2,1, figsize=(7,10))\r\n",
    "\r\n",
    "for ax, X, y, title in zip([ax1, ax2], [X_train, X_test], [y_train, y_test], ['Training Data', 'Testing Data']):\r\n",
    "\r\n",
    "    X_scaled = grid.best_estimator_.steps[0][1].transform(X)\r\n",
    "\r\n",
    "    X0, X1 = X_scaled[:, 0], X_scaled[:, 1]\r\n",
    "\r\n",
    "    xx, yy = make_meshgrid(X0, X1)\r\n",
    "\r\n",
    "    plot_contours(ax, grid.best_estimator_.steps[1][1], xx, yy, cmap=cmap, alpha=0.3)\r\n",
    "\r\n",
    "    ax.scatter(X0, X1, c=y, cmap=cmap, s=20, alpha=0.5)\r\n",
    "\r\n",
    "    ax.set_ylabel('Base Egg Steps (scaled)')\r\n",
    "    ax.set_xlabel('Capture Rate (scaled)')\r\n",
    "    ax.set_xlim([-0.05,1.25])\r\n",
    "    ax.set_ylim([-0.05,1.05])\r\n",
    "    ax.set_title(title)\r\n",
    "\r\n",
    "fig.legend(handles=legend_elements, bbox_to_anchor=(0.9,0.89), loc=\"upper left\")\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 504x720 with 2 Axes>"
      ],
      "image/png": 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"
     },
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   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Comments\n",
    "\n",
    "Note that the decision function depends on how the data is split, as it appears possible to obtain X_train such that not enough legendaries are present in the smaller clusters (near the origin, or near (1,1)). Such a scenario could lead to a decision boundary that does not cover that region, resulting in misclassification. One approach which could reduce the likelihood of this happening, would be to generate synthetic data to boost the number of examples in the minority class, thus reducing the probability of excluding the smaller clusters from the training set. One of the many flavours of SMOTE might do the trick: https://imbalanced-learn.org/dev/auto_examples/over-sampling/plot_comparison_over_sampling.html#sphx-glr-auto-examples-over-sampling-plot-comparison-over-sampling-py."
   ],
   "metadata": {}
  }
 ],
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