TPK_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import savefig\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Unnamed: 0</th>\n",
       "      <th>combat_id</th>\n",
       "      <th>start_time</th>\n",
       "      <th>number_of_rounds</th>\n",
       "      <th>player_ids</th>\n",
       "      <th>player_info</th>\n",
       "      <th>monsters_info</th>\n",
       "      <th>party_size</th>\n",
       "      <th>total_slots</th>\n",
       "      <th>total_max_slots</th>\n",
       "      <th>...</th>\n",
       "      <th>Cleric</th>\n",
       "      <th>Warlock</th>\n",
       "      <th>Ranger</th>\n",
       "      <th>Barbarian</th>\n",
       "      <th>Druid</th>\n",
       "      <th>Monk</th>\n",
       "      <th>Sorcerer</th>\n",
       "      <th>Rogue</th>\n",
       "      <th>weighted_monster_level</th>\n",
       "      <th>weighted_spell_slots</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1653817902-15ec25a2-6757-4ceb-ac41-d44352b97705</td>\n",
       "      <td>1.653818e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>['209330647903743785']</td>\n",
       "      <td>[{'hp_ratio': (65, 65), 'class': [('Ranger', 1...</td>\n",
       "      <td>[{'monster_id': 'c0cdb522-64c2-42ee-b1a8-6ef22...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': ...</td>\n",
       "      <td>{'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': ...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>1653869871-e7fd0606-de62-4688-a55b-ae4c25a14beb</td>\n",
       "      <td>1.653870e+09</td>\n",
       "      <td>2</td>\n",
       "      <td>['176588704091156599']</td>\n",
       "      <td>[{'hp_ratio': (0, 38), 'class': [('Monk', 4), ...</td>\n",
       "      <td>[{'monster_id': '3bdb4cc1-6ca9-47e8-9610-d68ef...</td>\n",
       "      <td>1</td>\n",
       "      <td>{'1': 2, '2': 0, '3': 0, '4': 0, '5': 0, '6': ...</td>\n",
       "      <td>{'1': 2, '2': 0, '3': 0, '4': 0, '5': 0, '6': ...</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>1653870265-3912208d-8851-4441-90a7-9473068eb93e</td>\n",
       "      <td>1.653870e+09</td>\n",
       "      <td>4</td>\n",
       "      <td>['743069156064144035', '219174602203404794']</td>\n",
       "      <td>[{'hp_ratio': (33, 73), 'class': [('Druid', 9)...</td>\n",
       "      <td>[{'monster_id': '203bce70-7a28-4c79-a990-fba6f...</td>\n",
       "      <td>2</td>\n",
       "      <td>{'1': 4, '2': 3, '3': 3, '4': 3, '5': 1, '6': ...</td>\n",
       "      <td>{'1': 7, '2': 3, '3': 3, '4': 3, '5': 1, '6': ...</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>1653872787-dd8c22a6-4f2b-46f9-901a-17c887a64f29</td>\n",
       "      <td>1.653873e+09</td>\n",
       "      <td>6</td>\n",
       "      <td>['743069156064144035', '219174602203404794']</td>\n",
       "      <td>[{'hp_ratio': (61, 81), 'class': [('Barbarian'...</td>\n",
       "      <td>[{'monster_id': 'aed32ba7-a864-41ca-a4b3-1314d...</td>\n",
       "      <td>2</td>\n",
       "      <td>{'1': 7, '2': 3, '3': 3, '4': 3, '5': 2, '6': ...</td>\n",
       "      <td>{'1': 7, '2': 3, '3': 3, '4': 3, '5': 2, '6': ...</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>1653896334-b9e6fdf9-8623-4f4f-999f-723261516a9f</td>\n",
       "      <td>1.653896e+09</td>\n",
       "      <td>2</td>\n",
       "      <td>['244501817820067418', '252441296436920394', '...</td>\n",
       "      <td>[{'hp_ratio': (42, 47), 'class': [('Paladin', ...</td>\n",
       "      <td>[{'monster_id': '6acfea01-ab7a-4a14-b17c-e05a9...</td>\n",
       "      <td>4</td>\n",
       "      <td>{'1': 18, '2': 11, '3': 5, '4': 3, '5': 2, '6'...</td>\n",
       "      <td>{'1': 19, '2': 11, '3': 5, '4': 3, '5': 2, '6'...</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                        combat_id    start_time  \\\n",
       "0           1  1653817902-15ec25a2-6757-4ceb-ac41-d44352b97705  1.653818e+09   \n",
       "1          14  1653869871-e7fd0606-de62-4688-a55b-ae4c25a14beb  1.653870e+09   \n",
       "2          15  1653870265-3912208d-8851-4441-90a7-9473068eb93e  1.653870e+09   \n",
       "3          18  1653872787-dd8c22a6-4f2b-46f9-901a-17c887a64f29  1.653873e+09   \n",
       "4          32  1653896334-b9e6fdf9-8623-4f4f-999f-723261516a9f  1.653896e+09   \n",
       "\n",
       "   number_of_rounds                                         player_ids  \\\n",
       "0                 1                             ['209330647903743785']   \n",
       "1                 2                             ['176588704091156599']   \n",
       "2                 4       ['743069156064144035', '219174602203404794']   \n",
       "3                 6       ['743069156064144035', '219174602203404794']   \n",
       "4                 2  ['244501817820067418', '252441296436920394', '...   \n",
       "\n",
       "                                         player_info  \\\n",
       "0  [{'hp_ratio': (65, 65), 'class': [('Ranger', 1...   \n",
       "1  [{'hp_ratio': (0, 38), 'class': [('Monk', 4), ...   \n",
       "2  [{'hp_ratio': (33, 73), 'class': [('Druid', 9)...   \n",
       "3  [{'hp_ratio': (61, 81), 'class': [('Barbarian'...   \n",
       "4  [{'hp_ratio': (42, 47), 'class': [('Paladin', ...   \n",
       "\n",
       "                                       monsters_info  party_size  \\\n",
       "0  [{'monster_id': 'c0cdb522-64c2-42ee-b1a8-6ef22...           1   \n",
       "1  [{'monster_id': '3bdb4cc1-6ca9-47e8-9610-d68ef...           1   \n",
       "2  [{'monster_id': '203bce70-7a28-4c79-a990-fba6f...           2   \n",
       "3  [{'monster_id': 'aed32ba7-a864-41ca-a4b3-1314d...           2   \n",
       "4  [{'monster_id': '6acfea01-ab7a-4a14-b17c-e05a9...           4   \n",
       "\n",
       "                                         total_slots  \\\n",
       "0  {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': ...   \n",
       "1  {'1': 2, '2': 0, '3': 0, '4': 0, '5': 0, '6': ...   \n",
       "2  {'1': 4, '2': 3, '3': 3, '4': 3, '5': 1, '6': ...   \n",
       "3  {'1': 7, '2': 3, '3': 3, '4': 3, '5': 2, '6': ...   \n",
       "4  {'1': 18, '2': 11, '3': 5, '4': 3, '5': 2, '6'...   \n",
       "\n",
       "                                     total_max_slots  ... Cleric Warlock  \\\n",
       "0  {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': ...  ...      0       0   \n",
       "1  {'1': 2, '2': 0, '3': 0, '4': 0, '5': 0, '6': ...  ...      1       0   \n",
       "2  {'1': 7, '2': 3, '3': 3, '4': 3, '5': 1, '6': ...  ...      1       0   \n",
       "3  {'1': 7, '2': 3, '3': 3, '4': 3, '5': 2, '6': ...  ...      1       0   \n",
       "4  {'1': 19, '2': 11, '3': 5, '4': 3, '5': 2, '6'...  ...      1       1   \n",
       "\n",
       "  Ranger Barbarian Druid Monk Sorcerer Rogue weighted_monster_level  \\\n",
       "0      1         0     0    0        0     0                    4.0   \n",
       "1      0         0     0    1        0     0                    5.0   \n",
       "2      0         1     1    0        0     0                    4.0   \n",
       "3      0         1     1    0        0     0                   12.0   \n",
       "4      0         1     1    0        0     0                    1.0   \n",
       "\n",
       "  weighted_spell_slots  \n",
       "0                  139  \n",
       "1                   12  \n",
       "2                  367  \n",
       "3                  449  \n",
       "4                  871  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('scaled_filtered_24_4_10.csv')\n",
    "display(df.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Additional filters to the data for EDA purposes\"\"\"\n",
    "filtered_df= df[df['party_total_hpratio']<1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of combats lost to the filter: 4619\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "332"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lost_combats = len(df['combat_id'].unique()) - len(filtered_df['combat_id'].unique())\n",
    "print(f\"Number of combats lost to the filter: {lost_combats}\")\n",
    "len(filtered_df[filtered_df['party_total_hpratio'] == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df = filtered_df[filtered_df['party_size'] <= 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of combats lost to the filter: 4619\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "332"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lost_combats = len(df['combat_id'].unique()) - len(filtered_df['combat_id'].unique())\n",
    "print(f\"Number of combats lost to the filter: {lost_combats}\")\n",
    "len(filtered_df[filtered_df['party_total_hpratio'] == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df = filtered_df[filtered_df['party_total_level']/filtered_df['party_size']  <= 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of combats lost to the filter: 4646\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "332"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lost_combats = len(df['combat_id'].unique()) - len(filtered_df['combat_id'].unique())\n",
    "print(f\"Number of combats lost to the filter: {lost_combats}\")\n",
    "len(filtered_df[filtered_df['party_total_hpratio'] == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df = filtered_df[filtered_df['party_total_ac']/filtered_df['party_size']  <= 38]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of combats lost to the filter: 4653\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "332"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lost_combats = len(df['combat_id'].unique()) - len(filtered_df['combat_id'].unique())\n",
    "print(f\"Number of combats lost to the filter: {lost_combats}\")\n",
    "len(filtered_df[filtered_df['party_total_hpratio'] == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df = filtered_df[filtered_df['party_total_precombat_hp']/filtered_df['party_size'] <350]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of combats lost to the filter: 4655\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "332"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lost_combats = len(df['combat_id'].unique()) - len(filtered_df['combat_id'].unique())\n",
    "print(f\"Number of combats lost to the filter: {lost_combats}\")\n",
    "len(filtered_df[filtered_df['party_total_hpratio'] == 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining extra variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df['avg_party_level'] = filtered_df['party_total_level'] / filtered_df['party_size']\n",
    "filtered_df['avg_hp_ratio'] = (filtered_df['party_total_precombat_hp']-filtered_df['party_total_postcombat_hp'])/ filtered_df['party_max_hp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered_df['weighted_monster_level'].replace(0, np.nan, inplace=True)\n",
    "\n",
    "\"\"\" Calculate the ratio of average party level to average monster level\"\"\"\n",
    "filtered_df['level_ratio'] = filtered_df['party_total_level'] / filtered_df['weighted_monster_level']\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(filtered_df['level_ratio'],filtered_df['avg_hp_ratio'])\n",
    "plt.xlabel('Party Level / Weighted Monster Level')\n",
    "plt.ylabel('Party HP Ratio')\n",
    "plt.title('Party Level to Weighted Monster Level Ratio vs. Party HP Ratio')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "320"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df = filtered_df[(filtered_df['level_ratio'] <50)]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(filtered_df['level_ratio'],filtered_df['avg_hp_ratio'])\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Party Level / Weighted Monster Level')\n",
    "plt.ylabel('Party HP Ratio')\n",
    "plt.title('Party Level to Weighted Monster Level Ratio vs. Party HP Ratio')\n",
    "plt.show()\n",
    "len(filtered_df[filtered_df['party_total_hpratio'] == 0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Start Building TPK Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"creating new column where tpk is 1 and no tpk is 0\"\"\"\n",
    "filtered_df['tpk'] = (filtered_df['party_total_postcombat_hp'] == 0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Our data has less tpks than not, we should balance out the data. Separating majority from the minority into two different dataframes\"\"\"\n",
    "from sklearn.utils import resample\n",
    "\n",
    "# Separate majority and minority classes\n",
    "df_majority = filtered_df[filtered_df.tpk == 0]\n",
    "df_minority = filtered_df[filtered_df.tpk == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tpk\n",
      "0    310\n",
      "1    310\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\"\"\" Downsample majority class\"\"\"\n",
    "df_majority_downsampled = resample(df_majority,\n",
    "                                   replace=False,    # sample without replacement\n",
    "                                   n_samples=len(df_minority),     # to match minority class\n",
    "                                   random_state=123) \n",
    "\n",
    "\"\"\" Combine minority class with downsampled majority class\"\"\"\n",
    "df_balanced = pd.concat([df_majority_downsampled, df_minority])\n",
    "\n",
    "# Display new class counts\n",
    "print(df_balanced.tpk.value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\"\"\" define features:'party_total_level', 'weighted_monster_level', 'party_size', 'monster_number'\n",
    "Parameter X is the balanced dataframe with only the selected features\n",
    "Parameter y is the balanced dataframe desired binary parameter tpk\"\"\"\n",
    "feature_columns = ['party_total_level', 'weighted_monster_level', 'party_size', 'monster_number']\n",
    "X = df_balanced[feature_columns]\n",
    "y = df_balanced['tpk']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "\"\"\"Initialize the Logistic Regression classifier and train the model on X_train set.\"\"\"\n",
    "model = LogisticRegression()\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.57      0.68       104\n",
      "           1       0.62      0.88      0.72        82\n",
      "\n",
      "    accuracy                           0.70       186\n",
      "   macro avg       0.74      0.72      0.70       186\n",
      "weighted avg       0.75      0.70      0.70       186\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting probabilities\n",
    "y_pred_proba_lr = model.predict_proba(X_test)[:, 1]  # Get the probability of TPK\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Actual  Predicted  Probability of TPK\n",
      "8608        0          0            0.374908\n",
      "9298        1          1            0.711288\n",
      "4801        0          0            0.407129\n",
      "10629       0          0            0.174136\n",
      "7800        0          1            0.663297\n",
      "10424       1          1            0.614164\n",
      "5457        0          1            0.706666\n",
      "1612        1          1            0.556578\n",
      "813         1          1            0.534776\n",
      "7846        1          1            0.748975\n",
      "7184        0          0            0.177322\n",
      "6220        1          0            0.489870\n",
      "4293        0          0            0.289451\n",
      "5818        0          0            0.357677\n",
      "1198        0          0            0.279424\n",
      "5718        0          1            0.675965\n",
      "9180        1          1            0.726738\n",
      "4331        1          1            0.692489\n",
      "4947        0          0            0.022228\n",
      "3317        1          1            0.739119\n"
     ]
    }
   ],
   "source": [
    "\n",
    "predictions_lr_df = pd.DataFrame({\n",
    "    'Actual': y_test,\n",
    "    'Predicted': y_pred,\n",
    "    'Probability of TPK': y_pred_proba_lr\n",
    "})\n",
    "\n",
    "print(predictions_lr_df.head(20))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plotting distribution of predicted probabilities\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(y_pred_proba_lr, bins=30, kde=True)\n",
    "plt.title('Distribution of Predicted Probabilities', fontsize=16)\n",
    "plt.xlabel('Predicted Probability of TPK', fontsize=14)\n",
    "plt.ylabel('Frequency', fontsize=14)\n",
    "plt.savefig('probability_distribution.png', dpi=300, transparent=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.calibration import calibration_curve\n",
    "\n",
    "\"\"\" Get true probabilities and predicted probabilities\"\"\"\n",
    "true_prob, pred_prob = calibration_curve(y_test, y_pred_proba_lr, n_bins=10)\n",
    "\n",
    "# Plotting the calibration curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(pred_prob, true_prob, marker='o', linewidth=1, label='Logistic Regression')\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', color='grey', label='Perfectly calibrated')\n",
    "plt.xlabel('Mean Predicted Probability', fontsize=14)\n",
    "plt.ylabel('True Probability in each bin', fontsize=14)\n",
    "plt.legend()\n",
    "plt.title('Calibration Curve', fontsize=16)\n",
    "plt.savefig('calibration_curve.png', dpi=300, transparent=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "dt_model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.74      0.75       104\n",
      "           1       0.68      0.70      0.69        82\n",
      "\n",
      "    accuracy                           0.72       186\n",
      "   macro avg       0.72      0.72      0.72       186\n",
      "weighted avg       0.72      0.72      0.72       186\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predicting classes\n",
    "y_pred_dt = dt_model.predict(X_test)\n",
    "print(classification_report(y_test, y_pred_dt))\n",
    "\n",
    "# Predicting probabilities\n",
    "y_pred_proba_dt = dt_model.predict_proba(X_test)[:, 1]  # Probabilities of the positive class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Actual  Predicted  Probability of TPK\n",
      "8608        0          0            0.000000\n",
      "9298        1          1            1.000000\n",
      "4801        0          0            0.000000\n",
      "10629       0          0            0.000000\n",
      "7800        0          0            0.000000\n",
      "10424       1          1            1.000000\n",
      "5457        0          1            1.000000\n",
      "1612        1          1            1.000000\n",
      "813         1          0            0.000000\n",
      "7846        1          1            1.000000\n",
      "7184        0          0            0.000000\n",
      "6220        1          1            1.000000\n",
      "4293        0          0            0.000000\n",
      "5818        0          0            0.000000\n",
      "1198        0          0            0.000000\n",
      "5718        0          0            0.125000\n",
      "9180        1          1            1.000000\n",
      "4331        1          0            0.333333\n",
      "4947        0          0            0.000000\n",
      "3317        1          1            1.000000\n"
     ]
    }
   ],
   "source": [
    "\"\"\" Create a DataFrame to view predictions and their probabilities\"\"\"\n",
    "predictions_df = pd.DataFrame({\n",
    "    'Actual': y_test,\n",
    "    'Predicted': y_pred_dt,\n",
    "    'Probability of TPK': y_pred_proba_dt\n",
    "})\n",
    "\n",
    "print(predictions_df.head(20))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Classifier performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\"\"\" Calculating ROC curve from both models: Logistic Regression and Decision Tree\"\"\"\n",
    "fpr_log, tpr_log, _ = roc_curve(y_test, model.predict_proba(X_test)[:, 1])\n",
    "fpr_dt, tpr_dt, _ = roc_curve(y_test, dt_model.predict_proba(X_test)[:, 1])\n",
    "\n",
    "\"\"\"Calculating AUC (Area under the ROC Curve)\"\"\"\n",
    "auc_log = auc(fpr_log, tpr_log)\n",
    "auc_dt = auc(fpr_dt, tpr_dt)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr_log, tpr_log, label=f'Logistic Regression (AUC = {auc_log:.2f})', color='blue')\n",
    "plt.plot(fpr_dt, tpr_dt, label=f'Decision Tree (AUC = {auc_dt:.2f})', color='green')\n",
    "plt.plot([0, 1], [0, 1], color='darkgrey', linestyle='--')\n",
    "plt.xlabel('False Positive Rate', fontsize=14)\n",
    "plt.ylabel('True Positive Rate', fontsize=14)\n",
    "plt.title('ROC Curve', fontsize=16)\n",
    "plt.legend()\n",
    "plt.savefig('roccurve.png', dpi=300, transparent=True)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}