machine-learning-comp47490/notebooks/a1-comp47490-18202817.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Assignment 1: Austin Animal Shelter Outcomes\n",
    "\n",
    "**Module:** COMP47490 Machine Learning, Autumn 2021  \n",
    "**Student:** Rajit Banerjee, 18202817  \n",
    "**Data:** `../data/animal_shelter_18202817.csv` (source of sample: [data.austintexas.gov](https://data.austintexas.gov/Health-and-Community-Services/Austin-Animal-Center-Outcomes/9t4d-g238))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1aa206b",
   "metadata": {},
   "source": [
    "Import all required packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "964ca4d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List, Union, Tuple\n",
    "from pprint import pprint\n",
    "from collections import defaultdict, Counter\n",
    "from collections import Counter\n",
    "import time\n",
    "import itertools\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# **scikit-learn: version 1.0.1**\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score, cross_validate \n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB   \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.metrics import f1_score, ConfusionMatrixDisplay, RocCurveDisplay\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "\n",
    "# **mlxtend: version 0.19.0**\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a826c6d",
   "metadata": {},
   "source": [
    "## 1A: Data Cleaning\n",
    "\n",
    "The first step in any machine learning task is to perform exploratory data analysis and data cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9c5ebbe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Animal ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>MonthYear</th>\n",
       "      <th>Date of Birth</th>\n",
       "      <th>Animal Type</th>\n",
       "      <th>Sex upon Outcome</th>\n",
       "      <th>Age upon Outcome</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A819419</td>\n",
       "      <td>*Chicken</td>\n",
       "      <td>09/22/2020 10:33:00 AM</td>\n",
       "      <td>09/22/2020 10:33:00 AM</td>\n",
       "      <td>04/28/2020</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>4 months</td>\n",
       "      <td>Domestic Shorthair</td>\n",
       "      <td>Blue Tabby/White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A710946</td>\n",
       "      <td>*Sabrina</td>\n",
       "      <td>09/07/2015 12:28:00 PM</td>\n",
       "      <td>09/07/2015 12:28:00 PM</td>\n",
       "      <td>06/15/2015</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>2 months</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Torbie</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A572792</td>\n",
       "      <td>Oreo</td>\n",
       "      <td>05/23/2014 05:16:00 PM</td>\n",
       "      <td>05/23/2014 05:16:00 PM</td>\n",
       "      <td>05/06/2007</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>7 years</td>\n",
       "      <td>English Springer Spaniel Mix</td>\n",
       "      <td>Black/White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A807189</td>\n",
       "      <td>Sequoia</td>\n",
       "      <td>10/27/2019 04:44:00 PM</td>\n",
       "      <td>10/27/2019 04:44:00 PM</td>\n",
       "      <td>08/12/2019</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>2 months</td>\n",
       "      <td>Labrador Retriever/Redbone Hound</td>\n",
       "      <td>Red/Black</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A746717</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04/08/2017 09:00:00 AM</td>\n",
       "      <td>04/08/2017 09:00:00 AM</td>\n",
       "      <td>04/07/2016</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>1 year</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>White/Orange</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>A803667</td>\n",
       "      <td>Jack</td>\n",
       "      <td>09/04/2019 07:04:00 PM</td>\n",
       "      <td>09/04/2019 07:04:00 PM</td>\n",
       "      <td>09/04/2017</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>2 years</td>\n",
       "      <td>Domestic Shorthair</td>\n",
       "      <td>Orange Tabby</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>A834769</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05/20/2021 02:29:00 PM</td>\n",
       "      <td>05/20/2021 02:29:00 PM</td>\n",
       "      <td>04/18/2021</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>4 weeks</td>\n",
       "      <td>Domestic Shorthair</td>\n",
       "      <td>Lynx Point</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>A826518</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/04/2020 11:06:00 AM</td>\n",
       "      <td>12/04/2020 11:06:00 AM</td>\n",
       "      <td>11/30/2014</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>6 years</td>\n",
       "      <td>Miniature Poodle</td>\n",
       "      <td>Brown/Gray</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>A748803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05/11/2017 05:02:00 PM</td>\n",
       "      <td>05/11/2017 05:02:00 PM</td>\n",
       "      <td>02/20/2017</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>2 months</td>\n",
       "      <td>Labrador Retriever Mix</td>\n",
       "      <td>Black/White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>A701097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04/24/2015 07:59:00 AM</td>\n",
       "      <td>04/24/2015 07:59:00 AM</td>\n",
       "      <td>03/24/2015</td>\n",
       "      <td>Other</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>4 weeks</td>\n",
       "      <td>Bat</td>\n",
       "      <td>Black/Brown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Animal ID      Name                DateTime               MonthYear  \\\n",
       "0      A819419  *Chicken  09/22/2020 10:33:00 AM  09/22/2020 10:33:00 AM   \n",
       "1      A710946  *Sabrina  09/07/2015 12:28:00 PM  09/07/2015 12:28:00 PM   \n",
       "2      A572792      Oreo  05/23/2014 05:16:00 PM  05/23/2014 05:16:00 PM   \n",
       "3      A807189   Sequoia  10/27/2019 04:44:00 PM  10/27/2019 04:44:00 PM   \n",
       "4      A746717       NaN  04/08/2017 09:00:00 AM  04/08/2017 09:00:00 AM   \n",
       "...        ...       ...                     ...                     ...   \n",
       "4995   A803667      Jack  09/04/2019 07:04:00 PM  09/04/2019 07:04:00 PM   \n",
       "4996   A834769       NaN  05/20/2021 02:29:00 PM  05/20/2021 02:29:00 PM   \n",
       "4997   A826518       NaN  12/04/2020 11:06:00 AM  12/04/2020 11:06:00 AM   \n",
       "4998   A748803       NaN  05/11/2017 05:02:00 PM  05/11/2017 05:02:00 PM   \n",
       "4999   A701097       NaN  04/24/2015 07:59:00 AM  04/24/2015 07:59:00 AM   \n",
       "\n",
       "     Date of Birth Animal Type Sex upon Outcome Age upon Outcome  \\\n",
       "0       04/28/2020         Cat    Spayed Female         4 months   \n",
       "1       06/15/2015         Cat    Spayed Female         2 months   \n",
       "2       05/06/2007         Dog    Neutered Male          7 years   \n",
       "3       08/12/2019         Dog    Spayed Female         2 months   \n",
       "4       04/07/2016         Cat      Intact Male           1 year   \n",
       "...            ...         ...              ...              ...   \n",
       "4995    09/04/2017         Cat    Neutered Male          2 years   \n",
       "4996    04/18/2021         Cat      Intact Male          4 weeks   \n",
       "4997    11/30/2014         Dog    Spayed Female          6 years   \n",
       "4998    02/20/2017         Dog    Neutered Male         2 months   \n",
       "4999    03/24/2015       Other          Unknown          4 weeks   \n",
       "\n",
       "                                 Breed             Color  Outcome  \n",
       "0                   Domestic Shorthair  Blue Tabby/White        0  \n",
       "1               Domestic Shorthair Mix            Torbie        0  \n",
       "2         English Springer Spaniel Mix       Black/White        0  \n",
       "3     Labrador Retriever/Redbone Hound         Red/Black        0  \n",
       "4               Domestic Shorthair Mix      White/Orange        0  \n",
       "...                                ...               ...      ...  \n",
       "4995                Domestic Shorthair      Orange Tabby        0  \n",
       "4996                Domestic Shorthair        Lynx Point        0  \n",
       "4997                  Miniature Poodle        Brown/Gray        0  \n",
       "4998            Labrador Retriever Mix       Black/White        0  \n",
       "4999                               Bat       Black/Brown        1  \n",
       "\n",
       "[5000 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../data/animal_shelter_18202817.csv\")\n",
    "df_raw = df.copy()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67c242fd",
   "metadata": {},
   "source": [
    "The raw dataset has 5000 observations with 11 characteristics."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5355661",
   "metadata": {},
   "source": [
    "Key points to be noted from the data description:\n",
    "    \n",
    "- The animal shelter in question is concerned with reducing the risk of animal death.\n",
    "- A data analytics solution for death risk prediction will help them in their planning \n",
    "  towards improving the welfare of shltered animals.\n",
    "- Outcome = 1 implies a *negative* outcome, e.g. death.\n",
    "- Outcome = 0 implies a *positive* outcome, e.g. adoption, return to owner."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a67193",
   "metadata": {},
   "source": [
    "### 1. Convert features to appropriate data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4fd7b74f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Animal ID         5000 non-null   object\n",
      " 1   Name              3507 non-null   object\n",
      " 2   DateTime          5000 non-null   object\n",
      " 3   MonthYear         5000 non-null   object\n",
      " 4   Date of Birth     5000 non-null   object\n",
      " 5   Animal Type       5000 non-null   object\n",
      " 6   Sex upon Outcome  4999 non-null   object\n",
      " 7   Age upon Outcome  5000 non-null   object\n",
      " 8   Breed             5000 non-null   object\n",
      " 9   Color             5000 non-null   object\n",
      " 10  Outcome           5000 non-null   int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 429.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50fd9524",
   "metadata": {},
   "source": [
    "On examining the data description, we can make the following changes:\n",
    "- `DateTime`, `MonthYear`: `object` to `datetime64`, since both the date and time are provided.\n",
    "- `Date of Birth`: `object` to `period[D]`, i.e. period with frequency = day, since the time of birth is not provided (also insignificant).\n",
    "- `Animal Type`, `Sex upon Outcome`, `Age upon Outcome`, `Breed`, `Color`: `object` to `category`, since there are a finite set of text values for these features.\n",
    "- `Outcome`: `uint8`, since it is a single bit of information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b8fc97b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Animal ID                   object\n",
       "Name                        object\n",
       "DateTime            datetime64[ns]\n",
       "MonthYear           datetime64[ns]\n",
       "Date of Birth            period[D]\n",
       "Animal Type               category\n",
       "Sex upon Outcome          category\n",
       "Age upon Outcome          category\n",
       "Breed                     category\n",
       "Color                     category\n",
       "Outcome                      uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def change_types(df: pd.DataFrame, changes: dict) -> None:\n",
    "    for new_type, columns in changes.items():\n",
    "        for col in columns:\n",
    "            df[col] = df[col].astype(new_type)\n",
    "\n",
    "\n",
    "change_types(df, changes={\n",
    "    \"datetime64\": [\"DateTime\", \"MonthYear\"],\n",
    "    \"period[D]\": [\"Date of Birth\"],\n",
    "    \"category\": [\"Animal Type\", \"Sex upon Outcome\", \"Age upon Outcome\", \"Breed\", \"Color\"],\n",
    "    \"uint8\": [\"Outcome\"]\n",
    "})\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a2cc52",
   "metadata": {},
   "source": [
    "### 2. Check for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "13d5b8f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Animal ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>MonthYear</th>\n",
       "      <th>Date of Birth</th>\n",
       "      <th>Animal Type</th>\n",
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       "      <th>Age upon Outcome</th>\n",
       "      <th>Breed</th>\n",
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       "  <tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Animal ID, Name, DateTime, MonthYear, Date of Birth, Animal Type, Sex upon Outcome, Age upon Outcome, Breed, Color, Outcome]\n",
       "Index: []"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check rows\n",
    "df[df.duplicated(keep=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a05ebdc9",
   "metadata": {},
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       "      <th>9</th>\n",
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       "      <th>4990</th>\n",
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       "      <th>4998</th>\n",
       "      <th>4999</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DateTime</th>\n",
       "      <td>2020-09-22 10:33:00</td>\n",
       "      <td>2015-09-07 12:28:00</td>\n",
       "      <td>2014-05-23 17:16:00</td>\n",
       "      <td>2019-10-27 16:44:00</td>\n",
       "      <td>2017-04-08 09:00:00</td>\n",
       "      <td>2016-03-27 09:02:00</td>\n",
       "      <td>2013-10-07 17:06:00</td>\n",
       "      <td>2014-12-12 16:13:00</td>\n",
       "      <td>2019-06-13 12:36:00</td>\n",
       "      <td>2016-11-10 11:02:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2014-01-10 14:20:00</td>\n",
       "      <td>2014-12-03 18:28:00</td>\n",
       "      <td>2017-11-28 15:50:00</td>\n",
       "      <td>2019-01-18 11:02:00</td>\n",
       "      <td>2014-06-06 12:28:00</td>\n",
       "      <td>2019-09-04 19:04:00</td>\n",
       "      <td>2021-05-20 14:29:00</td>\n",
       "      <td>2020-12-04 11:06:00</td>\n",
       "      <td>2017-05-11 17:02:00</td>\n",
       "      <td>2015-04-24 07:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthYear</th>\n",
       "      <td>2020-09-22 10:33:00</td>\n",
       "      <td>2015-09-07 12:28:00</td>\n",
       "      <td>2014-05-23 17:16:00</td>\n",
       "      <td>2019-10-27 16:44:00</td>\n",
       "      <td>2017-04-08 09:00:00</td>\n",
       "      <td>2016-03-27 09:02:00</td>\n",
       "      <td>2013-10-07 17:06:00</td>\n",
       "      <td>2014-12-12 16:13:00</td>\n",
       "      <td>2019-06-13 12:36:00</td>\n",
       "      <td>2016-11-10 11:02:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2014-01-10 14:20:00</td>\n",
       "      <td>2014-12-03 18:28:00</td>\n",
       "      <td>2017-11-28 15:50:00</td>\n",
       "      <td>2019-01-18 11:02:00</td>\n",
       "      <td>2014-06-06 12:28:00</td>\n",
       "      <td>2019-09-04 19:04:00</td>\n",
       "      <td>2021-05-20 14:29:00</td>\n",
       "      <td>2020-12-04 11:06:00</td>\n",
       "      <td>2017-05-11 17:02:00</td>\n",
       "      <td>2015-04-24 07:59:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 5000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0                    1                    2     \\\n",
       "DateTime   2020-09-22 10:33:00  2015-09-07 12:28:00  2014-05-23 17:16:00   \n",
       "MonthYear  2020-09-22 10:33:00  2015-09-07 12:28:00  2014-05-23 17:16:00   \n",
       "\n",
       "                          3                    4                    5     \\\n",
       "DateTime   2019-10-27 16:44:00  2017-04-08 09:00:00  2016-03-27 09:02:00   \n",
       "MonthYear  2019-10-27 16:44:00  2017-04-08 09:00:00  2016-03-27 09:02:00   \n",
       "\n",
       "                          6                    7                    8     \\\n",
       "DateTime   2013-10-07 17:06:00  2014-12-12 16:13:00  2019-06-13 12:36:00   \n",
       "MonthYear  2013-10-07 17:06:00  2014-12-12 16:13:00  2019-06-13 12:36:00   \n",
       "\n",
       "                          9     ...                 4990                 4991  \\\n",
       "DateTime   2016-11-10 11:02:00  ...  2014-01-10 14:20:00  2014-12-03 18:28:00   \n",
       "MonthYear  2016-11-10 11:02:00  ...  2014-01-10 14:20:00  2014-12-03 18:28:00   \n",
       "\n",
       "                          4992                 4993                 4994  \\\n",
       "DateTime   2017-11-28 15:50:00  2019-01-18 11:02:00  2014-06-06 12:28:00   \n",
       "MonthYear  2017-11-28 15:50:00  2019-01-18 11:02:00  2014-06-06 12:28:00   \n",
       "\n",
       "                          4995                 4996                 4997  \\\n",
       "DateTime   2019-09-04 19:04:00  2021-05-20 14:29:00  2020-12-04 11:06:00   \n",
       "MonthYear  2019-09-04 19:04:00  2021-05-20 14:29:00  2020-12-04 11:06:00   \n",
       "\n",
       "                          4998                 4999  \n",
       "DateTime   2017-05-11 17:02:00  2015-04-24 07:59:00  \n",
       "MonthYear  2017-05-11 17:02:00  2015-04-24 07:59:00  \n",
       "\n",
       "[2 rows x 5000 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check columns\n",
    "dfT = df.T  # transpose dataframe and perform same check as done for rows\n",
    "dfT[dfT.duplicated(keep=False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "487c6260",
   "metadata": {},
   "source": [
    "It appears that there are no duplicate rows, but the `DateTime` and `MonthYear` columns are identical, so we can safely drop the latter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8c4d030b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicate rows remaining: 0\n",
      "Duplicate columns remaining: 0\n"
     ]
    }
   ],
   "source": [
    "df.drop(columns=[\"MonthYear\"], inplace=True)\n",
    "print(f\"Duplicate rows remaining: {len(df[df.duplicated(keep=False)])}\")\n",
    "print(f\"Duplicate columns remaining: {len(df.T[df.T.duplicated(keep=False)])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bb9a529",
   "metadata": {},
   "source": [
    "### 3. Check for constant columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d3d96d1",
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Animal Type</th>\n",
       "      <td>5000</td>\n",
       "      <td>5</td>\n",
       "      <td>Dog</td>\n",
       "      <td>2884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex upon Outcome</th>\n",
       "      <td>4999</td>\n",
       "      <td>5</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>1809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age upon Outcome</th>\n",
       "      <td>5000</td>\n",
       "      <td>44</td>\n",
       "      <td>1 year</td>\n",
       "      <td>869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Breed</th>\n",
       "      <td>5000</td>\n",
       "      <td>561</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>1199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Color</th>\n",
       "      <td>5000</td>\n",
       "      <td>224</td>\n",
       "      <td>Black/White</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count unique                     top  freq\n",
       "Animal Type       5000      5                     Dog  2884\n",
       "Sex upon Outcome  4999      5           Neutered Male  1809\n",
       "Age upon Outcome  5000     44                  1 year   869\n",
       "Breed             5000    561  Domestic Shorthair Mix  1199\n",
       "Color             5000    224             Black/White   544"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes([\"category\"]).describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dfcf3e86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Animal ID           4968\n",
       "Name                2417\n",
       "DateTime            4948\n",
       "Date of Birth       2944\n",
       "Animal Type            5\n",
       "Sex upon Outcome       5\n",
       "Age upon Outcome      44\n",
       "Breed                561\n",
       "Color                224\n",
       "Outcome                2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b48a2a45",
   "metadata": {},
   "source": [
    "All columns have cardinality > 1, hence there are no constant columns in the dataset. The issue of unnecessarily high cardinality of `Breed` and `Color` features will be dealt with when fixing data quality issues."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2316c83",
   "metadata": {},
   "source": [
    "Save the intermediate cleaned dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "62fc2c4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"../data/animal_shelter_cleaned_1ai.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc46e9f5",
   "metadata": {},
   "source": [
    "### 4. Check for outliers and typographical/data input errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f35f0dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Animal ID', 'Name', 'DateTime', 'Date of Birth', 'Animal Type',\n",
       "       'Sex upon Outcome', 'Age upon Outcome', 'Breed', 'Color', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d88f4e61",
   "metadata": {},
   "source": [
    "Since there aren't any numerical predictor features, there is no need to check for numerical outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2d35ce7",
   "metadata": {},
   "source": [
    "We can count the unique values in some of our features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d2261321",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------\n",
      "Dog          2884\n",
      "Cat          1878\n",
      "Other         223\n",
      "Bird           14\n",
      "Livestock       1\n",
      "Name: Animal Type, dtype: int64\n",
      "--------------------------------------\n",
      "Neutered Male    1809\n",
      "Spayed Female    1554\n",
      "Intact Female     650\n",
      "Intact Male       630\n",
      "Unknown           356\n",
      "NaN                 1\n",
      "Name: Sex upon Outcome, dtype: int64\n",
      "--------------------------------------\n",
      "1 year       869\n",
      "2 years      787\n",
      "2 months     629\n",
      "3 years      296\n",
      "3 months     227\n",
      "1 month      185\n",
      "4 years      176\n",
      "4 months     174\n",
      "5 years      153\n",
      "5 months     138\n",
      "6 months     109\n",
      "6 years      105\n",
      "7 years      101\n",
      "8 years       94\n",
      "8 months      90\n",
      "2 weeks       88\n",
      "3 weeks       75\n",
      "10 months     73\n",
      "4 weeks       68\n",
      "10 years      63\n",
      "9 months      56\n",
      "7 months      52\n",
      "9 years       50\n",
      "11 months     42\n",
      "12 years      39\n",
      "1 weeks       38\n",
      "11 years      37\n",
      "1 week        28\n",
      "2 days        21\n",
      "13 years      21\n",
      "3 days        20\n",
      "6 days        15\n",
      "15 years      14\n",
      "14 years      12\n",
      "1 day         11\n",
      "4 days        11\n",
      "16 years       8\n",
      "5 days         8\n",
      "5 weeks        4\n",
      "0 years        4\n",
      "17 years       3\n",
      "18 years       3\n",
      "-1 years       2\n",
      "19 years       1\n",
      "Name: Age upon Outcome, dtype: int64\n",
      "--------------------------------------\n",
      "Domestic Shorthair Mix          1199\n",
      "Domestic Shorthair               354\n",
      "Pit Bull Mix                     339\n",
      "Labrador Retriever Mix           301\n",
      "Chihuahua Shorthair Mix          241\n",
      "                                ... \n",
      "Dachshund/Pug                      1\n",
      "Dachshund/Italian Greyhound        1\n",
      "Dachshund/Cocker Spaniel           1\n",
      "Dachshund/Catahoula                1\n",
      "Yorkshire Terrier/Toy Poodle       1\n",
      "Name: Breed, Length: 561, dtype: int64\n",
      "--------------------------------------\n",
      "Black/White     544\n",
      "Black           423\n",
      "Brown Tabby     294\n",
      "Brown           210\n",
      "Brown/White     182\n",
      "               ... \n",
      "Tan/Buff          1\n",
      "Apricot           1\n",
      "Black/Silver      1\n",
      "Tan/Apricot       1\n",
      "Agouti            1\n",
      "Name: Color, Length: 224, dtype: int64\n",
      "--------------------------------------\n",
      "0    4631\n",
      "1     369\n",
      "Name: Outcome, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for col in [\"Animal Type\", \"Sex upon Outcome\", \"Age upon Outcome\", \"Breed\", \"Color\", \"Outcome\"]:\n",
    "    print(f\"{'-' * 38}\\n{df[col].value_counts(dropna=False)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a9e49da",
   "metadata": {},
   "source": [
    "Among other things, observations that stand out above are: `Age upon Outcome` as `-1 years` and `0 years`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba82d8e8",
   "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>Animal ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Date of Birth</th>\n",
       "      <th>Animal Type</th>\n",
       "      <th>Sex upon Outcome</th>\n",
       "      <th>Age upon Outcome</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2452</th>\n",
       "      <td>A687107</td>\n",
       "      <td>Montopolis</td>\n",
       "      <td>2018-02-28 11:18:00</td>\n",
       "      <td>2019-03-17</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>-1 years</td>\n",
       "      <td>Rhod Ridgeback</td>\n",
       "      <td>Red/Brown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2491</th>\n",
       "      <td>A797495</td>\n",
       "      <td>Ace</td>\n",
       "      <td>2019-06-15 12:44:00</td>\n",
       "      <td>2020-12-16</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>-1 years</td>\n",
       "      <td>Cairn Terrier</td>\n",
       "      <td>Black/Tan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2631</th>\n",
       "      <td>A666600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-11-04 14:54:00</td>\n",
       "      <td>2013-11-04</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>0 years</td>\n",
       "      <td>Beagle/Chihuahua Shorthair</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3170</th>\n",
       "      <td>A819590</td>\n",
       "      <td>*85 G Single</td>\n",
       "      <td>2020-07-01 14:53:00</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Female</td>\n",
       "      <td>0 years</td>\n",
       "      <td>Domestic Shorthair</td>\n",
       "      <td>Gray Tabby</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4287</th>\n",
       "      <td>A677463</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-04-25 15:17:00</td>\n",
       "      <td>2014-04-25</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0 years</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Blue</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4683</th>\n",
       "      <td>A795144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-05-16 18:16:00</td>\n",
       "      <td>2019-05-16</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0 years</td>\n",
       "      <td>Domestic Shorthair</td>\n",
       "      <td>White/Black</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Animal ID          Name            DateTime Date of Birth Animal Type  \\\n",
       "2452   A687107    Montopolis 2018-02-28 11:18:00    2019-03-17         Dog   \n",
       "2491   A797495           Ace 2019-06-15 12:44:00    2020-12-16         Dog   \n",
       "2631   A666600           NaN 2013-11-04 14:54:00    2013-11-04         Dog   \n",
       "3170   A819590  *85 G Single 2020-07-01 14:53:00    2020-07-01         Cat   \n",
       "4287   A677463           NaN 2014-04-25 15:17:00    2014-04-25         Cat   \n",
       "4683   A795144           NaN 2019-05-16 18:16:00    2019-05-16         Cat   \n",
       "\n",
       "     Sex upon Outcome Age upon Outcome                       Breed  \\\n",
       "2452    Neutered Male         -1 years              Rhod Ridgeback   \n",
       "2491    Neutered Male         -1 years               Cairn Terrier   \n",
       "2631      Intact Male          0 years  Beagle/Chihuahua Shorthair   \n",
       "3170    Intact Female          0 years          Domestic Shorthair   \n",
       "4287          Unknown          0 years      Domestic Shorthair Mix   \n",
       "4683          Unknown          0 years          Domestic Shorthair   \n",
       "\n",
       "            Color  Outcome  \n",
       "2452    Red/Brown        0  \n",
       "2491    Black/Tan        0  \n",
       "2631        White        0  \n",
       "3170   Gray Tabby        0  \n",
       "4287         Blue        0  \n",
       "4683  White/Black        0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Age upon Outcome\"].isin([\"-1 years\", \"0 years\"])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb51575e",
   "metadata": {},
   "source": [
    "- The 2 `-1 years` entries are clearly data input errors, with `Date of Birth > DateTime`. \n",
    "- For the 4 `0 years` entries, the `Date of Birth`, `DateTime` and `Outcome` columns indicate that the animals had a positive outcome on the same day that they were born, so these are not necessarily erroneous observations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "629043b4",
   "metadata": {},
   "source": [
    "### 5. Check for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c48deec0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Animal ID            0.00\n",
       "Name                29.86\n",
       "DateTime             0.00\n",
       "Date of Birth        0.00\n",
       "Animal Type          0.00\n",
       "Sex upon Outcome     0.02\n",
       "Age upon Outcome     0.00\n",
       "Breed                0.00\n",
       "Color                0.00\n",
       "Outcome              0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * (df.isnull().sum()/len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e38912",
   "metadata": {},
   "source": [
    "We note that that 29.86% of `Name` and 0.02% of `Sex upon Outcome` are missing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ae7c31",
   "metadata": {},
   "source": [
    "### 6. Data quality plan\n",
    "\n",
    "\n",
    "| Feature | Data type | Issue | Mitigation |\n",
    "| --- | --- | --- | --- |\n",
    "| `Animal ID` | `object` | Too many distinct values (noise). | Drop | \n",
    "| `Name` | `object` | 29.86% missing values. | Convert `Name` to binary feature: 1 if name exists, 0 if not. |\n",
    "| `DateTime` | `datetime64` | Inconsequential; the date/time of recording an observation can be assumed to have no significant impact on the outcome in this scenario. | Drop |\n",
    "| `Date of Birth` | `period[D]` | Duplicate information; the `Age upon Outcome` feature approximately conveys the same thing. | Drop |  \n",
    "| `AnimalType` | `category` | `Bird` (14) and `Livestock` (1) have very low frequency compared to `Cat` (1878) and `Dog` (2884). | `Bird` and `Livestock` can be grouped with the existing `Other` class. |\n",
    "| `Sex upon Outcome` | `category` | 0.02% missing values | Fill in missing values with existing `Unknown` class. |\n",
    "| `Age upon Outcome` | `category` | There are 2 instances of negative age (`-1 years`). There are 4 instances of age as `0 years`. Moreover, units for age are not consistent (days, weeks, months, years, etc.), thereby increasing the total number of categories. | Since the number of instances of negative age is very small (2), we can remove the observations as there is no reliable way of guessing the ages. The 4 entries with `0 years` age can be converted to `0 days` as per our analysis earlier. Regarding the age unit consistency, we can convert all entries to days since that is the smallest observed unit in the dataset. Furthermore, we can convert the days to an interval of days to reduce the number of distinct categories. |\n",
    "| `Breed` | `category` | Too many unique categories (561), including some with very low frequency. | Investigate further and decide how to group multiple breeds together. |\n",
    "| `Color` | `category` | Too many unique categories (224), including some with very low frequency. | Investigate further and decide how to group multiple colors together. |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08c56137",
   "metadata": {},
   "source": [
    "### 7. Drop unnecessary features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "db800294",
   "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>Name</th>\n",
       "      <th>Animal Type</th>\n",
       "      <th>Sex upon Outcome</th>\n",
       "      <th>Age upon Outcome</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>*Chicken</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>4 months</td>\n",
       "      <td>Domestic Shorthair</td>\n",
       "      <td>Blue Tabby/White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>*Sabrina</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>2 months</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Torbie</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oreo</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>7 years</td>\n",
       "      <td>English Springer Spaniel Mix</td>\n",
       "      <td>Black/White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sequoia</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>2 months</td>\n",
       "      <td>Labrador Retriever/Redbone Hound</td>\n",
       "      <td>Red/Black</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>1 year</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>White/Orange</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name Animal Type Sex upon Outcome Age upon Outcome  \\\n",
       "0  *Chicken         Cat    Spayed Female         4 months   \n",
       "1  *Sabrina         Cat    Spayed Female         2 months   \n",
       "2      Oreo         Dog    Neutered Male          7 years   \n",
       "3   Sequoia         Dog    Spayed Female         2 months   \n",
       "4       NaN         Cat      Intact Male           1 year   \n",
       "\n",
       "                              Breed             Color  Outcome  \n",
       "0                Domestic Shorthair  Blue Tabby/White        0  \n",
       "1            Domestic Shorthair Mix            Torbie        0  \n",
       "2      English Springer Spaniel Mix       Black/White        0  \n",
       "3  Labrador Retriever/Redbone Hound         Red/Black        0  \n",
       "4            Domestic Shorthair Mix      White/Orange        0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=[\"Animal ID\", \"DateTime\", \"Date of Birth\"], inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64fde777",
   "metadata": {},
   "source": [
    "### 8. Deal with `Name`\n",
    "\n",
    "If name is provided, put a value of 1, otherwise 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad933e7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3507\n",
       "0    1493\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Name\"].fillna(0, inplace=True)\n",
    "df[\"Name\"] = df[\"Name\"].apply(lambda x: 1 if x else 0).astype(\"uint8\")\n",
    "df[\"Name\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7b7a005e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Name\"].isnull().sum()  # verify"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "659e101d",
   "metadata": {},
   "source": [
    "### 9. Deal with `AnimalType` \n",
    "\n",
    "Group `Bird` and `Livestock` with `Other`, due to low frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "780b3465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dog      2884\n",
       "Cat      1878\n",
       "Other     238\n",
       "Name: Animal Type, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Animal Type\"].isin([\"Bird\", \"Livestock\"]), \"Animal Type\"] = \"Other\"\n",
    "df[\"Animal Type\"] = df[\"Animal Type\"].cat.remove_unused_categories()\n",
    "df[\"Animal Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fc40c0e",
   "metadata": {},
   "source": [
    "### 10. Deal with `Sex upon Outcome`\n",
    "\n",
    "Fill in missing values with `Unknown`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fd1efae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neutered Male    1809\n",
       "Spayed Female    1554\n",
       "Intact Female     650\n",
       "Intact Male       630\n",
       "Unknown           357\n",
       "Name: Sex upon Outcome, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sex upon Outcome\"].fillna(\"Unknown\", inplace=True)\n",
    "df[\"Sex upon Outcome\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5c40837f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sex upon Outcome\"].isnull().sum()  # verify"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2849dbbc",
   "metadata": {},
   "source": [
    "### 11. Deal with `Age upon Outcome`\n",
    "\n",
    "- Remove observations with `-1 years` as age.\n",
    "- Convert `0 years` to `0 days`.\n",
    "- Convert all age units to days and consequently rename the feature to `Age in Days upon Outcome`.\n",
    "- Group observations into intervals of days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "45902855",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove `-1 years` observations\n",
    "df.drop(df[df[\"Age upon Outcome\"] == \"-1 years\"].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "60461e52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert `0 years` to `0 days`\n",
    "df[\"Age upon Outcome\"] = df[\"Age upon Outcome\"].cat.rename_categories({\"0 years\": \"0 days\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "67b3252e",
   "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>Name</th>\n",
       "      <th>Animal Type</th>\n",
       "      <th>Sex upon Outcome</th>\n",
       "      <th>Age in Days upon Outcome</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>120</td>\n",
       "      <td>Domestic Shorthair</td>\n",
       "      <td>Blue Tabby/White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>60</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Torbie</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>2555</td>\n",
       "      <td>English Springer Spaniel Mix</td>\n",
       "      <td>Black/White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>60</td>\n",
       "      <td>Labrador Retriever/Redbone Hound</td>\n",
       "      <td>Red/Black</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>365</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>White/Orange</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name Animal Type Sex upon Outcome  Age in Days upon Outcome  \\\n",
       "0     1         Cat    Spayed Female                       120   \n",
       "1     1         Cat    Spayed Female                        60   \n",
       "2     1         Dog    Neutered Male                      2555   \n",
       "3     1         Dog    Spayed Female                        60   \n",
       "4     0         Cat      Intact Male                       365   \n",
       "\n",
       "                              Breed             Color  Outcome  \n",
       "0                Domestic Shorthair  Blue Tabby/White        0  \n",
       "1            Domestic Shorthair Mix            Torbie        0  \n",
       "2      English Springer Spaniel Mix       Black/White        0  \n",
       "3  Labrador Retriever/Redbone Hound         Red/Black        0  \n",
       "4            Domestic Shorthair Mix      White/Orange        0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make age units consistent (approximate: 1 week = 7 days, 1 month = 30 days, 1 year = 365 days)\n",
    "def convert_to_days(age: str) -> int:\n",
    "    n, unit = age.split()\n",
    "    days = int(n)\n",
    "    if \"year\" in unit:\n",
    "        return days * 365\n",
    "    elif \"month\" in unit:\n",
    "        return days * 30\n",
    "    elif \"week\" in unit:\n",
    "        return days * 7\n",
    "    else:\n",
    "        return days\n",
    "\n",
    "\n",
    "df[\"Age upon Outcome\"] = df[\"Age upon Outcome\"].astype(\"object\").apply(convert_to_days).astype(\"int64\")\n",
    "df.rename(columns={\"Age upon Outcome\": \"Age in Days upon Outcome\"}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12ae54bb",
   "metadata": {},
   "source": [
    "Discretise age into multiple bins. The `qcut` function defines the bins using quantiles based on the distribution of the data, not the actual numeric edges of the bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "399ab968",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90.0, 365.0]      1603\n",
       "(-0.001, 90.0]     1432\n",
       "(730.0, 6935.0]    1176\n",
       "(365.0, 730.0]      787\n",
       "Name: Age in Days upon Outcome, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Age in Days upon Outcome\"] = pd.qcut(np.array(df[\"Age in Days upon Outcome\"]), q=4)\n",
    "df[\"Age in Days upon Outcome\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "824231a8",
   "metadata": {},
   "source": [
    "### 12. Deal with `Breed`\n",
    "\n",
    "Perform an initial inspection of the top 20 most frequent breeds. The goal is to cut down the number of distinct breeds present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2d3703ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Domestic Shorthair Mix       1199\n",
       "Domestic Shorthair            354\n",
       "Pit Bull Mix                  339\n",
       "Labrador Retriever Mix        301\n",
       "Chihuahua Shorthair Mix       241\n",
       "German Shepherd Mix           123\n",
       "Domestic Medium Hair Mix      122\n",
       "Australian Cattle Dog Mix      74\n",
       "Domestic Longhair Mix          58\n",
       "Bat Mix                        54\n",
       "German Shepherd                51\n",
       "Labrador Retriever             49\n",
       "Pit Bull                       49\n",
       "Siamese Mix                    48\n",
       "Boxer Mix                      47\n",
       "Dachshund Mix                  46\n",
       "Bat                            45\n",
       "Chihuahua Shorthair            44\n",
       "Border Collie Mix              43\n",
       "Domestic Medium Hair           36\n",
       "Name: Breed, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Breed\"].value_counts()[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "169200b1",
   "metadata": {},
   "source": [
    "Convert `Breed` from `category` to `object` temporarily to make cleaning easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "175aca89",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Breed\"] = df[\"Breed\"].astype(\"object\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a42b2d",
   "metadata": {},
   "source": [
    "\"Mix\" breeds are created by breeding the specified breed with another purebred. Here, we can remove the word \"Mix\" from all breeds and group the items in question with the non-\"Mix\" purebred category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e0962c8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Domestic Shorthair                       1553\n",
       "Pit Bull                                  388\n",
       "Labrador Retriever                        350\n",
       "Chihuahua Shorthair                       285\n",
       "German Shepherd                           174\n",
       "                                         ... \n",
       "Parson Russell Terrier                      1\n",
       "Australian Cattle Dog/Chinese Sharpei       1\n",
       "Border Collie/Catahoula                     1\n",
       "Labrador Retriever/English Pointer          1\n",
       "Black Mouth Cur/Pit Bull                    1\n",
       "Name: Breed, Length: 465, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Breed\"] = df[\"Breed\"].str.replace(\"Mix\", \"\").str.strip()\n",
    "df[\"Breed\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79d8a1bd",
   "metadata": {},
   "source": [
    "Similarly, we can ignore the coat hair lengths/types of the animals (short, medium, long, wire)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "df47329e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Domestic Shorthair                    1553\n",
       "Chihuahua Shorthair                    285\n",
       "Domestic Medium Hair                   158\n",
       "Domestic Longhair                       70\n",
       "Chihuahua Longhair                      24\n",
       "                                      ... \n",
       "Tibetan Spaniel/Chihuahua Longhair       1\n",
       "Rat Terrier/Chihuahua Longhair           1\n",
       "Border Terrier/Chihuahua Longhair        1\n",
       "Carolina Dog/Chihuahua Shorthair         1\n",
       "Shih Tzu/Dachshund Longhair              1\n",
       "Name: Breed, Length: 62, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Breed\"][df[\"Breed\"].str.lower().str.contains(\"hair\")].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4fa3a833",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Breed, dtype: int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for hair in [\"Shorthair\", \"Medium Hair\", \"Longhair\", \"Wirehair\", \"Wire Hair\"]:\n",
    "    df[\"Breed\"] = df[\"Breed\"].str.replace(hair, \"\").str.strip()\n",
    "    \n",
    "df[\"Breed\"][df[\"Breed\"].str.lower().str.contains(\"hair\")].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a4652d1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Domestic                               1781\n",
       "Pit Bull                                388\n",
       "Labrador Retriever                      350\n",
       "Chihuahua                               309\n",
       "German Shepherd                         174\n",
       "                                       ... \n",
       "Great Pyrenees/American Bulldog           1\n",
       "Bull Terrier Miniature                    1\n",
       "Labrador Retriever/Great Dane             1\n",
       "Australian Cattle Dog/Border Collie       1\n",
       "Black Mouth Cur/Pit Bull                  1\n",
       "Name: Breed, Length: 454, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Breed\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60bb8e25",
   "metadata": {},
   "source": [
    "Note, there are several breeds which are a mix of two (Breed A/Breed B). One possible strategy to reduce the number of such entries is to consider the first (Breed A, assumed primary) only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a92d5311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Domestic              1782\n",
       "Labrador Retriever     436\n",
       "Pit Bull               413\n",
       "Chihuahua              357\n",
       "German Shepherd        197\n",
       "                      ... \n",
       "Pig                      1\n",
       "Abyssinian               1\n",
       "Dove                     1\n",
       "Pharaoh Hound            1\n",
       "Kangal                   1\n",
       "Name: Breed, Length: 188, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Breed\"] = df[\"Breed\"].apply(lambda x: x.split(\"/\")[0].strip())\n",
    "df[\"Breed\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c703cab6",
   "metadata": {},
   "source": [
    "Next, we can group together a few variants of breeds such as \"Pit Bull\", \"Terrier\", etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a110b6bf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Domestic', 1782),\n",
       " ('Retriever', 451),\n",
       " ('Pit Bull', 430),\n",
       " ('Chihuahua', 357),\n",
       " ('Shepherd', 260),\n",
       " ('Terrier', 222),\n",
       " ('Australian Cattle Dog', 100),\n",
       " ('Bat', 99),\n",
       " ('Dachshund', 90),\n",
       " ('Boxer', 68),\n",
       " ('Collie', 64),\n",
       " ('Husky', 59),\n",
       " ('Siamese', 59),\n",
       " ('Hound', 56),\n",
       " ('Poodle', 54),\n",
       " ('Beagle', 50),\n",
       " ('Bulldog', 42),\n",
       " ('Pointer', 40),\n",
       " ('Catahoula', 35),\n",
       " ('Great Pyrenees', 35)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if main type is present in the given feature value, and then replace\n",
    "def group_subtypes(feature: str, main_types: List[str]) -> str:\n",
    "    for main_type in main_types:\n",
    "        if main_type.lower().replace(\" \", \"\") in feature.lower().replace(\" \", \"\"):\n",
    "            return main_type\n",
    "    return feature\n",
    "\n",
    "\n",
    "df[\"Breed\"] = df[\"Breed\"].apply(\n",
    "    lambda x: group_subtypes(\n",
    "        feature=x, \n",
    "        main_types=[\"Pit Bull\", \"Terrier\", \"Retriever\", \"Shepherd\", \n",
    "                    \"Collie\", \"Corgi\", \"Pointer\", \"Poodle\", \"Husky\", \n",
    "                    \"Bulldog\", \"Hound\", \"Mastiff\", \"Spaniel\", \"Pinsch\"]))\n",
    "\n",
    "freq = df[\"Breed\"].value_counts().to_dict()\n",
    "list(freq.items())[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88989126",
   "metadata": {},
   "source": [
    "Finally, we group all breeds with frequency in interval [10, 45) as \"Rare\" and [1, 10) as \"Very Rare\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "82add637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Domestic                 1782\n",
       " Rare                      534\n",
       " Retriever                 451\n",
       " Pit Bull                  430\n",
       " Chihuahua                 357\n",
       " Very Rare                 263\n",
       " Shepherd                  260\n",
       " Terrier                   222\n",
       " Australian Cattle Dog     100\n",
       " Bat                        99\n",
       " Dachshund                  90\n",
       " Boxer                      68\n",
       " Collie                     64\n",
       " Husky                      59\n",
       " Siamese                    59\n",
       " Hound                      56\n",
       " Poodle                     54\n",
       " Beagle                     50\n",
       " Name: Breed, dtype: int64,\n",
       " 18)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def group_rare(breed: str) -> str:\n",
    "    if 10 <= freq[breed] < 45:\n",
    "        return \"Rare\"\n",
    "    elif freq[breed] < 10:\n",
    "        return \"Very Rare\"\n",
    "    else:\n",
    "        return breed\n",
    "\n",
    "\n",
    "df[\"Breed\"] = df[\"Breed\"].apply(group_rare)\n",
    "df[\"Breed\"].value_counts(), df[\"Breed\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e64b7f",
   "metadata": {},
   "source": [
    "Let us proceed with the reduced number of distinct breed categories obtained here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ccc9aca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Breed\"] = df[\"Breed\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2193a1c9",
   "metadata": {},
   "source": [
    "### 13. Deal with `Color`\n",
    "\n",
    "Similar to `Breed`, we can perform an initial inspection of the top 20 most frequent colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f82af8da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Black/White          544\n",
       "Black                423\n",
       "Brown Tabby          294\n",
       "Brown                210\n",
       "Brown/White          182\n",
       "Tan/White            162\n",
       "White                158\n",
       "White/Black          152\n",
       "Blue/White           134\n",
       "Tan                  125\n",
       "Brown Tabby/White    125\n",
       "Tricolor             121\n",
       "Orange Tabby         120\n",
       "Black/Tan            118\n",
       "White/Brown           95\n",
       "Black/Brown           93\n",
       "Tortie                92\n",
       "Brown/Black           85\n",
       "Blue                  85\n",
       "Calico                80\n",
       "Name: Color, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Color\"].value_counts()[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d199aea",
   "metadata": {},
   "source": [
    "Convert `Color` from `category` to `object` temporarily to make cleaning easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f5c081d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Color\"] = df[\"Color\"].astype(\"object\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "022e8ad8",
   "metadata": {},
   "source": [
    "From the initial examination, we see that a number of animals have color given in the form Color A/Color B. For simplicity, we can assume Color A to be primary and get rid of B."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "41b6aa36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Black              1238\n",
       "White               631\n",
       "Brown               515\n",
       "Brown Tabby         425\n",
       "Tan                 357\n",
       "Blue                225\n",
       "Orange Tabby        198\n",
       "Red                 137\n",
       "Tricolor            137\n",
       "Brown Brindle       119\n",
       "Blue Tabby          112\n",
       "Tortie               97\n",
       "Calico               87\n",
       "Chocolate            82\n",
       "Gray                 75\n",
       "Torbie               73\n",
       "Cream Tabby          48\n",
       "Sable                47\n",
       "Fawn                 45\n",
       "Cream                44\n",
       "Yellow               33\n",
       "Lynx Point           33\n",
       "Buff                 28\n",
       "Seal Point           22\n",
       "Blue Merle           21\n",
       "Gray Tabby           19\n",
       "Blue Tick            16\n",
       "Flame Point          13\n",
       "Black Smoke          12\n",
       "Black Brindle        12\n",
       "Black Tabby          11\n",
       "Gold                 10\n",
       "Brown Merle           9\n",
       "Silver Tabby          8\n",
       "Red Merle             8\n",
       "Orange                7\n",
       "Lilac Point           7\n",
       "Silver                6\n",
       "Red Tick              6\n",
       "Tortie Point          5\n",
       "Yellow Brindle        4\n",
       "Brown Tiger           3\n",
       "Calico Point          2\n",
       "Liver                 2\n",
       "Apricot               2\n",
       "Chocolate Point       2\n",
       "Pink                  2\n",
       "Agouti                1\n",
       "Green                 1\n",
       "Blue Point            1\n",
       "Name: Color, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Color\"] = df[\"Color\"].apply(lambda x: x.split(\"/\")[0].strip())\n",
    "df[\"Color\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6043337e",
   "metadata": {},
   "source": [
    "Next, we can remove words like \"Tabby\" and \"Point\"  and also group together several shades of the same base color and also."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8bbf52a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Black        1273\n",
       "Brown        1071\n",
       "White         631\n",
       "Blue          375\n",
       "Tan           357\n",
       "Orange        205\n",
       "Red           151\n",
       "Tricolor      137\n",
       "Tortie        102\n",
       "Gray           94\n",
       "Cream          92\n",
       "Calico         89\n",
       "Chocolate      84\n",
       "Torbie         73\n",
       "Sable          47\n",
       "Fawn           45\n",
       "Yellow         37\n",
       "Lynx           33\n",
       "Buff           28\n",
       "Seal           22\n",
       "Silver         14\n",
       "Flame          13\n",
       "Gold           10\n",
       "Lilac           7\n",
       "Pink            2\n",
       "Liver           2\n",
       "Apricot         2\n",
       "Agouti          1\n",
       "Green           1\n",
       "Name: Color, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for var in [\"Tabby\", \"Point\"]:\n",
    "    df[\"Color\"] = df[\"Color\"].str.replace(var, \"\").str.strip()\n",
    "    \n",
    "df[\"Color\"] = df[\"Color\"].apply(\n",
    "    lambda x: group_subtypes(\n",
    "        feature=x, \n",
    "        main_types=[\"Black\", \"Brown\", \"Blue\", \"Red\", \"Yellow\", \"Orange\"]))\n",
    "\n",
    "df[\"Color\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c7c0ab1",
   "metadata": {},
   "source": [
    "On doing further research on colors, we can group together similar shades such as \"Sable\" and \"Black\", \"Gold\" and \"Yellow\", \"Lilac\" and \"Pink\", etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e6750e88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Black', 1320),\n",
       " ('Brown', 1212),\n",
       " ('White', 631),\n",
       " ('Tan', 496),\n",
       " ('Blue', 375),\n",
       " ('Tricolor', 226),\n",
       " ('Orange', 218),\n",
       " ('Red', 151),\n",
       " ('Gray', 109),\n",
       " ('Tortie', 102),\n",
       " ('Yellow', 75),\n",
       " ('Torbie', 73),\n",
       " ('Pink', 9),\n",
       " ('Green', 1)]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def replace_color(df: pd.DataFrame, old_color: str, new_color: str) -> None:\n",
    "    df[\"Color\"] = df[\"Color\"].str.replace(old_color, new_color)\n",
    "    \n",
    "    \n",
    "replace_color(df, \"Sable\", \"Black\")\n",
    "replace_color(df, \"Chocolate\", \"Brown\")\n",
    "replace_color(df, \"Lynx\", \"Brown\")\n",
    "replace_color(df, \"Seal\", \"Brown\")\n",
    "replace_color(df, \"Liver\", \"Brown\")\n",
    "replace_color(df, \"Gold\", \"Yellow\")\n",
    "replace_color(df, \"Buff\", \"Yellow\")\n",
    "replace_color(df, \"Lilac\", \"Pink\")\n",
    "replace_color(df, \"Cream\", \"Tan\")\n",
    "replace_color(df, \"Fawn\", \"Tan\")\n",
    "replace_color(df, \"Apricot\", \"Tan\")\n",
    "replace_color(df, \"Silver\", \"Gray\")\n",
    "replace_color(df, \"Agouti\", \"Gray\")\n",
    "replace_color(df, \"Flame\", \"Orange\")\n",
    "replace_color(df, \"Calico\", \"Tricolor\")\n",
    "\n",
    "\n",
    "freq = df[\"Color\"].value_counts().to_dict()\n",
    "list(freq.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555f3089",
   "metadata": {},
   "source": [
    "Finally, any colors with frequency < 50 can be categorised as \"Other\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8721ab65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Black       1320\n",
       " Brown       1212\n",
       " White        631\n",
       " Tan          496\n",
       " Blue         375\n",
       " Tricolor     226\n",
       " Orange       218\n",
       " Red          151\n",
       " Gray         109\n",
       " Tortie       102\n",
       " Yellow        75\n",
       " Torbie        73\n",
       " Other         10\n",
       " Name: Color, dtype: int64,\n",
       " 13)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Color\"] = df[\"Color\"].apply(lambda x: \"Other\" if freq[x] < 50 else x)\n",
    "df[\"Color\"].value_counts(), df[\"Color\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16d4870e",
   "metadata": {},
   "source": [
    "We can proceed with the reduced number of color categories obtained here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4c905076",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Color\"] = df[\"Color\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dcb2191",
   "metadata": {},
   "source": [
    "Reset the DataFrame index and save. No data normalisation is necessary due to the absence of numerical predictor variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "26e4fbf6",
   "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>Name</th>\n",
       "      <th>Animal Type</th>\n",
       "      <th>Sex upon Outcome</th>\n",
       "      <th>Age in Days upon Outcome</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>(90.0, 365.0]</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>Blue</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>(-0.001, 90.0]</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>Torbie</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>(730.0, 6935.0]</td>\n",
       "      <td>Rare</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>(-0.001, 90.0]</td>\n",
       "      <td>Retriever</td>\n",
       "      <td>Red</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>(90.0, 365.0]</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4993</th>\n",
       "      <td>1</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>(365.0, 730.0]</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>Orange</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4994</th>\n",
       "      <td>0</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>(-0.001, 90.0]</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>Brown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>0</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>(730.0, 6935.0]</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>Brown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>0</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>(-0.001, 90.0]</td>\n",
       "      <td>Retriever</td>\n",
       "      <td>Black</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>(-0.001, 90.0]</td>\n",
       "      <td>Bat</td>\n",
       "      <td>Black</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4998 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name Animal Type Sex upon Outcome Age in Days upon Outcome      Breed  \\\n",
       "0        1         Cat    Spayed Female            (90.0, 365.0]   Domestic   \n",
       "1        1         Cat    Spayed Female           (-0.001, 90.0]   Domestic   \n",
       "2        1         Dog    Neutered Male          (730.0, 6935.0]       Rare   \n",
       "3        1         Dog    Spayed Female           (-0.001, 90.0]  Retriever   \n",
       "4        0         Cat      Intact Male            (90.0, 365.0]   Domestic   \n",
       "...    ...         ...              ...                      ...        ...   \n",
       "4993     1         Cat    Neutered Male           (365.0, 730.0]   Domestic   \n",
       "4994     0         Cat      Intact Male           (-0.001, 90.0]   Domestic   \n",
       "4995     0         Dog    Spayed Female          (730.0, 6935.0]     Poodle   \n",
       "4996     0         Dog    Neutered Male           (-0.001, 90.0]  Retriever   \n",
       "4997     0       Other          Unknown           (-0.001, 90.0]        Bat   \n",
       "\n",
       "       Color  Outcome  \n",
       "0       Blue        0  \n",
       "1     Torbie        0  \n",
       "2      Black        0  \n",
       "3        Red        0  \n",
       "4      White        0  \n",
       "...      ...      ...  \n",
       "4993  Orange        0  \n",
       "4994   Brown        0  \n",
       "4995   Brown        0  \n",
       "4996   Black        0  \n",
       "4997   Black        1  \n",
       "\n",
       "[4998 rows x 7 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(inplace=True, drop=True)\n",
    "df.to_csv(\"../data/animal_shelter_cleaned_1aii.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b9d758b",
   "metadata": {},
   "source": [
    "## 1B: Feature Selection\n",
    "\n",
    "The second major step after data cleaning is feature selection using filter and wrapper techniques.\n",
    "\n",
    "### 1. Understanding the cleaned dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9495f7d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4998 entries, 0 to 4997\n",
      "Data columns (total 7 columns):\n",
      " #   Column                    Non-Null Count  Dtype   \n",
      "---  ------                    --------------  -----   \n",
      " 0   Name                      4998 non-null   uint8   \n",
      " 1   Animal Type               4998 non-null   category\n",
      " 2   Sex upon Outcome          4998 non-null   category\n",
      " 3   Age in Days upon Outcome  4998 non-null   category\n",
      " 4   Breed                     4998 non-null   category\n",
      " 5   Color                     4998 non-null   category\n",
      " 6   Outcome                   4998 non-null   uint8   \n",
      "dtypes: category(5), uint8(2)\n",
      "memory usage: 36.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0cd8249",
   "metadata": {},
   "source": [
    "- Target variable: `Outcome`\n",
    "- Predictors: `Name, Animal Type, Sex upon Outcome, Age in Days upon Outcome, Breed, Color`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d957e485",
   "metadata": {},
   "source": [
    "### 2. Encoding categorical features\n",
    "\n",
    "We must encode some categorical features (`Age in Days upon Outcome`: ordinal; `Animal Type, Sex upon Outcome, Breed, Color`: one-hot) since numerical data is required to fit/evaluate models. Age is ordinal since there is a defined order of the age intervals, unlike the other 4 features being one-hot encoded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "100d88ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (90.0, 365.0]\n",
       "1     (-0.001, 90.0]\n",
       "2    (730.0, 6935.0]\n",
       "3     (-0.001, 90.0]\n",
       "4      (90.0, 365.0]\n",
       "5    (730.0, 6935.0]\n",
       "6     (365.0, 730.0]\n",
       "7    (730.0, 6935.0]\n",
       "8    (730.0, 6935.0]\n",
       "9    (730.0, 6935.0]\n",
       "Name: Age in Days upon Outcome, dtype: category\n",
       "Categories (4, interval[float64, right]): [(-0.001, 90.0] < (90.0, 365.0] < (365.0, 730.0] < (730.0, 6935.0]]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Age in Days upon Outcome\"].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "69e44133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    3\n",
       "3    0\n",
       "4    1\n",
       "5    3\n",
       "6    2\n",
       "7    3\n",
       "8    3\n",
       "9    3\n",
       "Name: Age in Days upon Outcome, dtype: int8"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Age in Days upon Outcome\"] = df[\"Age in Days upon Outcome\"].cat.codes\n",
    "df[\"Age in Days upon Outcome\"].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c512e67a",
   "metadata": {},
   "source": [
    "The age intervals have been encoded as 0 < 1 < 2 < 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f1859b7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age in Days upon Outcome</th>\n",
       "      <th>Outcome</th>\n",
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       "      <th>Animal Type_Dog</th>\n",
       "      <th>Animal Type_Other</th>\n",
       "      <th>Sex upon Outcome_Intact Female</th>\n",
       "      <th>Sex upon Outcome_Intact Male</th>\n",
       "      <th>Sex upon Outcome_Neutered Male</th>\n",
       "      <th>Sex upon Outcome_Spayed Female</th>\n",
       "      <th>...</th>\n",
       "      <th>Color_Gray</th>\n",
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       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Age in Days upon Outcome  Outcome  Animal Type_Cat  Animal Type_Dog  \\\n",
       "0     1                         1        0                1                0   \n",
       "1     1                         0        0                1                0   \n",
       "2     1                         3        0                0                1   \n",
       "3     1                         0        0                0                1   \n",
       "4     0                         1        0                1                0   \n",
       "\n",
       "   Animal Type_Other  Sex upon Outcome_Intact Female  \\\n",
       "0                  0                               0   \n",
       "1                  0                               0   \n",
       "2                  0                               0   \n",
       "3                  0                               0   \n",
       "4                  0                               0   \n",
       "\n",
       "   Sex upon Outcome_Intact Male  Sex upon Outcome_Neutered Male  \\\n",
       "0                             0                               0   \n",
       "1                             0                               0   \n",
       "2                             0                               1   \n",
       "3                             0                               0   \n",
       "4                             1                               0   \n",
       "\n",
       "   Sex upon Outcome_Spayed Female  ...  Color_Gray  Color_Orange  Color_Other  \\\n",
       "0                               1  ...           0             0            0   \n",
       "1                               1  ...           0             0            0   \n",
       "2                               0  ...           0             0            0   \n",
       "3                               1  ...           0             0            0   \n",
       "4                               0  ...           0             0            0   \n",
       "\n",
       "   Color_Red  Color_Tan  Color_Torbie  Color_Tortie  Color_Tricolor  \\\n",
       "0          0          0             0             0               0   \n",
       "1          0          0             1             0               0   \n",
       "2          0          0             0             0               0   \n",
       "3          1          0             0             0               0   \n",
       "4          0          0             0             0               0   \n",
       "\n",
       "   Color_White  Color_Yellow  \n",
       "0            0             0  \n",
       "1            0             0  \n",
       "2            0             0  \n",
       "3            0             0  \n",
       "4            1             0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features = [\"Animal Type\", \"Sex upon Outcome\", \"Breed\", \"Color\"]\n",
    "df = pd.concat([df.drop(columns=categorical_features), \n",
    "                pd.get_dummies(df[categorical_features], prefix=categorical_features)],\n",
    "              axis=1)\n",
    "    \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9c55ee21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4998, 42)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a26d220c",
   "metadata": {},
   "source": [
    "The encoded dataframe above has:\n",
    "- 4998 observations\n",
    "- 41 predictor variables\n",
    "- 1 target variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2200c575",
   "metadata": {},
   "source": [
    "### 3. Train-test split\n",
    "\n",
    "To avoid data peeking, we must split the dataset into train and test sets, and only use the training data for feature selection and model fitting. The test set will only be used for evaluation after the best model has been selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "75518a40",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"Outcome\", axis=1)\n",
    "y = df[\"Outcome\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4a5d021a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4629\n",
       "1     369\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "541455d5",
   "metadata": {},
   "source": [
    "- By default, 25% of the observations are set aside for testing.\n",
    "- Integral `random_state` ensures reproducible results.\n",
    "- Setting `stratify=y` maintains the target class imbalance when doing the split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "708c595e",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4956</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age in Days upon Outcome  Animal Type_Cat  Animal Type_Dog  \\\n",
       "4530     1                         1                0                1   \n",
       "2191     1                         1                0                1   \n",
       "3913     1                         1                1                0   \n",
       "4857     1                         1                0                1   \n",
       "4956     1                         1                0                1   \n",
       "\n",
       "      Animal Type_Other  Sex upon Outcome_Intact Female  \\\n",
       "4530                  0                               0   \n",
       "2191                  0                               0   \n",
       "3913                  0                               0   \n",
       "4857                  0                               0   \n",
       "4956                  0                               0   \n",
       "\n",
       "      Sex upon Outcome_Intact Male  Sex upon Outcome_Neutered Male  \\\n",
       "4530                             0                               1   \n",
       "2191                             0                               0   \n",
       "3913                             0                               0   \n",
       "4857                             0                               0   \n",
       "4956                             0                               0   \n",
       "\n",
       "      Sex upon Outcome_Spayed Female  Sex upon Outcome_Unknown  ...  \\\n",
       "4530                               0                         0  ...   \n",
       "2191                               1                         0  ...   \n",
       "3913                               1                         0  ...   \n",
       "4857                               1                         0  ...   \n",
       "4956                               1                         0  ...   \n",
       "\n",
       "      Color_Gray  Color_Orange  Color_Other  Color_Red  Color_Tan  \\\n",
       "4530           0             0            0          0          0   \n",
       "2191           0             0            0          0          0   \n",
       "3913           0             0            0          0          0   \n",
       "4857           0             0            0          0          0   \n",
       "4956           0             0            0          0          0   \n",
       "\n",
       "      Color_Torbie  Color_Tortie  Color_Tricolor  Color_White  Color_Yellow  \n",
       "4530             0             0               0            1             0  \n",
       "2191             0             0               0            0             0  \n",
       "3913             0             0               0            0             0  \n",
       "4857             0             0               0            0             0  \n",
       "4956             0             0               0            0             0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, stratify=y) \n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1a05e515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4530    0\n",
       "2191    0\n",
       "3913    0\n",
       "4857    0\n",
       "4956    0\n",
       "Name: Outcome, dtype: uint8"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6b31a22",
   "metadata": {},
   "source": [
    "### 4. Selection with Filters \n",
    "\n",
    "- Filter techniques for feature selection rank the features based on some predictiveness score (e.g. information gain, Gini index, chi-square statistic, relief algorithm, etc.). \n",
    "- Information gain (called mutual information when used for classification tasks) and Gini index are the most common when performing information theoretic analysis.\n",
    "- Mutual information measures the amount of dependency between two random variables and is based on entropy - a value of 0 indicates variables are completely independent. \n",
    "- We select mutual entropy/mutual information/information gain as the criterion for our filter technique for feature selection because it generally yields better results than Breiman's Gini index, although it is more computationally expensive than the latter.\n",
    "- Setting an integral value for `random_state` guarantees reproducible results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e7772110",
   "metadata": {},
   "outputs": [],
   "source": [
    "i_scores = mutual_info_classif(X_train, y_train, random_state=0)\n",
    "features_ig = pd.DataFrame(i_scores,index = X_train.columns, columns =[\"I-Gain\"])\n",
    "features_ig.sort_values(by=[\"I-Gain\"], ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4df24747",
   "metadata": {},
   "source": [
    "We can now plot the features with non-zero information gain in descending order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "58961896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 12))\n",
    "f = features_ig[features_ig[\"I-Gain\"] > 0]\n",
    "sns.barplot(x=\"I-Gain\", y=f.index, data=f, color=\"teal\")\n",
    "plt.xlabel(\"Information Gain\", fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "737ec948",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features selected by filter technique:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(['Animal Type_Other',\n",
       "  'Sex upon Outcome_Unknown',\n",
       "  'Breed_Bat',\n",
       "  'Name',\n",
       "  'Sex upon Outcome_Spayed Female',\n",
       "  'Animal Type_Dog',\n",
       "  'Breed_Poodle',\n",
       "  'Sex upon Outcome_Neutered Male',\n",
       "  'Breed_Shepherd',\n",
       "  'Animal Type_Cat'],\n",
       " 10)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select top 10 features ranked by I-Gain\n",
    "print(\"Features selected by filter technique:\")\n",
    "top_10_features_filter = list(features_ig[:10].index)\n",
    "top_10_features_filter, len(top_10_features_filter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "419bc6a9",
   "metadata": {},
   "source": [
    "### 5. Selection with Wrappers\n",
    "\n",
    "Wrapper techniques, although more computationally expensive than filters, are good at dealing with issues such as the lack of model bias and feature dependencies. The classifier is inherently part of the selection algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "33688a12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3471\n",
       "1     277\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b9ab67",
   "metadata": {},
   "source": [
    "- Due to the high target class imbalance, we will use F1 as the scoring metric for the sequential feature selector wrapper technique (that chooses a subset by adding/removing one feature at a time).\n",
    "- With `k_features` as `\"best\"` instead of an integer, the feature selector will return the feature subset with the best cross-validation performance.\n",
    "- A `cv` value of 10 implies 10-fold cross validation, where the score (based on selected metric) is computed 10 consecutive times (with different splits each time). \n",
    "- A value of `n_jobs = -1` forces the algorithm to use all available CPUs and generally provides a speed-up. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "13e18bb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform forward/backward sequential search with a given classifier\n",
    "def sequential_fs(clf: Union[DecisionTreeClassifier, GaussianNB, SVC, KNeighborsClassifier], \n",
    "                  forward: bool, \n",
    "                  k: Union[int, str] = \"best\", \n",
    "                  scoring: str = \"f1\", \n",
    "                  cv: int = 10,\n",
    "                  n_jobs: int = -1,\n",
    "                  figsize: Tuple[int, int] = (12, 6)) -> pd.DataFrame:\n",
    "    # Apply wrapper with cross-validation and measure execution time\n",
    "    t1 = time.time()\n",
    "    sfs = SFS(clf, k_features=k, forward=forward, scoring=scoring, cv=cv, n_jobs=n_jobs)\n",
    "    sfs = sfs.fit(X_train, y_train, custom_feature_names=X_train.columns)\n",
    "    t2 = time.time()\n",
    "    \n",
    "    dct = pd.DataFrame.from_dict(sfs.get_metric_dict()).T\n",
    "    mini, maxi = min(dct[\"avg_score\"]), max(dct[\"avg_score\"])\n",
    "\n",
    "    # Plot\n",
    "    plot_sfs(sfs.get_metric_dict(), ylabel=scoring, figsize=figsize)\n",
    "    plt.ylim([mini * 0.9, maxi * 1.1])\n",
    "    plt.title(f\"Sequential {'Forward' if forward else 'Backward'} Selection ({clf})\", fontsize=16)\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "    \n",
    "    # \"Best\" feature subset\n",
    "    subset = dct.loc[dct[\"avg_score\"] == maxi, \"feature_names\"].sort_index().iloc[0]\n",
    "    \n",
    "    # Display results\n",
    "    print(f\"Time taken to fit SFS: {t2 - t1:.3f} seconds\")\n",
    "    print(f\"Scoring: {scoring}\")\n",
    "    print(f\"Smallest feature subset (size = {len(subset)}) with max. score ({maxi}):\")\n",
    "    pprint(subset)\n",
    "    \n",
    "    print(\"\\nTop 5 feature subsets in descending order of average score and ascending order of size:\")\n",
    "    return {\n",
    "        \"sfs\": sfs,\n",
    "        \"subset\": list(subset),\n",
    "        \"avg_score\": maxi,\n",
    "        \"top5\": dct.rename_axis(\"#\").sort_values(by=[\"avg_score\", \"#\"], ascending=[False, True]).head()\n",
    "    }\n",
    "\n",
    "\n",
    "# Dictionary to store results for multiple classifiers\n",
    "sfs = defaultdict(dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7442b5e3",
   "metadata": {},
   "source": [
    "#### i. Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6718716d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit SFS: 7.536 seconds\n",
      "Scoring: f1\n",
      "Smallest feature subset (size = 17) with max. score (0.613577131139243):\n",
      "('Name',\n",
      " 'Age in Days upon Outcome',\n",
      " 'Animal Type_Cat',\n",
      " 'Animal Type_Dog',\n",
      " 'Animal Type_Other',\n",
      " 'Sex upon Outcome_Intact Male',\n",
      " 'Sex upon Outcome_Unknown',\n",
      " 'Breed_Australian Cattle Dog',\n",
      " 'Breed_Beagle',\n",
      " 'Breed_Boxer',\n",
      " 'Breed_Chihuahua',\n",
      " 'Breed_Collie',\n",
      " 'Breed_Hound',\n",
      " 'Breed_Pit Bull',\n",
      " 'Breed_Poodle',\n",
      " 'Breed_Rare',\n",
      " 'Color_Orange')\n",
      "\n",
      "Top 5 feature subsets in descending order of average score and ascending order of size:\n"
     ]
    },
    {
     "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>feature_idx</th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>feature_names</th>\n",
       "      <th>ci_bound</th>\n",
       "      <th>std_dev</th>\n",
       "      <th>std_err</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(0, 1, 2, 3, 4, 6, 9, 10, 12, 13, 14, 15, 18, ...</td>\n",
       "      <td>[0.8163265306122449, 0.7346938775510204, 0.5, ...</td>\n",
       "      <td>0.613577</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.078468</td>\n",
       "      <td>0.105651</td>\n",
       "      <td>0.035217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(0, 1, 2, 3, 4, 6, 9, 10, 12, 13, 14, 15, 16, ...</td>\n",
       "      <td>[0.8163265306122449, 0.7346938775510204, 0.5, ...</td>\n",
       "      <td>0.613577</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.078468</td>\n",
       "      <td>0.105651</td>\n",
       "      <td>0.035217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(0, 1, 2, 3, 4, 6, 9, 10, 12, 13, 14, 15, 16, ...</td>\n",
       "      <td>[0.8163265306122449, 0.7346938775510204, 0.5, ...</td>\n",
       "      <td>0.613577</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.078468</td>\n",
       "      <td>0.105651</td>\n",
       "      <td>0.035217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(0, 1, 2, 3, 4, 6, 9, 10, 12, 13, 14, 15, 16, ...</td>\n",
       "      <td>[0.8163265306122449, 0.7346938775510204, 0.5, ...</td>\n",
       "      <td>0.612423</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.079733</td>\n",
       "      <td>0.107354</td>\n",
       "      <td>0.035785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(0, 1, 2, 3, 4, 6, 9, 10, 12, 13, 14, 15, 16, ...</td>\n",
       "      <td>[0.8163265306122449, 0.7346938775510204, 0.5, ...</td>\n",
       "      <td>0.612423</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.079733</td>\n",
       "      <td>0.107354</td>\n",
       "      <td>0.035785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          feature_idx  \\\n",
       "#                                                       \n",
       "17  (0, 1, 2, 3, 4, 6, 9, 10, 12, 13, 14, 15, 18, ...   \n",
       "19  (0, 1, 2, 3, 4, 6, 9, 10, 12, 13, 14, 15, 16, ...   \n",
       "22  (0, 1, 2, 3, 4, 6, 9, 10, 12, 13, 14, 15, 16, ...   \n",
       "20  (0, 1, 2, 3, 4, 6, 9, 10, 12, 13, 14, 15, 16, ...   \n",
       "21  (0, 1, 2, 3, 4, 6, 9, 10, 12, 13, 14, 15, 16, ...   \n",
       "\n",
       "                                            cv_scores avg_score  \\\n",
       "#                                                                 \n",
       "17  [0.8163265306122449, 0.7346938775510204, 0.5, ...  0.613577   \n",
       "19  [0.8163265306122449, 0.7346938775510204, 0.5, ...  0.613577   \n",
       "22  [0.8163265306122449, 0.7346938775510204, 0.5, ...  0.613577   \n",
       "20  [0.8163265306122449, 0.7346938775510204, 0.5, ...  0.612423   \n",
       "21  [0.8163265306122449, 0.7346938775510204, 0.5, ...  0.612423   \n",
       "\n",
       "                                        feature_names  ci_bound   std_dev  \\\n",
       "#                                                                           \n",
       "17  (Name, Age in Days upon Outcome, Animal Type_C...  0.078468  0.105651   \n",
       "19  (Name, Age in Days upon Outcome, Animal Type_C...  0.078468  0.105651   \n",
       "22  (Name, Age in Days upon Outcome, Animal Type_C...  0.078468  0.105651   \n",
       "20  (Name, Age in Days upon Outcome, Animal Type_C...  0.079733  0.107354   \n",
       "21  (Name, Age in Days upon Outcome, Animal Type_C...  0.079733  0.107354   \n",
       "\n",
       "     std_err  \n",
       "#             \n",
       "17  0.035217  \n",
       "19  0.035217  \n",
       "22  0.035217  \n",
       "20  0.035785  \n",
       "21  0.035785  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(criterion=\"entropy\")\n",
    "clf_name = dt.__class__.__name__\n",
    "sfs[clf_name][\"forward\"] = sequential_fs(dt, forward=True)\n",
    "sfs[clf_name][\"forward\"][\"top5\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "96271b75",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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u9R+Y2Uy8yzxfN7MfO69f0jTg4/6jlMl4l/jCwKoSr5daVolpeJcHu/rIt9D6oue9N1vEGCAzexPeF+eteJdmV+L9Er56MNsr8LDbfCPlf8zsXn/bpwFn+Mt/gXeJ81y8H0LdeCeHHxfk3bvvldyQtI+//pecc6WO5V3AF8y7QXFfvBbU5Wb2rP/8ZbzP5d0FaS7Eu8T6TbyAvhOYjXeCKj4+q11RP0tgBkOsK865bryT8Z8BzOtvfwfepc95ePUHvJbHn5fYRNl+sH7a1+AFKKVMLvh/WG4KY/MJu3hEjFeLXu812PoeK1h3IOb4//eWbxpeX8xKjtFjA8zrV3jl/BTej6mMmd0MnOace6lMmkl4XaOKvYoXzE5k8z0UUPr4DfSzXWr0kkrOl2m8Vu2v+Y+tmNe//BX/6bYDLFfvNubgtag/hfd5XYoXsH0L7ybHXt/Ga6n7GN5l/C4z+yNwunNurXPuHvOGPzuFzZ+3e/Dej8cHU7YCAz3Plzrmve9bcb2ejNftoRJDGYmm91zzfJnXK9mHYiXPB865rN8QNJjzwat4LcH9yffx2kD3dTi+l4uP16QSy6D8ubL4e6X3eeFwvT8B/mre/T4v4n0WrnDOpSsoX+8PqOL7rsotH1UUdNeQc26FmV2N18dxR+A/eH0x7wO+UybZCja3oLSVeL0NL3DrlfT/L25NKf6wrvPXLb6xqDDfavkA3j4dUfgr3bwbBduHKxPn3CozW4t3KQvzbhw6HO/y3qaROczsdUVJ1/r/z8JroevLlcAE4DdmlnXeTXGF7sE7ye7nP67wl9/lP38Zr1XhfwVpjgJ+5ZzbNAa4mTWV280Sy1YCpW7yKVV/KuKce8DMbsNrOQOv/oDXz/2OEkn6Opmuw/ty+kKZ15/1/1/Llifuoej9cpqO112Ggue9ZRoO6/AC0IF6D97n8eGC7byId8WmlJf8/wd8jPyWzCuBK/3P3Lvxujz9Hu8qUSnr2XysCk3Hq4PFX/7DoVTdruR8GcL7zP0Y7wfG1hv2rvysMLOn8bpTfXUQ5TsI77P/IeeNEAN4N8AW5ZXxy/sdM5uO11/6UqABr+sGzrk/An/0P+fz/fVvMbPZrujm5QEa6Hm+3DEHr14XtiCvxeu2VYlS261Ub/7vpvRIMsWf3UryKjwfPNm70G9Vnlxim5U4F++ejv58A69rRykD3dfhUHy81lP6inO5c2UbBceQzd8zhT/IbsY7Z52I10jQTGVXkmFzkL+2wuWjioLuEWJmc5xzr5R4qfdO3d5fjbfgdQd4sq9LYmb2IPBBMzuv9yRs3kgJc9ky6O79e3e8myF6TyTvLtrkLXitvxOcc3dWul/9SFPZr84GvJbtTR928ybV2QYv0BgWZjYDmMLmy1BRvBaw4tbDY4ue/wuvZegEvNb4vjjn3MlmlgUWmNlHnXN/KHhxo5n9Fy+Q3pXN3UjuwgvAlxUs69VQooyf7Kcche4HPmlmb+ntYuK37JUL4Dbxu7v0DrFVuDyI90OxtwXkWbyT6G7OuYsGUDbw6t4pwFLnXF8tQ7cBR5jZDOdcudarFN4JvD/3+P8fhdfdq9fR/v/3VrCNSjzDALsvmDdxxWF49zP0DjN4C96P0y7n3DN9JL8NOMfM3uCcG2iLN/7Vtt/755IT+1j1HuBUM5vb2xru14kPA/91znUONO9BquR8mTKz+4A34PU37yto/TbesI2nOecuLX7RzN4IrHOlRzDpDa4LGw52wut/W/IKjXPuVeBqMzuEEgGrf7XsJv/K2A/wAsChDH05HOf53vq3PVsG6bcBR5nZe/0uaUPV20pb/B1yO96PqG2cc7cPQz7g3UeTwjsfFB6XD+PFSfeUStSPq9i6q18pfTVoVWNfUwysNfge4Egze5tz7p8Fyz+K11hSPPzuh9jyO+wovH34T+8C51zezK7Eu2H7HcAdrvIhI7fD6y5Z/Hnfzv//WUYxBd0jZ7GZ3Y13ufBFvBvMDgFOAq4vOImfi1c57zWzy/ECmYl4J+TtnXO9E398He8k9xe/8k7F+8VcPLTbg3gteZf4gVbvUFtbDBfnnFtkZr/Da1251C9DHi+IPwTvzvnnBrjPTwHvMbNb8H6lryjTH/cWvBEZrjWzX+D15f4alV+qLGdv88bmDuBdMj4dL7i/AjYFwA8AXzKzlXi/kI+jqKXQOddpZmcBPzKzP+Hdud0J7AEknXM/Ks7YOXeqn/dvzSzgnPt9wct3+WVZ7ZzrbRFYhPdLfTLel2uhW4BPmNkTeJcZj8DrZ16pX+Kd3G4ws6/inShPwquD/dkZuNvMfo33ZbQar7vK8Xh18rP+/joz+xzeGKoRvH59a/FaOf4PL6DeKojxfR/vy+0+84aSehbvjvldgHc45w731/s6Xgvwv8zs23jHYhbe6Ae9w1c9BXzWzD6MV+87nXNbnYSdc0/69f08/0fov/CCt68BvxuGy/i97sX7wTPZOVeqVWoPM5uCdyVqG7xWzyPxvmzPKljvOrwfWneaN3nLY36aHfAC9Pf5Afr38b4M7zBvdtQn8H5oHg6cVCoYNrOr8Orz/Xjv7054N27eVrxuge/j/Ti93cy+jteV5LN+2vf0kW64VXq+PA3vvbjVzH6O92NxCt6oJkHn3JkAzrnf+N3dvmdmb8Wrx6/iXeZ/D95xmUfp7lJ34F2x+5X/Hs3AOycvpWB4XjO7Ee/9ewTvvPhGvFbyK/3Xv4n3ubkbLyCbjTd6yaMl7hEZkGE6z/8H73tkL7zRYHr9Bu+m7d+Z2YV4/Y6b8W4KvqyfH4ulPId3PI8zs/V+ns86514ws+8Al5vZznhBYRKvS9YBwNXOubvLbbQU59x6/3icZWbdeC2xr8W7Mf4fbH1PQiXbXMEQrxBXY1/xzpHvMLND8er22j66kYF3g+sX8L4/zsb7AXm0n/+JJbozHmJml+CdP/bCO2//qkS9+jleC/8b8BoUKrU3pRtF9sa7SrxkANsaecV3VupRnQdekLMQr+U5idd3+L/AVyi4o95fdzZef+bleK3FK/G+hD9WtN5H8AKUFN7lnPdTNMqAv95u/vIuvC+A0yg9lFQA78P1mF/Gjf7fF+O1jMDmu9qPL0o7n62HYHob3uXxJAV3aVP6zvtT8H6MJPB+KLyreF9K5VHmWJ/HlneH5/FOfn8F9ipady7eCBOdeAHH5XhfrqXu1v4g3hdJAi/I+DdwaMHrjqLRWvAuC2eBjxQsO9hft3hYwscoPdLMFLybUTb4j+vwbsBxwLEF611L0agPBa9tj/dF0oPXUvYDvJbMrfIrSteKF9j09ofP+GW4m6Ihw/z134rXurPBf99f8sv+1oJ1tnhf/WUT8QK5F/Hq/Gq8bgOnFq23A97Nemvx6v0SCkZCwLvkebP/frrefCg9ZGAY70v1ZX+/XvafF979P5cK63uZ49d7Cf4TRct7y9P7SPj5/xkv6LYS24rh1e1n/H1fj/dZOY8thyqbhtfKttI/lq/g/fCKFn4+Ctb/BJuH5Ev578H38Ydn6+M92xlvOLmN/nv9AP7wbyU+i6Gi5ddSZgQjyo9estWoNG5g58vX4tXF3v1chndOPqTENg/BC7TW+HVjFd5QmO8t3reidB/y358k3jn5qOJ9xRs68QG8y/IJvHP4efj1Du/8c6u/Hyn//fs5W44MtdXxo4LRS/xlgz7PF2zj98DdJZY34Q33+XLBe/FHNo+ucyxlRu0ps08n4n3Gs2z9/XKMfxy78b7bnsY7f88uWGerc3K5zy/evQhf9N+P3rL/mILPQbltFhyvY4vzGo7HYPe1VLnwGjPuw/sucL11hjKfVf+1GXgDDPSedx9n689X73u7D95npQvvHPVjikaPKUhzK34XsAqPwxy87/NDS7z2HPDdahz/4XyYX1gZI8yfwMI5N7+2JRER8Cb9wPtyfFetyyIyHMybVOYuvB+xfd0kLeOEmR2LNzDBjm7LWXbLrT8RrxHwMudcyRucS6Q5A/gM3tC/uYLle+NdrXytG/gV+RGlIQNFRKrrG8DbzWxerQsiMhycN3zcHXhXakUqZt6MuW/HuxoXwBvJpJJ0MbwrNOe6rbu0nAn8crQH3KCgW0Skqpw3Lu2xbB7+S2Qs+DywzMys3zVFNnsPXveWvfC63VU6fORcvG6Rvy5c6Afj/8WbhGfUU/cSEREREZEqU0u3iIiIiEiVKegWEREREamycTFO95QpU9zcuXNHPN/u7m4aGxtrkr43bSaXJ53NY0AgYFTS+S6VTBCNDW4m1VqlrWXe2uf6SFvLvLXPI5s2GIkSDgaJhAberjQc593xkraWeWuf6yNtLfMearmH4uGHH17rnJtavHxcBN1z587loYceGvF8Fy1axPz580c8vXOOu+5exOTtX0cmm6M5HiUYqPxelyWLH2L73Qc30EKt0tYyb+1zfaStZd7a55FNO3e3N9PelWK7thamTRhY8D6U8/Z4S1vLvLXP9ZG2lnkPtdxDYWYvl1o+LoLuevTc8nZ60lkeWTLwCciccyQzOcLBAI3RcBVKJyIyegXMmNAY5cXVHURCAVobo/0nEhGpMgXdo1Ayk2NjT5qgGY2xwQXNoYARCQWHuWQiIvUhGDCaYxGef7WD185uVQOEiNScbqQchboSaXpHPg2YDeohIjLehUMBYuEgz61oJ5Upnk9DRGRkKegehdZ3pYiE1UotIjJU0XCQgAX438qNZHP5WhdHRMYxBd2jTC6fpyORJqagW0RkWDREQ6QyOZas6iCX14RwIlIbCrpHme5kFpcHzawrIjJ8WhoitHeneWVtJ5qJWURqQUH3KNPekyI0iLFlRUSkb62NEVZtTLKyvafWRRGRcUjR3SjinGN9Z4p4RF1LRESGm5nR2hBh6Zou1nYma10cERlnFHSPIslMjkw2TzCgt0VEpBoCAaO1McKSVzvoSKRrXRwRGUcU3Y0inYkMFc3TLiIigxYMBGiMhXhuRTuJdLbWxRGRcUJB9yiyvjNJPKL5ikREqi0SChIJeWN4p7Maw1tEqk9B9yiRyeXpTGaI6CZKEZEREY+EyDt4fuVGcnmN4S0i1aUIb5ToTmbAaahAEZGR1BQL05PK8uLqTvIaSlBEqkh9GUaJ9u40YbVyi4iMuAmNUdZ3poiEumpdFBEZwxTljQJ551jflSSm/twiIjXR2hhh5YYetXaLSNVUNeg2s4PM7Fkze97Mziyzznwze9TMnjSze/xlO/vLeh8dZnaq/9p5Zra84LVDqrkPIyGRzpLLOYIBdS0REakFM6MhEiKdzWvGShGpiqo1rZpZEPgxcACwDHjQzBY6554qWKcV+AlwkHNuqZlNA3DOPQvsUbCd5cCfCzb/fefcd6tV9pHWmchgCrhFRGoqFgmRyzvau1NMbIrVujgiMsZUs6V7L+B559wS51waWAAcXrTOR4EbnHNLAZxzq0tsZ3/gBefcy1Usa02t60hqFkoRkVEgGDCWretWNxMRGXZWrctoZvZBvBbs4/3nxwB7O+dOLljnMiAM7AY0Az9wzv2qaDvXAI845y73n58HHAt0AA8BX3LObSiR/wnACQBtbW1vXrBgwTDvYf+6urpoamrqcx0HJFLZkl1LUskeorGGQeU93tLWMm/tc32krWXe2uf6SNubPhSJEwkHCQ3wCmQl5/yxlLaWeWuf6yNtLfMearmHYt99933YOTeveHk179wrdbYqjvBDwJvxWrPjwP1m9oBz7jkAM4sAhwFnFaT5KfAtf1vfAr4HHLdVRs5dBVwFMG/ePDd//vyh7MugLFq0iP7ybe9O8dyKjUxsim712pLFD7H97lu9ZxUZb2lrmbf2uT7S1jJv7XN9pO1NP2eXN5HO5XjdNpMIBiq/IFzJOX8spa1l3trn+khby7yHWu5qqGb3kmXAnILns4EVJda5xTnX7ZxbC9wLvKHg9YPxWrlX9S5wzq1yzuWcc3ngZ3jdWOrW+s4k0bC6loiIjBbhUIB0Js/ajlStiyIiY0g1g+4HgR3NbDu/xfooYGHROjcC7zCzkJk1AHsDTxe8/hHgd4UJzGxGwdP3A4uHveQjJO8cG7rTxBR0i4iMKi0NYZav7yKbG3szVaazObqSGdZ2JMjk8qQyuVoXSWRcqFr3Eudc1sxOBm4FgsA1zrknzewk//UrnHNPm9ktwONAHrjaObcYwA/CDwBOLNr0xWa2B173kpdKvF43elJZ8s4R0MglIiKjSjAQIJ93rO5IMHNiY62LMyh550hncqSyeXpSGboSWbqSGbIFU95nsnkef3kdc6Y0MrWlQUPXilRRVWdjcc7dDNxctOyKoueXAJeUSNsDTC6x/JhhLmbNdPSkCWjadxGRUakpHmH5um6mNMeIhKp3RTKdzW0aqtDMMIOA/3/QrGiZETBvXPFCmVyedCZHIp2lJ5WlK+n97/xbqYIBIxwMEI+Gtgis1weM5niEV9Z2s6o9wbZTm2lt3PoeIxEZOk2BWEPrOpPENQuliMioFAwYATNWtSeYM6U6oyBkc3meW7GRVCbH/1Zu3LTc8C7nFv+/6XWDoAUIBLwJ1h59ce2m18LBAOFQgJaG8FbBeSnBgNHaGCWdzfHcinZaG6NsM6WparMk5/J5OhMZNCqjjDeK+GokmcmRzORobdRbICIyWjXFw6xs72HqhPiw33/jnOPlNZ0k09lNge9A0joHDofZwNKWEwkFiTQF6U5meHzpemZNbKCttYFQcOi3fznn6ElnWdeRZE1Hklw+TyKdZUNXUhMRybihiK9GupOZWhdBRET6ETAjFAiwcn0327W1DOu2X23vYV2nF3SuG2Da3i4nYCXH5x2KxliYeN6xckMPazqSbDu1idbGaEWt5sUyuTzt3Slebe8hkcoSCgZpioUJBIz1AeO5FRuZPTnLzEmNg9q+SD1R0F0j6ztTGipQRKQONMVCrOlI0NbaQEN0eL4227tTLF3bRWvD6Ow/HQgYExqjZLJ5nlvZwYSGMNtMaa5o//PO+aOjJFnXmQSgIRraqkXbgNamKMvXd5PI5Jg7tXlYWtVFRivV7hrI5fNsTCjoFhGpB2ZGJBRkxfquYdleIp3lfys30hyLjPrRq8KhAJOaoqQyeZ5Yuo5X1naRKTOMYjKT49X2bh5/eR3PLmunoydNS0OE1sZo2RtRA2ZMbIqxsTvN08s3kExnq7k7IjWllu4a6E5lcQ6NXCIiUicaY2HWdSaZPjFDUyw86O1kcnmeX7mRaDhIOFQ/7V4N0RCxSJDVGxOs6UiwzZQmJjV7Ldft3SlWbeyhozsDBk2xMI3RgR2jloYIPaksi19Zz47TJzBBI6jIGFQ/n/gxZGNPekBTC4uISO3FoyFeWdeFG+SwG3nneGlVB+lsvi5HrgqY0dIQIR4JsWRVB0++sp6eVJbnVrSTyuSZ0Oi1ag+2i0hDNERDJMwzy9t5tb1n0MdZZLSqv099nXPO+UMFqmuJiEg9iUdCbOhK0pnI0NIQGXD6leu72dCdZmJTfbfihoIBJjbFSGdzBAI2rKOPhEMBWhujvLymi0QqyzZTm9RINQrk9QNoWCjoHmGpTI50Nj/gS28iIlJ7DdEwS9d2seuciQPqIri+M8my9d1jauKZSCg47COngHcT58TGCOs6kyTSWXaYPkH3QI2gXD5PTypHdyrNhq40PaksD7+wZtDbS2fzdKcyintQ0D3iOpMZ9ekREalT0XCQ9q4UG7tTFbfwdqcyvLCqg5Z4RPfyVMjMGz2lO5nhqWXr2XFG65D60kt5m4PsDBu6UnQlMxjeexANBwc8hnwh5xxr83meXLqBloYwMyY20hKvbNKmsUhB9whb35ms2ixfIiJSfQ2xEC+v7aKlIbrFlOqlpLPeTJOxcFDD4Q1CYyxMKpPjqVc2sH1bM1Na4gPeRi7vyGRzpLJ5kuksmVyeTC5PeJy+H97ERDm6kqWD7AkNkWELis3MH6EmSiKd5dnl7cQiQWZObGBiU3TcdR1S9DeCsrk8HYkMEwbRF1BEREaHSChITyrL+q4kU/sIAvPO8dLqTvJ5R2NcrbSDFQ0HCQWNJas66ElnmTWpqeR6uXyedDZPyp/xuSuZoSeVJZXJYYDDuxk0k83zxMvr2GZKE5ObY2O+1TXvHHnneLW9hw1dKbpTGXDVCbL7Eo+EiEdCpLM5Xlzdyctrupg5qYHJzbGyQ0qONQq6R5A3VKAb8x9wEZGxrikWZtm6Lib10Vq3fF037WPgxsnRIBjwbrB8dUOCRCpH3jnau1Mk0lm6Elm60xky2fwWwXU4FCAcDGw1UszagBGLhFiyqpM1HUm2mdo0JvsbZ3J5NnSlWLGhm2Q6x/J13UTDQVriIxNklxMJBYmEguTyeZav62bZum6mTYgzbUK8Lkf1GYixvXejTHt3ivA4+TUnIjKWhYIBsknHmo4k01sbtnp9bUeCFeu7FXAPI/O7KXQmMiTTXredYMAIBwPEwsEBBc7hYGBTl4fFS9czc2IjMyY2jIkuQMl0ljWdSVa19+Cc10UnGLBBjbhTTcFAgAmNUfLOsa4zxar2Hlobo0yf2EBzbGz2+1bQPUKcc6zvTBEfpimERUSktprjYZav62Zyc2yL/sHdyQxLVnUyobG2LYpjVXM8zJoh3NxXKB4JEQ0HWdXew9rOBNtOaWZiU3RY3zfnHIl0jo5EimzekUhniYWDw55HVzLLq+3dbOhKEwxYXcx4Ct5VieZ4GAjTk8ryzLINxKMhZk1srHXRhp0iwBGSSOfI5vP93nQjIiL1IRgI4JxjdXsPsyZ7/Yx7b5xsiIbG3U1i9Srgj5SSyeb536sdtDZE2GZq05C7OiTSWTb2pFm9MUEq7Y1pns7kWPzyekLBAK1NESY2RmmIhgbdpzmXz9PenWbFhm4SqSzRcIjWOv6x1xAN0RANkcrkeG7lRrL5sTU+uILuEdKZSNfth0BEREprjkdYsaFn06gaL6zqwIHGla5D4VCASU1Rbzr6l9czc1IDba0D63KSzOTY2J1idUeCRDpHwIyGaIh4kxdurQ0YrU1RcnlHR0+GtR1JcBCLBJnUFKW5IUJDJNRvnulsjnWdSVas7yHvHA3R0LBOUlRr0XAQM2jP5mtdlGGloHuErOtMaRZKEZExJhDwhkRbuaGbdDZPVyIzpibAGY8aoiFi4SArN/SwpiPJtlObaG0s3+UklcnRkUizuj1BdypDwIx4NMTEPupBMGCbWnXBu+lx1UYviMagORZmYlOUpniYeCS0aXz37lSG1e0J1nYkMYOmeHjMXlGJhILknSOZzo6ZoZbHxl6McumsN+i8TsQiImNPUzzMqo0JMrm8hoQdIwIBr8tJOut1c5jYGGWbKU2bgr90NkdHT5rVHUm6ExkwhtTaHA4GCMe94Nk5Rzqb55V13bi888sSIZnJ8eTSDYRDAVoax89ESxu6U8xQ0C2V6klla10EERGpkoB5N621B0zdCMeYSCjIpKYg3ckMjy9dz4zWBlKZHI++tA7DuxGzdZhHqOkdP7u3i1LeOXpSWZxj3I2GEwgYqzd6IwSNhc+Wgu4RsL4rNW4GfhcRGY/CobF5iV88jbEw8bxj9cYEeQetIzShDHg/6rwuJiOS3ahieN13elJZGmP1P5a6zhJVlneODV0pYrqpRkREpG4F/LGuA8aYaHWtF6FggPXdqVoXY1go6K6yRCpLzrm6GCtTREREZDRpiIZYszFBbgwMH6igu8o6EmmC+kUsIiIiMmDBgJHNObqTmVoXZcgUdFfZ2o7kkAfYFxERERmvIqEAazuTtS7GkCnoriLnIJnO6QYbERERkUGKR0Os70qSzdX3ZDmKBqso7xxY/fdBEhEREamVgBnOebN71zMF3VWUzeWJhtW1RERERGQoouEgqzvqu4uJgu4qyeUdubzbNLi9iIiIiAxOLBxkY0+adDZX66IMmoLuKsk7r1vJeJmmVURERKRazAwDNvbUbxcTBd0iIiIiMuo1REOsbk/UuhiDpqBbREREREa9SChITypLMp2tdVEGRUG3iIiIiNQFCxgb6nRaeAXdIiIiIlIXGqIhVm1M4Fz9Dclc1aDbzA4ys2fN7HkzO7PMOvPN7FEze9LM7ilY/pKZPeG/9lDB8klmdruZ/c//f2I190FERERERodwMEAmk6cnVX9dTKoWdJtZEPgxcDCwK/ARM9u1aJ1W4CfAYc653YAjizazr3NuD+fcvIJlZwJ3Oud2BO70n4uIiIjIOBAMBlhfh11MqtnSvRfwvHNuiXMuDSwADi9a56PADc65pQDOudUVbPdw4Jf+378E3jc8xRURERGR0a4hGmLNxgS5fH11MbFq9Ykxsw8CBznnjvefHwPs7Zw7uWCdy4AwsBvQDPzAOfcr/7UXgQ2AA650zl3lL293zrUWbGODc26rLiZmdgJwAkBbW9ubFyxYUI3dLMsBnR2dROMNg95GKtlDNDa49OMtbS3z1j7XR9pa5q19ro+0tcy7HtPWMm/tc32krWbeubwjFg4SCJSeD6Wrq4umpqZB5TtU++6778NFvTQAqOYc5aWOQnGEHwLeDOwPxIH7zewB59xzwNuccyvMbBpwu5k945y7t9LM/SD9KoB58+a5+fPnD2YfBi2Ty3P7HXex/e5bHfOKLVn80KDTj7e0tcxb+1wfaWuZt/a5PtLWMu96TFvLvLXP9ZG2mnn3pLI0x8Ns39ZS8vVFixYx0rFff6rZvWQZMKfg+WxgRYl1bnHOdTvn1gL3Am8AcM6t8P9fDfwZr7sKwCozmwHg/19JlxQRERERGSNikSDru5Jkc/laF6Vi1Qy6HwR2NLPtzCwCHAUsLFrnRuAdZhYyswZgb+BpM2s0s2YAM2sE3g0s9tMsBD7h//0JfxsiIiIiMk4EzHAOOupoWviqdS9xzmXN7GTgViAIXOOce9LMTvJfv8I597SZ3QI8DuSBq51zi81se+DPZtZbxt86527xN30RcL2ZfQpYytYjnoiIiIjIGBcNB1nTmWBSc6zWRalINft045y7Gbi5aNkVRc8vAS4pWrYEv5tJiW2uw+sDLiIiIiLjVCwcpL0nTTqbIxIK1ro4/dKMlCIiIiJSd8yMALCxTrqYKOgWERERkboUj4ZY3Z6odTEqoqBbREREROpSJBSkO5UlkR7908Ir6BYRERGRuhUIGO11MC28gm4RERERqVsN0RCrNiao1izrw0VBt4iIiIjUrXAwQCaTpyc1uruYKOgWERERkboWDAZY15msdTH6pKBbREREROpaQzTEmo4kufzo7WKioFtERERE6lowYOTyju5kptZFKUtBt4iIiIjUvWg4yNpR3MVEQbeIiIiI1L1YJMj6riTZXL7WRSlJQbeIiIiI1L2AGc5BxyidFl5Bt4iIiIiMCdFwkDWdo3NaeAXdIiIiIjImxMJBNvZkGI1jmCjoFhEREZExwcwwGJVDByroFhEREZExIxQMjMop4RV0i4iIiIhUmYJuEREREZEqU9AtIiIiIlJlCrpFRERERKpMQbeIiIiISJUp6BYRERERqTIF3SIiIiIiVaagW0RERESkyhR0i4iIiIhUmYJuEREREZEqU9AtIiIiIlJlCrpFRERERKpMQbeIiIiISJUp6BYRERERqTIF3SIiIiIiVaagW0RERESkyhR0i4iIiIhUmYJuEREREZEqq2rQbWYHmdmzZva8mZ1ZZp35ZvaomT1pZvf4y+aY2d1m9rS//AsF659nZsv9NI+a2SHV3AcRERERkaEKVWvDZhYEfgwcACwDHjSzhc65pwrWaQV+AhzknFtqZtP8l7LAl5xzj5hZM/Cwmd1ekPb7zrnvVqvsIiIiIiLDqZot3XsBzzvnljjn0sAC4PCidT4K3OCcWwrgnFvt/7/SOfeI/3cn8DQwq4plFREpaeENYebv2czBB+7H/D2bWXhDuNZFEhGROlTNoHsW8ErB82VsHTjvBEw0s0Vm9rCZfbx4I2Y2F3gj8O+CxSeb2eNmdo2ZTRzmcouIAF7Afc7pcVYsD+CcsWJ5gHNOjyvwFhGRATPnXHU2bHYkcKBz7nj/+THAXs65UwrWuRyYB+wPxIH7gfc4557zX28C7gEucM7d4C9rA9YCDvgWMMM5d1yJ/E8ATgBoa2t784IFC6qyn+U4oLOjk2i8YdDbSCV7iMYGl368pa1l3uNpn++6s41rf7EDa9bEmDo1ybGffIH99l9V9XyHI+1A0zsHH/nw22lvj2712rRpCX71m39VJd/hTFvLvOsxbS3zrse0tcxb+1wfaWuVd945sqkEzc3Ng8p3qPbdd9+HnXPzipdXrU83Xsv2nILns4EVJdZZ65zrBrrN7F7gDcBzZhYG/gRc1xtwAzjnNn3Dm9nPgJtKZe6cuwq4CmDevHlu/vz5Q96hgcjk8tx+x11sv/tWx7xiSxY/NOj04y1tLfMeL/u88IYwP/xhnGTCAFi9Os4Pf7gb0+Zsz2FHZKqW73ClrTT9+nXGDb8Pc/1vI7S3B0uus3p1jP8ufisHH5ahoYLvg9G+z0pb+7zrMW0t89Y+10faWuXdlcywdsnjjHTs159qdi95ENjRzLYzswhwFLCwaJ0bgXeYWcjMGoC9gafNzICfA0875y4tTGBmMwqevh9YXLU9EJFNLr0wting7pVMGN+9IFajEg0f5+D+fwQ59aQ473hTMxefH2fyFEfrxHzJ9YMhOOu0Bt7+xha+fmaMxY9r9FUREelb1Vq6nXNZMzsZuBUIAtc45540s5P8169wzj1tZrcAjwN54Grn3GIzeztwDPCEmT3qb/KrzrmbgYvNbA+8HhwvASdWax9EBBI98Pe/hlmx3Eq+/urKAG9/YzM775pjl9fmvf93zbHdDnnCBV2fF94Q5tILY6xcsR8zZjpOOytZcQv5UNL2Zd1a48/Xh/n9dRFefjHIhNY8Rx+b5kNHp3nNTvlNfboLf2zE4o7zL04wY3aeP/w2wg3XR/jdr6LsunuODx2d5r3vT9PcMuSiiYjIGFPN7iX4QfLNRcuuKHp+CXBJ0bJ/ACW/4Z1zxwxzMUWkhKcWB7j+uggLb4jQ1WkEQ45cduv1Wibk+b99sjz7VJBr/xkik/Y+uuGI4zU7ekF4Lge33hQm7b+2YrlxzulxgH6D5+LAdyBpC7dRGLS/94g0r7wc4Pa/h8lkjHl7Zzn5tB4OPCRDLL45Xe/2vbS2VcC/594JzvlmgoU3RLj+ugjnnRXnO9+McfB7M3zo6DTLlga49KLh/7EgIiL1p6pBt4jUl64uuOkvYa6/LsLix0JEY44D35Phw0enWbk8wDlf2brV99wLNgeSmQy8+EKAZ54K8uxTQZ55KsC/7g2xetXW3S+SCeP0U+Kcd1ackvdz+8t6esC5rbu1nHVanBv/GKap2dHcAs3NjuYW5z93m54/8mCQn/wgRiq5OWi/8kcx4vEtW7XLOeyIDIcdkSnbt7BlAnzsk2mOPjbNE48Fuf66CH/7S5gbro9g5jaVfTA/FkREZOxQ0C0yzmzVVePMJNtul+f310W4+cYwPT3Gzq/Ncc63Ehz+gTQTWntT5sDKt/oChMOw0y55dtolDwXLd57VslXgDF5f6g8elfaelLi2ZQa/uDJScj8yaWhvN5YtDdDZaXR22qbAuhKtE+Gr30hWvH5/zOD1e+R4/R4Jzjovwfw9m9nYvuWPjWTCuPTCmIJuEZFxSEG31L1q9fcdi0p11Tj983GcM+Jxx3ve57Vqv/6NOaxE/Npfq285M2a6kn3CZ85y/Qa+t95Uuj/5zFmOP93cvcWydBq6Oo3ODqOrEzo7jY8f2UipiP7VlZUH6APV2AgdG0tvf8VyI5+HgO69FBEZV3Tal7qmyUsGptQIJM4ZE1rz/PPRDr79vQRveFPpgHsoTjsrSSy+ZR+SWNz7gTScaSMRmDTZse12eXZ7fZ63vC3HzFml5yKYMbM6cxT0v33jAwc38cA/Sw9HKCIiY5OCbqlr5Yaxu/TC+h/Gbjgtfdn4wSXRsiOQdGw0mqo4h8BhR2Q4/5IEM2flMXPMnJXn/EsSFV2RGEpaGFrAPxTl8v3IJ1KsX2d8/MgmTvpEAy/8T6dhEZHxQN1LpG5tbKdsELlyRfW6DtSL7m6va8affh/hwQdCmDmiUUiltl632q2+MPiuKcORFvrui14NfeWbPDfJL6+OcsWPohy6XxMf/liaU76UYvKU6r8PIiJSG2pikbqzcrnx7fNivHNeC2VGlsQ546jDGvnNLyKsXzf6AvCFN4SZv2czBx+4H/P3bB627jD5PPzn/iBnnBrnbW9o4cwvNrBmtXHaWUnuebCTC76XqEmrb60ddkSGRQ928vdb72LRg50j1ue/XL6xOJx4Soo77+/kqGPS/P43Ed71f81c8aMoycSIFE1EREaYWrqlbjz3TICrfxrlpj+HcQ7e874Mr9kpx08u27KLSTTm2O/dGZ5/Nsg3z45zwbkx3vbOLO99f4b9D8zQ1FTDnaA6404fe0KSzo4Af/5DhGVLAzQ2OQ59f4YjPpTmjfM299GuVauvlDZpsuPr307ysU+m+e4FMS69MMbvfhnZ9CPo+0MY43s0TkYkIjKeKeiWUc05eOjfQX72kyiL7ggTjzuOPjbNsSekmDXba7GdOcuVDSKffTrAX/8c5qY/Rzj9lDAxPyB/7/szvGPfLLfcNPLBRbl+6Gd/Kc6dt4ZomeC8Rwu0TPDGmvaee38/8M8g3/nWlkH7t7/eADje+vYcp34lyQEHZYg3lM5/KF01pDp22DHPT6/t4d//CvKdb8Y4/ZSGIY3xPZQfdsPxo1BERLamoFtqrlSr2nsOz3DHrSGu/kmUxx4JMWlynlO/kuQjH08zcdKW3SP6CiJ3fm2enV+b4rQzUzzyYJCb/hLm5oVhbl4YIR7Pk04budzIBRf5fPl+6KkUPPNUkM4OY+NG2zSzY6Xapjt+eX13/yvKqLX3/+X4483dvOV1zbRv2HqM76+eFudXP4+QzRiZjDcZUTZjpDPeuOWZjJHNQDIJxV2vkgnjjC/EueryKLG4o6HB61oUjzvicYg3eMsW/Dpc9uZkBd0iIoOnoFtqqlSr2llfjHPRN2KsXRNgzrY5zrswwREfSm8xPfdABQIwb+8c8/bOcfY3k/zz3hBfOKFhU8Ddq5rBxcP/CXLBuTHK9UOfOctx631dgNfCn0pCR4fR0WF0btz8/2mfi5fcxupVo6/vugxcIAAb20u/l+k0TJjgCIUc4QiEw73/+3+Hvb+v/mnpCYVyOZi7fZ5EDyQSxppVARIJSPSY93+i/ARDK1cYzjHsw0kWUrcWERnLFHRLTZXqapHJGBvb4bIrejjwPRmCwzyccTgM8/fP+q2BW1ux3Fi/zpg0eXhGknh1hXHx+TFu+kuEthl5PvqJFDdcH9lqOvXCmxnNvJvtYnHHtLYty/Hdb8dKtpaPxAgkMjL6mkzo57/t6Tf9zQvLTyh0+dV9p5+/ZzMrlm99j71zxn57N3PgezIc/N5M2QmUBkvdWkRkrNPoJVJT5Yb2y2bhkMOGP+Au1NfkJfP3bOYbX42x9OXBRxXJBPz4+1EOfEczt98S5nOnJrn1vk7OuzBZl+NOy8gZ6ns87JMRxRwfPjrFa3bO8etrIhx5aBPz92zm21+P8ciDQfL5zesOZGQe57wfpf+8J8Q3zy59r8N5Z8VY8OsId98R4qnFAdav81rci1VrRCARkeGilm6pqXIteiPRanvaWcktWtbAC0xOPi3Ji88Huf66CL/7VYQD35PhU59J8/o9chVt1zm45aYQF38rzvJlAQ5+b5rTz0kye87mfarHcadl5Az1PR5K+v7SdmyEu24Pc8tfw1z3ywjX/ixK24w8Bx6SoanZcc2V0a1aq52DN87L8sJzQV74X4DnnwvywvPe/91dff+w7eo0zj1jy75lkaijbXqe6TO8/7s6jX/eGyKTUSu5iIxeCrqlpj7/5SRnnRbfNEoDjFyrbX/BxalnJPnVzyMs+HWUv/81wp5vyXL8Z1O8c78sgTLXiJ5aHODbX4/zn/tD7Lxrjl//sYu9/6+yYH2gZdcIJGPbUN/jak1G1DIB3vfBDO/7YIauTj8AvynMgt9ESKe2DqCTCeP0U7a8D2HqtDw77JTn/Uem2WHHPK/ZKcfppzTw6sqtP1gzZjl+f2Mnr64MsOpV8/5fGeDVld7fj/03yCsvByh146hu/hSR0URBt9TUC88HcM6YNCXPhnUj32rbV3DRNt1x+tkpPvOFFNdfF+GXP4ty4scbec1OOY47MUUgAD/8rnfTV9t0x9ztc/znfm/Iv29+J8GRH01XtXuMSK01NW/+DHV1wZt2Kj9h1fmX9PCanfLssGOOCa1bv/7ls0tfefrSWUmmz3RMn1n+x+vOs1pKdjlZsdx49OEge7x5+H/4iogMlIJuqZlHHw7y859GOfKjaS74bmLUtto2NcFxJ6Y55rg0f/9rmKt/EuWrX/LGxe4NMLxWN+Md87Nc+pOekkGFyFjW1OTdqFnuBs4PHd33D+mhdIkp100tEIAPvbeJd+6f4QunJ9n99fkSqUVERoZupJSaSCbgjFPjtE13nPX1+pj3Ohz2AoMbb+9i8pQ8W7foGS/8L6iAW8atod4AetgRGRY92Mnfb72LRQ92Dvnm4m9dkuBLZyV57JEgRxzUzGePa+CZp/S1JyK1oZZuqYnLLo7x4gtBfrGgi6bmWpdmYMxg/bryYxmLjFe1usm3v3yPPjbFtT+Lcs2VUe64pZmD35vmlC+neM2OavkWkZGjn/wy4h7+T5BfXBXhIx9P8bZ96rOvZbnRVTRWtox3g22trma+Tc1w8mkp7v53B5/5QpJ77wpz6L5NfPmUOC8t8b4GNeSgiFSbgm4ZUYkeOPOLcWbNdpx+Tv2OK62xskXqz4RW+OIZKe58oJPjTkxz29/CHPzOJo4+opGzvxxnxXLvxu4VywOcc3pcgbeIDCsF3TKiLr0oxssvBvn2pT00NdW6NIN32BGZIU1wIyK1M2my4ytfS3LnA5187JNpHnwgSCpZeshBEZHhoj7dMmL+c3+QX14d5WOfTPGWt9Vnt5JCGitbpL5NneY4+5veePylhhzUPRoiMpzU0i0jorsbzjotzpxtc3z5bHXBEJHRQ/doiMhIUNAtI+K7F8RYtjTARZclaGiodWlERDbTPRoiMhLUvUSq7v5/BLnu2ijHfjrFnnvXf7cSERlbCocc9CbZMY7/TEr3aIjIsFJLt1RVVxd89bQG5m6f44tnqNVIREan3iEH/3LjIqZOy/PPe0Il+3mLiAyWgm6pqou/5bUcXfT9BHF1KxGRUS4Wz/P505P89+EQt/1dF4NFZPgo6Jaq+ceiEAt+HeW4E9O8aU91KxGR+vCBD2fYYccc3/t2jIx6mIjIMFHQLVXR2QFnfznO9q/J8YXT1a1EROpHKARfOSfJS0uC/P43kVoXR0TGCAXdUhUXfiPOqleN71yWIBavdWlERAZm/ruy7PXWLJdfGqWrs9alEZGxQEG3DLt77grxx99FOP6zKd7wJnUrEZH6YwZf+VqS9esCXP3TaK2LIyJjgIJu2WThDWHm79nMwQfux/w9m1l4Q3hQaU/8eANt0/N8/kupKpZWRKS6Xr9HjvccnuaaK6K8ulKzU4rI0CjoFsALms85Pc6K5QGcM1YsD3DO6fGKAu/itPm80b7BuOVvlQftIiKj0RfPTJLLwQ+/G6t1UUSkzlV1PCQzOwj4ARAErnbOXVRinfnAZUAYWOuce2dfac1sEvB7YC7wEvAh59yGau7HeHDphTGSiS1bcpIJ4+wvxbnh930Hzw//J0QqtWXaVMq49MKYJpcQkbq2zbaOoz+Z5ldXRzj20yl22iVf6yKJSJ2qWku3mQWBHwMHA7sCHzGzXYvWaQV+AhzmnNsNOLKCtGcCdzrndgTu9J/LEK1cUfrSaSrlBd99PVJlepGU26aISD35zOdTNDbBJReotVtEBq+aLd17Ac8755YAmNkC4HDgqYJ1Pgrc4JxbCuCcW11B2sOB+f56vwQWAWdUcT/GhWnTHatK9FmcOcuxYGF3n2nn79nsT528pRkzNZ2biNS/iZMcJ52S5JIL4tz/jyBvfbtuEBeRgatmn+5ZwCsFz5f5ywrtBEw0s0Vm9rCZfbyCtG3OuZUA/v/Thr3k40wuB03NDtgySI7FHaed1f8Y26edlSQWH1xaEZF68PFPpZk5K88l58fIq4eJiAyCOVed1kgzOxI40Dl3vP/8GGAv59wpBetcDswD9gfiwP3Ae4A3lEtrZu3OudaCbWxwzk0skf8JwAkAbW1tb16wYEFV9rMcB3R2dBIdwtznqWQP0djg0g8k7XW/2Y5f/2p7Djp4GY88PJk1a2JMnZrk2E++wH77r6poG3fd2ca1v9hhUGkHU+bhTl+PaWuZdz2mrWXe2uf6SNtf+jvumM53L96NM85czL77bX1+q8d9Vv2qn7zrMW2t8s47RzaVoLm5eVD5DtW+++77sHNuXvHyanYvWQbMKXg+G1hRYp21zrluoNvM7sULuPtKu8rMZjjnVprZDGA1JTjnrgKuApg3b56bP3/+EHdnYDK5PLffcRfb777VMa/YksUPDTp9pWn//a8g1/2mkcOOSHPJj5oxS7Nk8b/8tHPY8m0ob/vd4fgvDC7tQMtcjfT1mLaWeddj2lrmrX2uj7T9pT9+V/jb33L85je7csyJc4hEK087lHxHa9pa5q19ro+0tcq7K5lh7ZLHGenYrz/V7F7yILCjmW1nZhHgKGBh0To3Au8ws5CZNQB7A0/3k3Yh8An/70/425BBWLfW+NLnGth2bp7zLkpguu9RRKSsQAC+8rUEy5cF+M21mh5eRAamakG3cy4LnAzcihdIX++ce9LMTjKzk/x1ngZuAR4H/oM3NODicmn9TV8EHGBm/wMO8J/LAOXzcPopcdrbjcuu7KGpqdYlEhEZ/d62T453zM/w0x9E2dhe69KISD2p6jjdzrmbgZuLll1R9PwS4JJK0vrL1+H1AZch+NmPo/zjnjDfuCjBa3fTXUEiIpU6/Zwkhx/QxBU/jHHGubphXEQqoxkpx6GH/h3ksoujHPzeNEcdk651cURE6souu+Z5/5EZfnVNhGWvqF+eiFRGQfc4s2G9cdpnG5g5O8/5l6gft4jIYJz6lSSBAFz2HU2YIyKVUdA9jjgHZ54aZ91a4wdX9tDcUusSiYjUp+kzHcd+OsXCGyI8+bi+SkWkfzpTjCPXXBnh7jvCnPG1JLu/Xv24RUSG4oTPpZg4Kc93zo9TpSkvRGQMUdA9Tjz2SJDvfTvGAQdnOOZT6sctIjJUzS3wuS+meOAfId76+mYOPnA/5u/ZzMIbwrUumoiMQoMKus1MA8zVkY3tcOpJDbRNd3z7ez3qxy0iMkyamh1mjvXrAjhnrFge4JzT4wq8RWQrg23pfmpYSyFV4xx89UsNrHrVuOyKHia01rpEIiJjxw8vieHcli0ZyYTx7a/HePzRIK+uNLLZ8ukX3hBm/p5qJRcZD8qO021mp5V7CVBLd5349TURbv97mDPOTfCGN+VqXRwRkTFl5YrSlw7XrwvwwUO8r0ozx5SpjmltjmlteaZNz9M23bFiubHwTxHSaW8bK5Yb55weB+CwIzIjswMiMmL6mhzn23iT1pT6ja6+4HVg8eMBvvOtGPPfleGTJ6gft4jIcJsx0wuei02dludbFydYvSrAqleNVa8GWL3KWLkiwGP/DbJ+Xemv0WTCuPTCmIJukTGor6D7EeAvzrmHi18ws+OrVyRZeEOYSy+MsXLFfsyY6TjtrGTFJ+DCtIEANDY5vnNZgoB+JomIDLvTzkpyzulxkonNgXcs7jjj3CT7vbt8v5J0Gl63XctWXVPAa/F+aUmAudtrlCmRsaSvUGw58LKZfaHEa/OqVJ5xb+ENYc45Pc6K5QO/Kac4bS5nJJPGfYv6+m0lIiKDddgRGc6/JMHMWXnMHDNneROP9ddQEol4reTlHPiOJj5/QgNPPBYc7iKLSI30FY3tCjQCx5nZr/D6cvfSda8qufTC2BYtJrD5ppzmlr4Hgv3217dOm07pUqWISDUddkSGw47IsGTxQ2y/e+VtUmVbyb+W4NWVAX77yyi33BTmrW/PcsLnUvzfPlmNPiVSx/oKuq8EbgG2Bx5my6Db+ctlmPV1U86JH28c1m2KiEjt9DaGeF0CbavuhCeenOL3v4nwi6uifPIjjez2uhyf/lyKA9+TIagGcJG6Uzbods79EPihmf3UOfeZESzTuFbuppwp0/JccW1Pn2lPOraBtau37jHU1yVMERGpnb5ayZua4VOfSXPMcWlu/FOYq38a5dSTGthmbo7jP5MmEnH88LuDu/9HREZev519FXCPrNPOSnL6KfEtbq6JxR1nnpvk9Xv0PeTfmeeWvlR52lnJqpVXRESqKxKFIz+a4QNHZbjj1hBXXR7l3DPieBedNdygSL3QmBajzBvnZXHOaJkwsJtyYPA39IiIyOgXCMC7D87yh5u6mTwlz5a9PjcPNygio5OGtRhl7rrNG6XkTzd3k+v+z4BuyoHB39AjIiL1wQzWryt9r86K5cZTTwTY9XUablBktFFL9yhz561hXrNTjm230wlTRERKK3evjhm878BmPv6hRu65K4TTLT0io4aC7lGkfYPx4ANB9j9Q3UFERKS8085KEotvGVHH4o5vXZzgjK8lePGFAJ/+WCOH7tfEDb8Pk07VqKAlLLwhzPw9mzn4wP2Yv2dzRfNQiIwFCrpHkXvuCpHLGe86sPwsZiIiIuXu4fnQ0Rk+9Zk0d97fySU/6iEYhDO/2MB+b2nmyh9F2dhe23IPZQI4kXqnoHsUufPWMNPa8ryun1FKREREDjsiw6IHO/n7rXex6MHOLW6aj0Tg8A9kuPH2Ln6xoIudX5vjexfGeOe8Fi44N8a1PxvZ1mbnYOnLxvlfKz0BnG4AlfFAN1KOEukU3Hd3iEPfnyagn0IiIjIMzOBt++R42z49PPNUgGuujPKrn0f8vt6DH25w4Q1hf1Kf0mOEp9Pw1BNBHnkoyCMPhvjvQ0HWlJhHoteK5cZ3vhljvwMzvPHNOUKKTmQMUrUeJe7/Z4jubuNdB6lriYiIDL9dds1z8Q8S3H9fiFWvbhkAJxPGWV+M8/e/hpkxM8+MmXmmz3BMn+X93TbdEfYbw3u7iPS2WK9Ybpzz5ThPPh4gFIZHHgzxxGNB0inv9dnb5HnrO7K8aV6OH38/WjL4jkbhVz+P8PMrorROzPPO/bPs/+4Mb5+fpalp83r9Bfsio5mC7lHizltDNDY63vo2Bd0iIlI9q1eVHm4wk4FXXg7wn/tDdHZsuY6ZY+o0x/SZeZ57OkgyWdRFJGn84qoY4bBj19flOPrYNG+al+WN83JMa9t8w2dTsys5idv5lyTY74AM/7gnxJ23hbnnzhA3/jFCOOLY+61Z9nt3llzO8b0Li4J9TQgkdURB9yiQz3v9ud+xb5ZItNalERGRsWzGTMeK5VsH3jNnOW66qwuAri54dUWAV1cEWLnCWFnwd7LcJMfmePiZDmLx8nn3Bsdea7Vt1Vp90KFZDjo0SzYL/304yF23hrnrthDfPLv0Rnv7gyvolnqgoHsUeOJRr6/b/gdqunYREamu085KlmxtPu2szd9BTU3wmp3yvGanreeMmL9nc+mgfabrM+DuVckkbqEQ7Ll3jj33znHGubDk+QAH7dNE8Syc4LV4L/h1hN3fkGWnXfJEIv2XYaSpW4yAgu5R4Y5bQwSDjnfup64lIiJSXf21NvenkqB9uG3/mjwzZ5VuobcAnHuGF+2HI45dds3xujf4jz1y7LBjnmCwdoFvyT7w6hYzLinoHgXuvDXMnm/J0TpRU4eJiEj1VdLa3FdaGHzQPljlgv3zL07wxj2zPPFoiMWPBXnisSA3/inCb3/prRePO9pm5HllaYBcduQD30suKD9M4mgOuof6I0Wt+1tT0F1jL78Y4Pnngnz4Y4laF0VERKQiQwnah5InlA/252yT4ZDDvL/zeXjxhQCLHw/yxKNBfvfryKaAu1cyYVx8fnUC32zWGwb4+t9GWLWy9I2rK1eUXj4aDLV1Xq37pSnorrE7bvXeAk39LiIi0rdKg/1AAHbYMc8OO+Y5/AMZfn1N6Y7eq18N8IFDGnnP4RkOeW+G6TOHdsV52SvGH38X4U+/j7BqZYApU/M0NTm6urYOsJ2DM06N87lTU2wzd+u+87V06YWlW+cvPC/GpEn9H6MLz6vP1v1qU9BdY3feGmbnXXPMnqOuJSIiItVQbsSWlgl58jnjom/Euegbcd68V5ZDDstw0KEZpk6r7Hs5nfaG/b3+ugj/us8Lq96xb5avnZ9g33dl+ftfw1t3i4k59vq/LDcvDLPwT2GO+HCGz56aZNbs2scCPT2UPFYA69YGOO6jjYPe9mhu3R8JCrpraP0645EHg3zm86laF0VERGTMKtcf/NwLvO4pLy0JcPPCMH+7Mcy3zolzwbkx9vq/HO85PM27D84ycZLbqo/y0Z9MsW5NgD//IcyG9QFmzMxz8mkpPvDhNDMLgue+usWsXmVc+aMoC34T4S9/CHPkR9OcdEpqyC3uA9XdDYvuCHPLTd4Y6aVGiQGYMjXP5Vf39Lu9k49vYO2arSdBmjHC+zXaKOiuoUV3hMjnjf0PGr+XWkRERKqtv/7gc7fP89lTU3z21BTPPbM5AP/a6Q184yzHDjvlWPK/IJnM5j7Kl5wfx8xxwMFZPnR0grftkyUYLJ9/qW4x09ocXzs/yac/m+KnP4zyh99G+OOCCEd9LM0JJ6e2mFhouHV1waLbw/z9pjD33h0ilTSmTsvzgaPStExw/OKq6FY/Us78epI37Znrd9tnfn3rHzkAjU15Vr1qtE0fn8G3gu4auuPWMNNn5NntdaOrL5eIiMhYU2l/8J12ybPTLim+cHqKp54I8LcbI/ziqgi53Natv9Omu4pafvszfabjGxcl+fTnUvzkshjXXRvh+usifPQTabaZm+Oqywc3Ckhx6/xnv5gkHoO/3xTmvkVeoD2tLc+RH0lz8HszvGnP3KYfDjvsmB/0CDWlfuS89e1Z/nZjmPfu38QF301wwMHjb5hkBd01kkzAP+8J8f4PpbHx3cVJRERk1DGD3V6fZ7fXJ/n5FeVuxBzeL/DZcxzf/l6CE09O8ePLolxzZW++m1vYz/5SnBXLjH32y4Lzbsh0gHPm/e0AB/feHeLKy6OkUwUjiHw5DhjTpuf50EfTHPTeDG/eM0dg654gQx6hplT64z+b4ssnN/C5TzXy4Y+lOOu8JA0NAz9O9aqqQbeZHQT8AAgCVzvnLip6fT5wI/Civ+gG59w3zWxn4PcFq24PnOucu8zMzgM+DazxX/uqc+7mqu1ElfzrvhCJhPGuA8ffLz0REZF6Uu5GzGr1Ud52uzwX/yDBv+4NsXrVlhFxKmVcelGcSy8qk7hPxpSpee59qLNkoF1tO+yY5/d/7eIHl0S5+idR/vOvEN/7SQ+7v358XPGvWtBtZkHgx8ABwDLgQTNb6Jx7qmjV+5xzhxYucM49C+xRsJ3lwJ8LVvm+c+671Sr7SLjj1jBNzd7dyyIiIjJ61WIWToA1q8u1pDsu/3kPhtciX/jA//+EYxoodUPkurVWk4C7VyQCp5+dYp99s5x+SgMfOrSJL5ye4vjPpsr2iR8rqtnSvRfwvHNuCYCZLQAOB4qD7v7sD7zgnHt5mMtXM7kc3H17iH32zRApfcVKRERERolazcJZroV95izHu/vpEz1z1si2zg/U3v+X4693dnLuGXG+d2GM+xaFuOSHPcyYNTrKVw3mXHV2zsw+CBzknDvef34MsLdz7uSCdeYDf8JrCV8BfNk592TRdq4BHnHOXe4/Pw84FugAHgK+5JzbUCL/E4ATANra2t68YMGC4d3Bfjigs6OTaHzrzkpPPTmB0744jzPOWsy++64qu41UsodobHCdncZb2lrmrX2uj7S1zFv7XB9pa5l3PaatZd7jZZ/vurONH1z2WlKpzU3A0WiOL5z6NPvtXz5+GGraoZR5oOmdgztun85PfrwzwaDjlM8/Qy5nXPuLHVizJsbUqUmO/eQLAypz3jmyqQTNzc2DLvdQ7Lvvvg8757bqCF/NoPtI4MCioHsv59wpBeu0AHnnXJeZHQL8wDm3Y8HrEbxgfDfn3Cp/WRuwFi+u/RYwwzl3XF9lmTdvnnvooYeGdwf7kcnluf2Ou9jx9Xtu9dol58f4xVURHniig5YJ5bcxlOl1x1vaWuatfa6PtLXMW/tcH2lrmXc9pq1l3uNpnzePQDLwFvahpB1KmQeTfulLAb58SpxHHw4RDLotRouJxR3nX5KouOxdyQxrlzzOAfvvN+hyD4WZlQy6q9m9ZBkwp+D5bLwAehPnXEfB3zeb2U/MbIpzbq2/+GC8Vu5VBett+tvMfgbcVI3CV9Mdt4bY663ZPgNuERERkaGMIjLUEUhG0jZz8/z2z93svVsznZ1bdjofK1PIV7Mr/YPAjma2nd9ifRSwsHAFM5tu5g2YZ2Z7+eVZV7DKR4DfFaWZUfD0/cDiKpS9apY8H+DFF4Lsr1FLRERERDYJhaCrq/TNo2NhCvmqtXQ757JmdjJwK96Qgdc45540s5P8168APgh8xsyyQAI4yvn9XcysAW/kkxOLNn2xme2B173kpRKvj2p33uod8v3fXd+/1kRERESG20gPzziSqjpOtz9+9s1Fy64o+Pty4PIyaXuAySWWHzPMxRxRd94aZtfdc8ycXf+VR0RERGQ41Wp4xpFQw5Eax5+1a4z/Phxk/wPVyi0iIiJS7LAjMpx/SYKZs/J4nRrg5NOqPzzjSFDQPYLuvj2Ec6agW0RERKSMw47IsOjBTq7/473EYo6XXhgbs+Yo6B5Bd9wSZtbsPK/dbXxMdyoiIiIyWC0tWT5wVJobbwj3MTtn/VDQPUJ6euBf/wix/4EZb5pWEREREenTsZ9Ok83Ab35R/1N4K+geIf+4J0Qqqa4lIiIiIpXadrs87zooy+9+FaGnp9alGRoF3SPkzlvDtExwzNs7V+uiiIiIiNSN405M0b4hwA2/r+/WbgXdIyCb9W6inL9/hnC41qURERERqR9v2jPHHm/Ocu3PIuTquO1SQfcI+O9DQdo3BNS1RERERGSAzLzW7qUvBbnztqpOMVNVCrpHwB23hglHHO/YV1O/i4iIiAzUAQdnmb1NnmuuiNa6KIOmoLvKnPOmfn/L27I0NdW6NCIiIiL1JxiEY49P8ciDIR59uD7H7VbQXWXPPxdg6UtB3nWgWrlFREREBusDH0nTMsFxzZX12dqtoLvK7rjVu3Nyv3erP7eIiIjIYDU2wlHHpLjt5hBLX66/SU8UdFfZnbeGeN0eWdqmu1oXRURERKSuHXNcmmAQfvmz+mvtVtBdRevWRXj8vyF1LREREREZBm3THYe+P8OfFkTY2F7r0gyMgu4q+s8DUwE0VKCIiIjIMPnkCSl6eowFv66v1m4F3VVw3XXwmu2Ny3+0C8Gg45kn6/MuWxEREZHRZpdd87xtnwy/viZCOl3r0lROQfcwu+46OOEEWLrUACOXM875SpyFN2gqShEREZHhcNxJaVavCnDTX+onvlLQPczOPht6erZclkwYl14Yq02BRERERMaYt78zy0675PjFlVFcnYxVoaB7mC1dWnr5yhX1N7SNiIiIyGjUOzX8s08H+de99TE1vILuYbbNNqWXz5hZJz/DREREROrAoe/LMHVanp9fEal1USqioHuYXXABNDRsuSwWd5x2VrI2BRIREREZgyJR+Nhxaf5xT5hnnx79Ie3oL2GdOfpouOoq2GYbh5lj5qw851+S4LAjNGygiIiIyHD6yDFp4nHHL+pgangF3VVw9NHw/BLHTX+/k0UPdirgFhEREamC1omODxyV5q9/DrPq1dF9/5yCbhERERGpW8d+Ok02C7/5xeju262gW0RERETq1jZz8xxwcJYFv45sNWzzaKKgW0RERETq2qdOSrGxPcCfFoze1m4F3SIiIiJS1944L8cb35zl2p9FyOVqXZrSFHSLiIiISN077qQUr7wcZNFto7O1W0G3iIiIiNS9dx2UZdLkPF89tZkDD9iXuXPhuutqXarN6mPeTBERERGRPvztxjCdHUYm4w0d+PLLcMIJ3mtHH13DgvnU0i0iIiIide/SC2ObAu5ePT1w9tk1KlARBd0iIiIiUvdWrig9Oc7SpSNckDIUdIuIiIhI3Zsx05Vcvs02I1yQMhR0i4iIiEjdO+2sJLH4loF3QwNccEGNClSkqkG3mR1kZs+a2fNmdmaJ1+eb2UYze9R/nFvw2ktm9oS//KGC5ZPM7HYz+5///8Rq7oOIiIiIjH6HHZHh/EsSzJiZw8yx7bZw1VWj4yZKqOLoJWYWBH4MHAAsAx40s4XOuaeKVr3POXdomc3s65xbW7TsTOBO59xFfiB/JnDGcJZdREREROrPYUdk2O+QHtYueZwD9t+v1sXZQjVbuvcCnnfOLXHOpYEFwOHDsN3DgV/6f/8SeN8wbFNEREREpGrMudKdzoe8YbMPAgc55473nx8D7O2cO7lgnfnAn/BawlcAX3bOPem/9iKwAXDAlc65q/zl7c651oJtbHDObdXFxMxOAE4AaGtre/OCBQuqsJflOaCzo5NovGHQ20gle4jGBpd+vKWtZd7a5/pIW8u8tc/1kbaWeddj2lrmrX2uj7S1yjvvHNlUgubm5kHlO1T77rvvw865ecXLqzk5TqlxW4oj/EeAbZ1zXWZ2CPAXYEf/tbc551aY2TTgdjN7xjl3b6WZ+0H6VQDz5s1z8+fPH2j5hySTy3P7HXex/e5bHfOKLVn80KDTj7e0tcxb+1wfaWuZt/a5PtLWMu96TFvLvLXP9ZG2Vnl3JTOsXfI4Ix379aea3UuWAXMKns/Ga83exDnX4Zzr8v++GQib2RT/+Qr//9XAn/G6qwCsMrMZAP7/q6u4DyIiIiIiQ1bNoPtBYEcz287MIsBRwMLCFcxsupmZ//defnnWmVmjmTX7yxuBdwOL/WQLgU/4f38CuLGK+yAiIiIiMmRV617inMua2cnArUAQuMY596SZneS/fgXwQeAzZpYFEsBRzjlnZm3An/14PAT81jl3i7/pi4DrzexTwFLgyGrtg4iIiIjIcKhmn+7eLiM3Fy27ouDvy4HLS6RbAryhzDbXAfsPb0lFpFLt3Sly+ercgC0iIjJWaUZKEalYVzJDUzxMMGB0JtK1Lo6IiEjdUNAtIhVJZ3M459h+WguRcJBAwEhlcrUuloiISF1Q0C0i/crnHd3JLDu0tRANBzFgxxkT6ElnyebytS6eiIjIqKegW0T6tbEnzewpjUxojG5a1hgNs31bCx09afJVmmRLRERkrFDQLSJ96kxkaG2MML1161nBpjTHmDmpkY3d6t8tIiLSl6qOXiIi9S2dzWHmmDutmYCVmmQWZk1upCeVpTORoTkeHuESCoBzjrxz5PLeI5/3nju39TTAIiJSGwq6RaSkfN7Rlcyw6+xJRELBsusFzNiurZmnlm0glckRDZdftx7l846NPd4wiYl0lnhk5E6bzg+ce1JZP6De3H/e/H8dDjMIBQNEgkEaokEioQCRUADnjFUvODZ2p2iKRwgGSv9wEhGR6lPQLaOCK2ilU2AwOmzsSbHt1KaKWq8joSA7zpjAk69sIBQ0goGx0XMt5wfc205tpmuZF8xu6EoSDYeIR4JYmdb/oeebpzORwQFm0NoYIRIKEA4GCAUDBAMBggHzj7URMCtblmcjIWZObmT5um4CZjTFw2WvWoiISPUo6JZRIZ3NY+YFeo3R8JhrLa03HT1pJjVFaZuwdT/ucnpvrHxh5UYmNkWrFpCOlFw+z8aeNNtNa2HahDjPmLHLrIl0JTOs3NDNhq404VCAxmho2PY1lcnRk8oQCgaYPbmRyc0x/rUsyLZTm4e03ZkTG5nSHGNVe4KV7T2EAgGaYsNXbhER6Z+CbhkVkuks4WCQ3eZM4vlXN5LqydHSEKl1scalVCZHMGhsO61lwEHZlOYYiVSWlRt6mNgU7T/BKJXN5enoSbPDjAlMaY5t8VpTLMyOM1rpSWVZ1d7D2o4kwaDRGBtcC3LeOXpSWTLZnL/tCbQ0RIf9ik8kFGTOlCamToizcn03azoSRMMhGqL6GujlNAqPiFSRzrYyKjgHwYDRFAuz25xJvLymk/WdKVoa1A91JOXyXgC425yJhIOD6yJS7zdWZnJ5OhNpdpo5gYlNsbLrNURDbNfWwoyJDazamGD1xgRmRnMsTKCCOpvN5elKZgCY2hJj6oQ4jdHqH69YOMh2bS20tTawfH0X67uSxCOhEe2rXit558jlHNl8nmzO7yPv6O0g7914qrhbRKpk7J9lZdTL5PJEI0G6/S++cDDADm0tNMcSvLSmU91NRtDGnjRzpzbTGBt88FfPN1Zmsl4gvPPMVlobK2upj0VCbDu1mRkTG1iz0eu+gYOmeLhk3/ZEOksqnSMcDrDNlCYmNkX7vFG1WhqiIXac0UpnIsOydV1s6ErSUOefNeccDu/KWTbvyObyGJtHcAkYRMMhmqIhYpEQsUiQcDBIOGiEQwG6U1nuW6KoW0SqQ0G31FwynWV6awPrC5aZGW2tDTTGwupuMkI6etJMaY4ybUL51t1KRUJBdpxeXzdWprM5elJZdpnlde8YqEgoyKzJTUxrbWBdZ4Ll63rIO7fpB0xnIkM2l6elIcy2U5tpHiU3NDbHw+wyq5WORIalaztp70rRGAsTDg3+PRuJyZJy+TzpbJ5MNu/l57dYO+f9EIpHgsTCQSKhIOGCm1D70hKPEAoYXckMTUP44SkiUoqCbqm5bN6VDajV3WRkJNNZwqEA205tHrab6xpjYbZva+aFVztG/Y2VqUyORDrLLrMmDrlLTDgYYHprI1Nb4qzrTLFifTe5vGNKc5SpE+KjshuHmTGhIcJucybR3pVi6bouupIZf/SWtN/XeXObce9fha3IhQLmdVVq705t0X0jYEYgYIQCtmkElv664uSdI5PNk8nlyeby3uYc/k2sYaa2eF1jon6Afd8ybySdwYqEgpsC+dHwo0hExo7Rd/aXcSWfdwTN+ryZq5rdTfJ5RyqbG9cTiOTyeRKZHLvNmdhvS+BATWmJ05PO8uqGxKi9sTKZzpLO5tl19sQhdaspFgwEmDYhzuTmGO0vh9hmiCOQjISAGZOaY0xojNLenaJjaYC5U5sIFAxLaOYHz2YEjC2WWcGyRa+E2GO7KeRyeTJ+/+l0Nk8y4x3vZDpH0h9/3Pzxxnv1BuwGWMBoioZobYzRGA0TCQeIhoLDXld7mcGMSQ2s2tDDhAq7GImIVEJBt9RUIp1lUnO03xal4exuks7mSKZz5POOQMBobYxsmghmvF1Sds5rydyhraVqN/HNmtRETyo3Ko9vIp0lm8vz2tkTqzaKRz1emQkGjMnNMULBAFNa4oPeTjjodevoq8NSzu973XtzYzaXZ+PSILvMmkg07KUf6ask01u9/vnZXL5qwb2IjD8KuqWmMtk8EwfQmrSpu8nqDtZ3VdbdJJfPk0jnyGa92fwaY2FmTW6kORYmHg0RMGPp0yEaIiE2dCWrMlzbaNXRk6ZtQnxIgVV/ggFj+7ZmnnrFu7FytOhJZXHO8drZE0dll4/xIhgwgoEgUTZfvQoFrKYj34SDAWZPbuLlNZ0V31ArItIffdNIzTjnwBhw62c4GGCH6RNo3li6u4lzjlQmRyqTIw9EggEmNUVpbYzSEA2VHArPDHacOYHVGxMsXdPl3YQ1xgOxvINoxBu7udoKZ6zs7UTQO8W5w+GN3OY9z7st/3fObboxL+dfkeidnXGwLaDdyQxmsMvsicTqeLQOqZ7JzTFWtffU3Qg8IjJ6je2oQka1VCZHS0N4UJdvS3U3yTm/H6hBczzC9IkNNMXCxMKVTdcdMGN6awPN8TBLXu1gY7fXkj6abwAcrGwuj3OO10yfMGIji/TeWLl2iX+DHRAKBAgGvf7Bod7/A0YgECAY8PpFB4NGKBAgYEbH0iCTm6N09GToSKRxecDYYoSK/nQlM4QCxk4zWxVMSVnBgLHN1GaeXb5B9UREhoWCbqmZVCbHjImVTzNeSm93k+XrulgfDLDLrIk0RINDCiQbo2F2nTORV9Z2sWpjguZYZEjDp40meefoTmbI5hzRcHDEu1VMaYnTEA3x5h2mDmpkiGDA2GaKd0NiLu9IZrIk0zk6E14Q3p3MbBotwxvNIrBFXcg5RzgYYKeZE2oyNrbUl5Z4mAmNUXpSWc3cKSJDprOI1IwDmuJDH3s7HAwwd1oLLwUDw9YPNBjwtjmhIcKLqztJZW3U3QQ4ELl8nq5EBgdMmxBn2oQ4/15euxb84RiKLRgwGqNhGqNhJvtTtWdzeZL+8H+dPWk6EhkyOW/Wx3zeGwJu51mtg55tU8YXM2PO5CaeWLqOWCSoIQRFZEgUdEtNZLJ5YuHgqO9PO7EpRmMszEurO+vyJsveCV8CZsya3Mjk5tiYbuENBQM0BQM0xcJM9W8OTWdzJDM50pkci1cEFXDLgDREQ0xvbWBtR1ITdInIkCjolppIpLPMnDS0riUjpfcmwN6bLGN1cJNlIu11u4hHgmzf1kJrY6QuZoWshkjImzSF6g3QImPcjIkNrOlIkMvnx+3nSESGbnRHDjJm5fOOlmHoWjJSem/cbIqHWbJqdN5k6ZyjO5Ulk/WmGp/rTzU+msooUo8ioSBzJjfx8pquUTvJk4iMfgq6ZcTl8o5QMEC8Dm9MaoyG2XX2RJat6+bV9h6aY7X/4ZDLezdH5pxjanOMaa3xqk10IzJeTWmJs7K9h3Q2N6a7aIlI9dRf1CN1r9JZKEerYCDAtlObmdAQYcmqjk1jSI+0TC6/adzqGa3eBDca2kykOoIBY9spTTy3YiOTmvU5E5GBU+c0GXHZ3MBmoRytWhuj7L7NJMyMjp70iObdk8qSTGeJhILsMXcysyY3KeAWqbLWxigtjRES6WytiyIidUhBt4wo5xxm0BgbGxdZIqGgN951NDhigXdXMkPAYNc5kwgFbVCTC4nIwJkZ20xuIpnOeTPqiogMgL6tZUQlMzla4mNrJA0DdprRSnM8zEZ/psVq6ehJEwkF2HlW66gfblFkLGqMhZk2IUZXMlProohInRk7kY/UhVQmu2kik7EkFAyww3RvaL72KgXeG7tTNMXC7DyzVTdyidTQjImN5PKOXF6t3SJSOQXdMrJcfc/s2JdgIMB2bROY1BRlQ1dq2C4/O+fY0JWitSnKa2a0qDuJSI1Fw0FmT26kMzGy93KISH3Tt7eMmHQ2RzwaGtM3/AUDxnZtLUybEKO9e+iBd9452rtTTJsQY/u2ljHVLUeknk2bECccDJDJ5mtdFBGpE/oGlxGTSOeY0jL2upYUC5ix7dRmpk9sYEN3etBDCubzjvauFDMnNbLt1Oa6HWJRZCwKBgJsM6VJfbtFpGIKumXEOFdfs1AOhZkxZ3ITsyc10N6dIj/Avp+5fJ72nhTbTG1i9uQmzSopMgpNbIrSFAtpCEERqUhVg24zO8jMnjWz583szBKvzzezjWb2qP84118+x8zuNrOnzexJM/tCQZrzzGx5QZpDqrkPMjxy+TzhYIB4ZOx2LSlmZsya3MQ2U5po705VfNNVNpdnY3ea7dtamDGxscqlFJHBMjO2mdpMMp3VEIIi0q+qDZZsZkHgx8ABwDLgQTNb6Jx7qmjV+5xzhxYtywJfcs49YmbNwMNmdntB2u87575brbLL8Eukc0xuio7LFtsZExsJmPHS6k4mNPY9XGI6m6M7mWWnmROY2DT2u+KI1LumWJjJLfERnyBLROpPNVu69wKed84tcc6lgQXA4ZUkdM6tdM494v/dCTwNzKpaSaXqstk8rU31PwvlYLW1NrDD9BY2dqfJ5krfeJXK5OhJZdlllgJukXoya1IjmTKfaxGRXtUMumcBrxQ8X0bpwPmtZvaYmf3dzHYrftHM5gJvBP5dsPhkM3vczK4xs4nDWWgZfnnnsIDRGB0bs1AO1pSWODvOmEBHIr3VF3QinSWdzbHr7Im0NIzfHyci9SgWDjJ7UqPG7RaRPlm1+qGZ2ZHAgc654/3nxwB7OedOKVinBcg757r8vtk/cM7tWPB6E3APcIFz7gZ/WRuwFnDAt4AZzrnjSuR/AnACQFtb25sXLFhQlf0sxwGdHZ1E4w2D3kYq2UM0Nrj0oylt3kEgANF+JnTp6uqiqalpUPkONf1Ips3lHalMjkDASCd7CEfjAMTCIQba+6Ze9nk0pK1l3trn+kg71PQdHZ2EYw0EBtGLbjSds+shb+1zfaStVd5558imEjQ3Nw8q36Had999H3bOzSteXs2mx2XAnILns4EVhSs45zoK/r7ZzH5iZlOcc2vNLAz8CbiuN+D211vV+7eZ/Qy4qVTmzrmrgKsA5s2b5+bPnz/0PRqATC7P7Xfcxfa7b3XMK7Zk8UODTj+a0m7oSvGa6S1M6mcmykWLFjGU92ko6Uc6bUcizbPL21nzwuPM3uWN7DSjdVDjl9fTPtc6bS3z1j7XR9qhpr/r7kU0zdmVlniE4AAj79F0zq6HvLXP9ZG2Vnl3JTOsXfL4kM4F1VDN7iUPAjua2XZmFgGOAhYWrmBm082/s87M9vLLs85f9nPgaefcpUVpZhQ8fT+wuIr7IMPBoCk+NmehHKyWeITXzp5IMGjsPHNwAbeIjC4Bg22nNNHRk6p1UURkFKpaS7dzLmtmJwO3AkHgGufck2Z2kv/6FcAHgc+YWRZIAEc555yZvR04BnjCzB71N/lV59zNwMVmtgdeD46XgBOrtQ8ydOlsjqZYmEg/XUvGo6ZYmGgoqGMjMoZMnRCnvTtNVzJDU0yNDSKyWVXvbPOD5JuLll1R8PflwOUl0v0DKHltzjl3zDAXU6qoJ5Vlm6mD718pIlJPAmZsO62ZxUvXkc3lCQU1B52IeHQ2kOpyjJtZKEVEwBvNZO60FjoTGrtbRDZT0C1Vk83lCYcDxNRfWUTGmclNUSY1xzRpjohsoqBbqiaRzjGlOTYuZ6EUkfHNzNhmShOBgHdvi4iIgm6pmlwuzwRN9CIi41QkFGS7aS10JTJUa04MEakfCrqlKvJ5RyBgNIzzWShFZHxrbYwyfWKDupmIiIJuqY5kJsfEpuiAJ4gQERlrZk9uJBIOksqom4nIeKagW6oinc0xqUldS0REgoEA27e10J3KkM+rm4nIeKWgW4adcw4cNGpiCBERwJsMa87kJjaqm4nIuKWgW4ZdOpunOR4mrEkhREQ2aWttoCkWoieVrXVRRKQGFBXJsEums0xuidW6GCIio0owYGzX1kI6myOnbiYi446Cbhl2zkGzupaIiGwlHgmxzZRmOnpStS6KiIwwBd0yrBwQjQSJRTRUoIhIKdMmxGhtjNKVzNS6KCIyghR0y7DKO8eUZnUtEREpx8zYdmoz+bwjk8vXujgiMkIUdMvwctDSEKl1KURERrVoOMh205rpTKQ1W6XIOKE+ALKFvHPknXcz5ED1fm1oFkoRkf5Nao4xtSfNhi717xYZDxQdyRY2dqcJGMQHGThHQgECplkoRUQqMWdKEx09adTWLTL2KeiWTTK5PJFQgGg4yGumTxjUNpY9ox5LIiKVCgcD7DB9Aiuec6QyOaLhYK2LJCJVoghJNulOZpg5qbHWxRARGVea42FikSCpTI7EILr2iUh9UNAtAOTyeQJmTG6O1rooIiLjTsCMXedMxDmnoQRFxigF3QJAVyLDrMkNBAOqEiIitRCPhHjt7IlEQgE6etK1Lo6IDDNFWEI+78BgcnO81kURERnXIqEgO89spTkepr07qeEERcYQBd1CZzLDjNYGwkFVBxGRWgv5N1dOaY7T3p0ir8BbZExQlDXO5Z3D5R1TJ6iVW0RktAgGjLnTmpk5qZH27hS5vAJvkXqnoHuc605mmNYaJxLSMFUiIqOJmTF7chNzpzazsSdFVlPGi9Q1Bd3jmHOOXM7RplZuEZFRq621gZ1mTKAzkSGdzdW6OCIySAq6x7HuVJbJLTFiEc2RJCIymk1sivHa2RNJpLMkNZa3SF1S0D2OZbJ5prc21LoYIiJSgeZ4mF1nTyKXd3RrLG+RuqOge5zqSWWZ2BShIapWbhGRetEQ9cbyDgU1lrdIvVHQPU4lM1lmTNSU7yIi9SYaDrLzrFYaY2E2dqdqXRwRqZCC7nEokc7SEo/QFAvXuigiIjII4WCAHWdMYGJTlFzeaRIdkTqgoHscSqazzJqkVm4RkXoWDBjbtbUQCgbY2KMWb5HRTkH3OJPK5GiIhmmOq5VbRKTeBcyIhAK0NkTV1URklFPQPc4kUllmTWrAzGpdFBERGSbbtbXQEAvRmdDNlSKjlYLucSSdzRGNBJnQGK11UUREZBiFggF2nNFKOBTQcIIio1RVg24zO8jMnjWz583szBKvzzezjWb2qP84t7+0ZjbJzG43s//5/0+s5j6MJd1Jry93QK3cIiJjTjgYYKcZrWDeDfMiMrpULeg2syDwY+BgYFfgI2a2a4lV73PO7eE/vllB2jOBO51zOwJ3+s+lH5lc3uv3p1ZuEZExKxoOsvPMVrK5PKmMpowXGU2q2dK9F/C8c26Jcy4NLAAOH4a0hwO/9P/+JfC+4Svy2NWdzDBzUiPBgFq5RUTGsngkxM4zW0lmcmSy+VoXZ1hkc3l6Ulk2dqfY0J0il3eks/pRIfWlmkH3LOCVgufL/GXF3mpmj5nZ381stwrStjnnVgL4/08b3mKPPbl8noAZk5vVyi0iMh40xsLsOL2FrmSGbK6+Au9cPk8inaW9O8XG7hTtXSkyuTwtDWHmTmtmt9kTiYaDZHJ52v0AXKQeWLUG1DezI4EDnXPH+8+PAfZyzp1SsE4LkHfOdZnZIcAPnHM79pXWzNqdc60F29jgnNuqX7eZnQCcANDW1vbmBQsWVGU/y3FAZ0cn0XjDoLeRSvYQjQ0ufWHaXN4RDgUIByv7jdXV1UVTU9Og8q1V2lrmrX2uj7S1zFv7XB9pa5l3tdLm8o5kJkcwYJS6zjmU75mhpu9Nm3dsMbmPmTcGecACBAJgtnXZe/c5m8uT9lvzK72SOxr2ebykrVXeeefIphI0NzcPKt+h2nfffR92zs0rXh6qYp7LgDkFz2cDKwpXcM51FPx9s5n9xMym9JN2lZnNcM6tNLMZwOpSmTvnrgKuApg3b56bP3/+EHdnYDK5PLffcRfb777VMa/YksUPDTp9b9p83tGZTPOGuVMqDroXLVrEYI9XrdLWMm/tc32krWXe2uf6SFvLvKuZdlV7gpdWd9DaGCVQFJgO5XumXPp83pFz3iyZubwjn3fknRcI9QbXBqx+4XGm7vB6mmMRWhrCxKMhYuEgkVCw33wL9zmdzbFifTerNyaJRYLEI32HNtXY5/5ksnl6UhnWLHliyN/r9ZS2Vnl3JTOsXfL4kM4F1VDNoPtBYEcz2w5YDhwFfLRwBTObDqxyzjkz2wuvu8s6oL2PtAuBTwAX+f/fWMV9qHtdyQwzWhsqDrhFRGRsaWuNk3N5XlnbzcTGyLDM05DK5EikM+Tyzp+Ux3A4r5XaAoSC3qQ9sXCQcDBAOBQgFAgQCga8VuyA0flKiDduN2XI5YmEgsyd1sLUljhL13axvitFcyxMOFT7771EOksynSMaDjJnahNrlsDG7hQtDcPzPkh9qVr3EgC/y8hlQBC4xjl3gZmdBOCcu8LMTgY+A2SBBHCac+5f5dL6yycD1wPbAEuBI51z6/spxxrg5WHfwf5NAdbWKP14S1vLvLXP9ZG2lnlrn+sjbS3zrse0tcxb+1wfaWuZ91DLPRTbOuembrXU+Zd79Bj+B/BQrdKPt7T1Wm7ts46X9nn0pK3Xcut4aZ9Ha9p6Lnc1HrW/9iIiIiIiMsYp6BYRERERqTIF3dV1VQ3Tj7e0tcxb+1wfaWuZt/a5PtLWMu96TFvLvLXP9ZG2lnkPtdzDrqo3UoqIiIiIiFq6RURERESqTkF3FZjZNWa22swWDyLtHDO728yeNrMnzewLA0gbM7P/mNljftpvDCL/oJn918xuGkTal8zsCTN71MweGmDaVjP7o5k94+/7WytMt7OfX++jw8xOHUC+X/SP1WIz+52ZxQZY7i/4aZ/sL99S9cLMJpnZ7Wb2P///rWZX7SPtkX6+eTPrc/aAMukv8Y/342b2ZzNrHUDab/npHjWz28xsZqVpC177spk5f0KsSvM9z8yWF7zfhwwkXzM7xcye9Y/bxaXS9pH37wvyfcnMHh1A2j3M7IHez4Y/L0Glad9gZvf7n62/mjeTb6m0Jc8dldSxPtL2W8f6SFtp/SqXvt86Vi5twetl61gf+fZbx/rKt7861ke+ldavcun7rWN9pO23jlmZ75cK61e5tBWdw/pI328d6yNtJfWrz+/UfupXuXwrqV9l8+2vfvWTd791rI+0ldSvcmkrOof5624Rg1RSv/pIW/F35Iip9fApY/EB7AO8CVg8iLQzgDf5fzcDzwG7VpjWgCb/7zDwb+AtA8z/NOC3wE2DKPtLwJRBHrNfAsf7f0eA1kFsIwi8ijc+ZiXrzwJeBOL+8+uBYweQ3+7AYqABb6KpO4AdB1IvgIuBM/2/zwS+M4C0rwV2BhYB8wZaJ4F3AyH/7+8MMO+Wgr8/D1xRaVp/+RzgVrzx80vWmTL5ngd8uYL3plTaff33KOo/nzaQ9EWvfw84dwB53wYc7P99CLBoAGkfBN7p/30c8K0yaUueOyqpY32k7beO9ZG20vpVLn2/daxc2krqWB/59lvH+kjbbx3rq8wV1q9yefdbx/pI228do8z3S4X1q1zais5hfaTvt471kbaS+lX2O7WC+lUu30rqV7m0FZ3D+ip3f3Wsj7wrqV/l0lZ0DvNf3yIGqaR+9ZG24u/IkXqopbsKnHP3An1O2NNH2pXOuUf8vzuBp/GCw0rSOudcl/807D8q7rRvZrOB9wBXD6jQQ+T/6t0H+DmAcy7tnGsfxKb2B15wzg1kIqQQEDezEF7wvGIAaV8LPOCc63HOZYF7gPeXW7lMvTgc7wcH/v/vqzStc+5p59yzlRS0TPrb/HIDPADMHkDajoKnjZSpZ318Fr4PfKVcun7S9qtM2s8AFznnUv46qweTt5kZ8CHgdwNI64De1p0JlKlnZdLuDNzr/3078IEyacudO/qtY+XSVlLH+khbaf0ql77fOtbP+bLPOjbEc225tP3Wsf7yraB+lUvfbx3rI22/dayP75dK6lfJtJWew/pI328d6yNtJfWrr+/U/urXoL+P+0hb0Tmsv7z7qmN9pK2kfpVLW9E5rEwMUtF3ZKm0A/mOHCkKukcxM5sLvBHv12KlaYL+JaPVwO3OuYrT4s0A+hUgP4A0hRxwm5k9bGYnDCDd9sAa4Bf+paGrzaxxEPkfRZkvqlKcc8uB7+LNbLoS2Oicu20A+S0G9jGzyWbWgPfrf84A0gO0OedW+uVZCUwbYPrhchzw94EkMLMLzOwV4Gjg3AGkOwxY7px7bGBF3ORk/7LwNX1daixhJ+AdZvZvM7vHzPYcZP7vAFY55/43gDSnApf4x+u7wFkDSLsYOMz/+0gqqGNF544B1bHBnHcqSFtR/SpOP5A6Vph2oHWsRLkrrmNFaQdUx8ocr4rrV1H6UxlAHStKW1EdK/P9UlH9GuJ3UyXpy9axcmkrqV+l0lZav/ooc7/1q0zaiutXP8erzzpWJu2pVFC/yqSt9Bx2GVvHIJWev0qlHXUUdI9SZtYE/Ak4tegXeZ+ccznn3B54v/j3MrPdK8zvUGC1c+7hwZTX9zbn3JuAg4HPmdk+FaYL4V1S/6lz7o1AN95lpIqZWQTvQ/2HAaSZiPcrejtgJtBoZh+rNL1z7mm8S5q3A7cAjwHZPhONQmZ2Nl65rxtIOufc2c65OX66kyvMqwE4mwEE6UV+CuwA7IH3Q+l7A0gbAibiXe48Hbjeb/EZqI8wgB93vs8AX/SP1xfxr+pU6Di8z9PDeF0C0n2tPNhzR7XSVlq/SqWvtI4VpvXzqriOlci34jpWIm3FdayPY11R/SqRvuI6ViJtRXVssN8vQ03bX/r+6li5tJXUrxJpX0+F9atMvhXVrzJpK65f/RzvPutYmbQV1a8yafutX0OJQYYpfhkZbhT0cRmLD2Aug+jT7acN4/UVO22IZfg6FfR/9de9EFiG1y/7VaAH+M0Q8j5vAHlPB14qeP4O4G8DzO9w4LYBpjkS+HnB848DPxnCPn8b+OxA6gXwLDDD/3sG8OxA6xQV9lcrlR74BHA/0DDQtAWvbdtXXS9MC7wOrwXkJf+RxbvSMH0Q+fb5GStxrG8B5hc8fwGYOsDjFQJWAbMH+D5vhE1DtBrQMchjvRPwnz7SbnXuqLSOlUpbaR0rl3YA9avPc15fdaw47UDqWAX59vVelDrWFdWxPo5XpfWrVN4V1bEK9rnPOlaw3teBL1dav0qlrbR+9ZW+0jpWLu/+6leJtF+rtH5VkG/Z+lXmWA/oHFbmeFVUx0rkXfE5rJ99Llm/KBODVFK/yqUdbP2q5kMt3aOM/6v158DTzrlLB5h2qvl3b5tZHHgX8EwlaZ1zZznnZjvn5uJ107jLOVdxq6+ZNZpZc+/feDe4VDR6i3PuVeAVM9vZX7Q/8FSlefsG0/q4FHiLmTX4x31/vD6OFTOzaf7/2wBHDKIMC/G+NPD/v3GA6QfNzA4CzgAOc871DDDtjgVPD6PyevaEc26ac26uX9eW4d3Y9WqF+c4oePp+Kqxjvr8A+/nb2Qnvht21A0gP/mfKObdsgOlWAO/0/94PqLhrSkEdCwDnAFeUWa/cuaPfOjbE807JtJXWrz7S91vHSqWttI71kW+/dayP4/UX+qlj/RzrfutXH+n7rWN97HO/dayP75dK6tegv5v6Sl9JHesjbSX1q1Ta/1ZYv8rlW0n9Kne8/kIF57B+jnefdayPtJXUr3L73G/96iMG6bd+DTV+GVG1jvrH4gMv8FoJZPA+kJ8aQNq34/WNfhx41H8cUmHa1wP/9dMupszd7xVsZz4DHL0Er1/2Y/7jSeDsAabfA3jIL/tfgIkDSNsArAMmDGJfv4F3QlkM/Br/rvABpL8P7wfCY8D+A60XwGTgTrwT2J3ApAGkfb//dwqv5eLWAeb9PPBKQT0rNwJJqbR/8o/Z48Bf8W58G/BngT5GvCmT76+BJ/x8F+K3gFSYNoLXcrIYeATYbyDHy19+LXDSIN7ntwMP+/Xk38CbB5D2C3ijTDwHXITf2lQibclzRyV1rI+0/daxPtJWWr/Kpe+3jpVLW0kd6yPffutYH2n7rWN9lZnK6le5vPutY32k7beOUeb7hcrqV7m0FZ3D+kjfbx3rI20l9avf71TK169y+VZSv8qlregc1le5+6tjfeRdSf0ql7aic1jBduazeQSSir4jy6St+DtypB6akVJEREREpMrUvUREREREpMoUdIuIiIiIVJmCbhERERGRKlPQLSIiIiJSZQq6RURERESqTEG3iEgVmJkzs+8VPP+ymZ03TNu+1sw+OBzb6iefI83saTO7u2j5XDNLmNmjBY/IILZ/rJnNHL4Si4iMXgq6RUSqIwUcYWZTal2QQmYWHMDqn8KbZXXfEq+94Jzbo+DR5/T0ZRwLDCjoNrPQIPIREak5Bd0iItWRBa4Cvlj8QnFLtZl1+f/PN7N7zOx6M3vOzC4ys6PN7D9m9oSZ7VCwmXeZ2X3+eof66YNmdomZPWhmj5vZiQXbvdvMfos3MUdxeT7ib3+xmX3HX3Yu3oQYV5jZJZXssJm928zuN7NHzOwPZtbUuy2/TIvN7CrzfBCYB1znt5THzeyl3h8pZjbPzBb5f5/np7sN+JU/892f/G0+aGZv89d7Z0HL+3/NnyVXRGQ0UNAtIlI9PwaONrMJA0jzBrwZ3F4HHAPs5JzbC7gaOKVgvbl40zK/By8wjuG1TG90zu0J7Al82sy289ffC2+m2F0LM/O7d3wHb2rnPYA9zex9zrlv4s0Se7Rz7vQS5dyhIMD9sR8snwO8yzn3Jj/taf66lzvn9nTO7Q7EgUOdc38s2P4ezrlEP8flzcDhzrmPAj8Avu/v5wf8YwPwZeBzzrk9gHcA/W1TRGTE6DKdiEiVOOc6zOxXwOepPAB80Dm3EsDMXgBu85c/ARR287jeOZcH/mdmS4BdgHcDry9oRZ8A7Aikgf84514skd+ewCLn3Bo/z+uAfYC/9FPOF/zgFj/docCuwD/NDLwpq+/3X97XzL4CNACTgCfxpt4eiIUFgfm7gF39fABa/FbtfwKX+vtwg3Nu2QDzEBGpGgXdIiLVdRnwCPCLgmVZ/CuN5kWOhTchpgr+zhc8z7PlOdsV5eMAA05xzt1a+IKZzQe6y5TPyiwfKANud859pCjvGPATYJ5z7hX/ZtJYmW1sOi4l1iksfwB4a4nW8YvM7G/AIcADZvYu59wzA98VEZHhp+4lIiJV5JxbD1yP1/Wj10t43SUADgfCg9j0kWYW8Pt5bw88C9wKfMbMwgBmtpOZNfaznX8D7zSzKf5Nlh8B7hlEeR4A3mZmr/HzbjCzndgcPK/1+3gXjrrSCRT2u36JzcflA33kdRtwcu8TM9vD/38H59wTzrnv4HVd2WUQ+yEiUhUKukVEqu97QOEoJj/DC3T/A+xN+VbovjyLFxz/HTjJOZfE69v8FPCImS0GrqSfK5p+V5azgLuBx4BHnHM3DrQwfveUY4HfmdnjeEH4Ls65drz9fQKvy8qDBcmuxeuP/qiZxYFvAD8ws/uAXB/ZfR6Y598s+hRwkr/8VP9mzcfwuvP8faD7ISJSLeZc8RVKEREREREZTmrpFhERERGpMgXdIiIiIiJVpqBbRERERKTKFHSLiIiIiFSZgm4RERERkSpT0C0iIiIiUmUKukVEREREqkxBt4iIiIhIlf0/GY+pGeSl+YYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit SFS: 10.343 seconds\n",
      "Scoring: f1\n",
      "Smallest feature subset (size = 17) with max. score (0.6244344171822142):\n",
      "('Name',\n",
      " 'Age in Days upon Outcome',\n",
      " 'Animal Type_Other',\n",
      " 'Sex upon Outcome_Intact Female',\n",
      " 'Sex upon Outcome_Neutered Male',\n",
      " 'Sex upon Outcome_Spayed Female',\n",
      " 'Sex upon Outcome_Unknown',\n",
      " 'Breed_Boxer',\n",
      " 'Breed_Chihuahua',\n",
      " 'Breed_Pit Bull',\n",
      " 'Breed_Rare',\n",
      " 'Breed_Very Rare',\n",
      " 'Color_Blue',\n",
      " 'Color_Brown',\n",
      " 'Color_Gray',\n",
      " 'Color_Orange',\n",
      " 'Color_Yellow')\n",
      "\n",
      "Top 5 feature subsets in descending order of average score and ascending order of size:\n"
     ]
    },
    {
     "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>feature_idx</th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>feature_names</th>\n",
       "      <th>ci_bound</th>\n",
       "      <th>std_dev</th>\n",
       "      <th>std_err</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(0, 1, 4, 5, 7, 8, 9, 13, 14, 20, 22, 27, 29, ...</td>\n",
       "      <td>[0.782608695652174, 0.7200000000000001, 0.5531...</td>\n",
       "      <td>0.624434</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_O...</td>\n",
       "      <td>0.060523</td>\n",
       "      <td>0.081489</td>\n",
       "      <td>0.027163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(0, 1, 4, 5, 7, 8, 9, 20, 22, 27, 29, 30, 32)</td>\n",
       "      <td>[0.782608695652174, 0.7083333333333333, 0.5531...</td>\n",
       "      <td>0.623021</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_O...</td>\n",
       "      <td>0.056473</td>\n",
       "      <td>0.076036</td>\n",
       "      <td>0.025345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(0, 1, 4, 5, 7, 8, 9, 13, 20, 22, 27, 29, 30, ...</td>\n",
       "      <td>[0.782608695652174, 0.7200000000000001, 0.5217...</td>\n",
       "      <td>0.621432</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_O...</td>\n",
       "      <td>0.063192</td>\n",
       "      <td>0.085083</td>\n",
       "      <td>0.028361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(0, 1, 4, 5, 7, 8, 9, 20, 22, 27, 29, 30, 31, 32)</td>\n",
       "      <td>[0.782608695652174, 0.7200000000000001, 0.5217...</td>\n",
       "      <td>0.621424</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_O...</td>\n",
       "      <td>0.063477</td>\n",
       "      <td>0.085466</td>\n",
       "      <td>0.028489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(0, 1, 4, 5, 7, 8, 9, 13, 14, 20, 22, 27, 29, ...</td>\n",
       "      <td>[0.782608695652174, 0.7058823529411765, 0.5957...</td>\n",
       "      <td>0.620966</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_O...</td>\n",
       "      <td>0.061047</td>\n",
       "      <td>0.082195</td>\n",
       "      <td>0.027398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          feature_idx  \\\n",
       "#                                                       \n",
       "17  (0, 1, 4, 5, 7, 8, 9, 13, 14, 20, 22, 27, 29, ...   \n",
       "13      (0, 1, 4, 5, 7, 8, 9, 20, 22, 27, 29, 30, 32)   \n",
       "15  (0, 1, 4, 5, 7, 8, 9, 13, 20, 22, 27, 29, 30, ...   \n",
       "14  (0, 1, 4, 5, 7, 8, 9, 20, 22, 27, 29, 30, 31, 32)   \n",
       "18  (0, 1, 4, 5, 7, 8, 9, 13, 14, 20, 22, 27, 29, ...   \n",
       "\n",
       "                                            cv_scores avg_score  \\\n",
       "#                                                                 \n",
       "17  [0.782608695652174, 0.7200000000000001, 0.5531...  0.624434   \n",
       "13  [0.782608695652174, 0.7083333333333333, 0.5531...  0.623021   \n",
       "15  [0.782608695652174, 0.7200000000000001, 0.5217...  0.621432   \n",
       "14  [0.782608695652174, 0.7200000000000001, 0.5217...  0.621424   \n",
       "18  [0.782608695652174, 0.7058823529411765, 0.5957...  0.620966   \n",
       "\n",
       "                                        feature_names  ci_bound   std_dev  \\\n",
       "#                                                                           \n",
       "17  (Name, Age in Days upon Outcome, Animal Type_O...  0.060523  0.081489   \n",
       "13  (Name, Age in Days upon Outcome, Animal Type_O...  0.056473  0.076036   \n",
       "15  (Name, Age in Days upon Outcome, Animal Type_O...  0.063192  0.085083   \n",
       "14  (Name, Age in Days upon Outcome, Animal Type_O...  0.063477  0.085466   \n",
       "18  (Name, Age in Days upon Outcome, Animal Type_O...  0.061047  0.082195   \n",
       "\n",
       "     std_err  \n",
       "#             \n",
       "17  0.027163  \n",
       "13  0.025345  \n",
       "15  0.028361  \n",
       "14  0.028489  \n",
       "18  0.027398  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfs[clf_name][\"backward\"] = sequential_fs(dt, forward=False)\n",
    "sfs[clf_name][\"backward\"][\"top5\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be963176",
   "metadata": {},
   "source": [
    "#### ii. Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "81c9a0a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit SFS: 3.619 seconds\n",
      "Scoring: f1\n",
      "Smallest feature subset (size = 3) with max. score (0.5640204124324624):\n",
      "('Animal Type_Other', 'Breed_Bat', 'Color_Red')\n",
      "\n",
      "Top 5 feature subsets in descending order of average score and ascending order of size:\n"
     ]
    },
    {
     "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>feature_idx</th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>feature_names</th>\n",
       "      <th>ci_bound</th>\n",
       "      <th>std_dev</th>\n",
       "      <th>std_err</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(4, 11, 34)</td>\n",
       "      <td>[0.7999999999999999, 0.6122448979591837, 0.478...</td>\n",
       "      <td>0.56402</td>\n",
       "      <td>(Animal Type_Other, Breed_Bat, Color_Red)</td>\n",
       "      <td>0.072526</td>\n",
       "      <td>0.09765</td>\n",
       "      <td>0.03255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(0, 4, 11, 34)</td>\n",
       "      <td>[0.7999999999999999, 0.6122448979591837, 0.478...</td>\n",
       "      <td>0.56402</td>\n",
       "      <td>(Name, Animal Type_Other, Breed_Bat, Color_Red)</td>\n",
       "      <td>0.072526</td>\n",
       "      <td>0.09765</td>\n",
       "      <td>0.03255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(0, 1, 4, 11, 34)</td>\n",
       "      <td>[0.7999999999999999, 0.6122448979591837, 0.478...</td>\n",
       "      <td>0.56402</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_O...</td>\n",
       "      <td>0.072526</td>\n",
       "      <td>0.09765</td>\n",
       "      <td>0.03255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(0, 1, 2, 4, 11, 34)</td>\n",
       "      <td>[0.7999999999999999, 0.6122448979591837, 0.478...</td>\n",
       "      <td>0.56402</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.072526</td>\n",
       "      <td>0.09765</td>\n",
       "      <td>0.03255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(0, 1, 2, 3, 4, 11, 34)</td>\n",
       "      <td>[0.7999999999999999, 0.6122448979591837, 0.478...</td>\n",
       "      <td>0.56402</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.072526</td>\n",
       "      <td>0.09765</td>\n",
       "      <td>0.03255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature_idx                                          cv_scores  \\\n",
       "#                                                                               \n",
       "3              (4, 11, 34)  [0.7999999999999999, 0.6122448979591837, 0.478...   \n",
       "4           (0, 4, 11, 34)  [0.7999999999999999, 0.6122448979591837, 0.478...   \n",
       "5        (0, 1, 4, 11, 34)  [0.7999999999999999, 0.6122448979591837, 0.478...   \n",
       "6     (0, 1, 2, 4, 11, 34)  [0.7999999999999999, 0.6122448979591837, 0.478...   \n",
       "7  (0, 1, 2, 3, 4, 11, 34)  [0.7999999999999999, 0.6122448979591837, 0.478...   \n",
       "\n",
       "  avg_score                                      feature_names  ci_bound  \\\n",
       "#                                                                          \n",
       "3   0.56402          (Animal Type_Other, Breed_Bat, Color_Red)  0.072526   \n",
       "4   0.56402    (Name, Animal Type_Other, Breed_Bat, Color_Red)  0.072526   \n",
       "5   0.56402  (Name, Age in Days upon Outcome, Animal Type_O...  0.072526   \n",
       "6   0.56402  (Name, Age in Days upon Outcome, Animal Type_C...  0.072526   \n",
       "7   0.56402  (Name, Age in Days upon Outcome, Animal Type_C...  0.072526   \n",
       "\n",
       "   std_dev  std_err  \n",
       "#                    \n",
       "3  0.09765  0.03255  \n",
       "4  0.09765  0.03255  \n",
       "5  0.09765  0.03255  \n",
       "6  0.09765  0.03255  \n",
       "7  0.09765  0.03255  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "clf_name = gnb.__class__.__name__\n",
    "sfs[clf_name][\"forward\"] = sequential_fs(gnb, forward=True)\n",
    "sfs[clf_name][\"forward\"][\"top5\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "162a36cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit SFS: 4.164 seconds\n",
      "Scoring: f1\n",
      "Smallest feature subset (size = 3) with max. score (0.5640204124324624):\n",
      "('Animal Type_Other', 'Breed_Bat', 'Color_Red')\n",
      "\n",
      "Top 5 feature subsets in descending order of average score and ascending order of size:\n"
     ]
    },
    {
     "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>feature_idx</th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>feature_names</th>\n",
       "      <th>ci_bound</th>\n",
       "      <th>std_dev</th>\n",
       "      <th>std_err</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(4, 11, 34)</td>\n",
       "      <td>[0.7999999999999999, 0.6122448979591837, 0.478...</td>\n",
       "      <td>0.56402</td>\n",
       "      <td>(Animal Type_Other, Breed_Bat, Color_Red)</td>\n",
       "      <td>0.072526</td>\n",
       "      <td>0.09765</td>\n",
       "      <td>0.03255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(0, 4, 11, 34)</td>\n",
       "      <td>[0.7999999999999999, 0.6122448979591837, 0.478...</td>\n",
       "      <td>0.56402</td>\n",
       "      <td>(Name, Animal Type_Other, Breed_Bat, Color_Red)</td>\n",
       "      <td>0.072526</td>\n",
       "      <td>0.09765</td>\n",
       "      <td>0.03255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(0, 1, 4, 11, 34)</td>\n",
       "      <td>[0.7999999999999999, 0.6122448979591837, 0.478...</td>\n",
       "      <td>0.56402</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_O...</td>\n",
       "      <td>0.072526</td>\n",
       "      <td>0.09765</td>\n",
       "      <td>0.03255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(0, 1, 2, 4, 11, 34)</td>\n",
       "      <td>[0.7999999999999999, 0.6122448979591837, 0.478...</td>\n",
       "      <td>0.56402</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.072526</td>\n",
       "      <td>0.09765</td>\n",
       "      <td>0.03255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(0, 1, 2, 3, 4, 11, 34)</td>\n",
       "      <td>[0.7999999999999999, 0.6122448979591837, 0.478...</td>\n",
       "      <td>0.56402</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.072526</td>\n",
       "      <td>0.09765</td>\n",
       "      <td>0.03255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               feature_idx                                          cv_scores  \\\n",
       "#                                                                               \n",
       "3              (4, 11, 34)  [0.7999999999999999, 0.6122448979591837, 0.478...   \n",
       "4           (0, 4, 11, 34)  [0.7999999999999999, 0.6122448979591837, 0.478...   \n",
       "5        (0, 1, 4, 11, 34)  [0.7999999999999999, 0.6122448979591837, 0.478...   \n",
       "6     (0, 1, 2, 4, 11, 34)  [0.7999999999999999, 0.6122448979591837, 0.478...   \n",
       "7  (0, 1, 2, 3, 4, 11, 34)  [0.7999999999999999, 0.6122448979591837, 0.478...   \n",
       "\n",
       "  avg_score                                      feature_names  ci_bound  \\\n",
       "#                                                                          \n",
       "3   0.56402          (Animal Type_Other, Breed_Bat, Color_Red)  0.072526   \n",
       "4   0.56402    (Name, Animal Type_Other, Breed_Bat, Color_Red)  0.072526   \n",
       "5   0.56402  (Name, Age in Days upon Outcome, Animal Type_O...  0.072526   \n",
       "6   0.56402  (Name, Age in Days upon Outcome, Animal Type_C...  0.072526   \n",
       "7   0.56402  (Name, Age in Days upon Outcome, Animal Type_C...  0.072526   \n",
       "\n",
       "   std_dev  std_err  \n",
       "#                    \n",
       "3  0.09765  0.03255  \n",
       "4  0.09765  0.03255  \n",
       "5  0.09765  0.03255  \n",
       "6  0.09765  0.03255  \n",
       "7  0.09765  0.03255  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfs[clf_name][\"backward\"] = sequential_fs(gnb, forward=False)\n",
    "sfs[clf_name][\"backward\"][\"top5\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d5a9a3",
   "metadata": {},
   "source": [
    "#### iii. SVM (Support Vector Machine) Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c4e28c9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IsRmq/hQfor5Xxv+DYprB4UMdF4ok9/0UH8gWlG1+mCIBPLq23TVtm0lxScQVZczFwKE12zT6P04o639rGfe38v95cm17KOZnn11z/O+hPOGvXL9R2c6/l227q0U/i/J/dxuPPl/OBDauq1tSd/UaHu3PbxyiT/VRPB8/Wrf8XyiuQf4HYClF/50HfBqY0mRfa65kcsMQZT6Zoo/+oTyWi8tjeUJt24C3l+s2arCPKyhOfK5fflJ5nJo+R7x525Bukdlo+p8kQZQ/IJOZ07tbE+nxJYofQXotsGuOojfiKH4sZwHwjsz8Ts3ybSk+TP9TZv6sLmYecEFmNjxpVdrQOOdakqT174sUl1ysn/PeVVn8JPzngA/U/YjN+4GrGiTWh1OcBHvq+qulNLo551qSpPUsMx+KiNez9uX9RoPTKKaubMOjV/a5j2LqTb2JFFPM/rF+qiaNfk4LkSRJkiritBBJkiSpIibXkiRJUkUeV3OuN99889xxxx3Xa5mrB5OlSxYzfsKkEcWvWL6M8RNG9oux6xLbzbJtc2/EdrNs29wbsd0suxdju1m2be6N2G6W3YttHhhMBlctZ/LkySMue6Suu+66BzJzi0brHlfJ9Y477sicOa1+8Kt6Dy5azjW/uZpd9tp3RPF3zp3DzntOXe+x3SzbNvdGbDfLts29EdvNsnsxtptl2+beiO1m2b3Y5oeWrGDRPfOYMWP6iMseqYj4U7N1TguRJEmSKmJyLUmSJFXE5FqSJEmqiMm1JEmSVBGTa0mSJKkiJteSJElSRUyuJUmSpIqYXEuSJEkVMbmWJEmSKmJyLUmSJFXE5FqSJEmqiMm1JEmSVBGTa0mSJKkiJteSJElSRUyuJUmSpIqYXEuSJEkVMbmWJEmSKmJyLUmSJFXE5FqSJEmqiMm1JEmSVBGTa0mSJKkiJteSJElSRUyuJUmSpIqYXEuSJEkV6WhyHRGHRMRtEXFHRJzQZJvpEXFjRNwSEb+sW9cXETdExP91sp6SJElSFfo7teOI6APOBF4ELABmR8TFmTmvZptNgC8Dh2Tm3RGxZd1u3g3MBzbuVD0lSZKkqnRy5HoacEdm3pmZK4GZwOF127wGuDAz7wbIzPvXrIiI7YGXAl/vYB0lSZKkykRmdmbHEUdSjEi/tXz8emC/zDy2Zpv/BMYCTwemAKdn5jnluvOBz5TLj8/MlzUp52jgaICtttpqn5kzZ3akPc0MDCZLFi9m/MRJI4pfsXwp4yes/9hulm2beyO2m2Xb5t6I7WbZvRjbzbJtc2/EdrPsXmzzwGAysHI5U6ZMHnHZIzVjxozrMnNqo3UdmxYCRINl9Zl8P7AP8EJgIvDbiJgF7Arcn5nXRcT0VoVk5tnA2QBTp07N6dNbbl65Bxct55rfXM3OezY8vkO6c+6crsR2s2zb3Bux3SzbNvdGbDfL7sXYbpZtm3sjtptl92KbH1qygkX3zGN9535D6WRyvQDYoebx9sDCBts8kJlLgCURcRXwTODZwGERcSgwAdg4Ir6Tma/rYH0lSZKkddLJOdezgV0iYqeIGAccBVxct80PgedHRH9ETAL2A+Zn5omZuX1m7ljG/dzEWpIkSaNdx0auM3N1RBwLXAb0Ad/MzFsi4phy/VmZOT8iLgV+BwwCX8/MuZ2qkyRJktRJnZwWQmZeAlxSt+ysusenAKe02MeVwJUdqJ4kSZJUKX+hUZIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTkWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTkWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTkWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTkWpIkSaqIybUkSZJUEZNrSZIkqSIdTa4j4pCIuC0i7oiIE5psMz0iboyIWyLil+WyHSLiFxExv1z+7k7WU5IkSapCf6d2HBF9wJnAi4AFwOyIuDgz59VsswnwZeCQzLw7IrYsV60G3peZ10fEFOC6iLiiNlaSJEkabTo5cj0NuCMz78zMlcBM4PC6bV4DXJiZdwNk5v3l3/sy8/ry/iJgPrBdB+sqSZIkrbPIzM7sOOJIihHpt5aPXw/sl5nH1mzzn8BY4OnAFOD0zDynbj87AlcBe2bmww3KORo4GmCrrbbaZ+bMmR1pTzMDg8mSxYsZP3HSiOJXLF/K+AnrP7abZdvm3ojtZtm2uTdiu1l2L8Z2s2zb3Bux3Sy7F9s8MJgMrFzOlCmTR1z2SM2YMeO6zJzaaF3HpoUA0WBZfSbfD+wDvBCYCPw2ImZl5u0AETEZuAB4T6PEGiAzzwbOBpg6dWpOnz69mtq36cFFy7nmN1ez854Nj++Q7pw7pyux3SzbNvdGbDfLts29EdvNsnsxtptl2+beiO1m2b3Y5oeWrGDRPfNY37nfUDqZXC8Adqh5vD2wsME2D2TmEmBJRFwFPBO4PSLGUiTW52bmhR2spyRJklSJTs65ng3sEhE7RcQ44Cjg4rptfgg8PyL6I2ISsB8wPyIC+AYwPzNP62AdJUmSpMp0bOQ6M1dHxLHAZUAf8M3MvCUijinXn5WZ8yPiUuB3wCDw9cycGxHPA14P3BwRN5a7/I/MvKRT9ZUkSZLWVSenhVAmw5fULTur7vEpwCl1y66m8ZxtSZIkadTyFxolSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTkWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTkWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTkWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTkWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIq0tHkOiIOiYjbIuKOiDihyTbTI+LGiLglIn45nFhJkiRpNOnv1I4jog84E3gRsACYHREXZ+a8mm02Ab4MHJKZd0fElu3GSpIkSaNNJ0eupwF3ZOadmbkSmAkcXrfNa4ALM/NugMy8fxixkiRJ0qgSmdmZHUccSTEi/dby8euB/TLz2Jpt/hMYCzwdmAKcnpnntBNbs4+jgaMBttpqq31mzpzZkfY0MzCYLFm8mPETJ40ofsXypYyfsP5ju1m2be6N2G6WbZt7I7abZfdibDfLts29EdvNsnuxzQODycDK5UyZMnnEZY/UjBkzrsvMqY3WdWxaCBANltVn8v3APsALgYnAbyNiVpuxxcLMs4GzAaZOnZrTp08faX1H5MFFy7nmN1ez854Nj++Q7pw7pyux3SzbNvdGbDfLts29EdvNsnsxtptl2+beiO1m2b3Y5oeWrGDRPfNY37nfUDqZXC8Adqh5vD2wsME2D2TmEmBJRFwFPLPNWEmSJGlU6eSc69nALhGxU0SMA44CLq7b5ofA8yOiPyImAfsB89uMlSRJkkaVjo1cZ+bqiDgWuAzoA76ZmbdExDHl+rMyc35EXAr8DhgEvp6ZcwEaxXaqrpIkSVIVOjkthMy8BLikbtlZdY9PAU5pJ1aSJEkazfyFRkmSJKkiJteSJElSRUyuJUmSpIqYXEuSJEkVMbmWpB518YVjmb7vFF7yTwcxfd8pXHzh2G5XSZI2eCbXktSDLr5wLB96/0QW3juGzGDhvWP40Psntp1gr2tivi7xG1qsNgy9+pxS9Tp6KT5JUmec9pkJLF8Wj1m2fFnwiQ9NYOlSGNsPff3Q3wd9/VlzH2b/to9vf208K1cW8QvvDf7jfRP50x/H8Pzpq4cs+1dX9vPVM8azcsXw4x8vsR96/0QADjtiVctYbRjWfNhd85xc07/+8PsxPPfAoZ9Tv76qn2+ctXb/bCe+Uaz9s7tMrjdAF184ltM+M4H7Fh7ENtsmx524vO0nYC/GSu0YzX17xXK4/bY+5s0dw/y5fcyb28fCe6Ph/h76xxg+8oFJbZVda+WK4EunTuBLpw47dJ3jezF2+bLgpOMn8vvbxrDb0wbZdfcBdnrKIGObDBj6+vf4lQmf/djaH3ZXrgi+cvoEvnL6yPa7LvHLlwWnfmaC/aRLTK43MI0+Xbf7CbcXY6V2jKq+ffxE7rxjDJtulswrE+k/3D6G1auLbaZsnOz+9AE2mgxLFq+9z623GeT7P17M6tUwsDqKvwM88ndgdfCqf94IslFynnz93KUt6wzw1tdOAkYW/3iKXbEcvvGV8Y/8b8aOTXZ+6iC7Pm2A3XYfYNfdB9ntaQPMntXPhz7g69/jzYJ7gh+eP46Lzh/LA39tMss2knPOWzLkvt7w6ibPyTbim8Xed29w8okTeMWrV7HX3gNE48/j6gCT6w3IsqWNP10vXxZ8+AMTufqXrbvDZT8eO6piT/20n8q17pYva/68+MgHJ3LDnL6W8T/4/riGsR/94ERuur6PCB55U4vgkRwtAs47t0Hs8uDL/zkBgC22HGT3PQeYcfAq9thzgD2eMcAOT0oi1k66ACZMTI4/aTlbbZ3lkqSRbbfNhiPf226XHDhj6K+wt91u5PGPt9jLf72IO+8Yw+239nHb/OLvnGv6+dEPxj2yXUSSuXYfOfmEidx6S+v+NfN/Gvev0xyV7IrFi+DS/xvLD74/jtmziveu/Z6zmof+Mcg//r52gr3ttsn+zx0Ycr9Nn5NtxDeLnTgRLvjfcXz3v8ez81MHeMWrVnH4K1ey9baNXxdUHZPrHtTOV4SLF8H8W/q45ebiNu/mPv7w+zEMDjb+6LpsKcyZ1bo7LGsyuNOt2PsWBi+dMZk9njHA058xwNP3GmD3pw8wefLa225oX6uua3tH8xSJdYl9+KHiebFmRHj+3OJ5MTDQ+HmxdAn8+IetTwxa2mRQackS+OEF48jyfSyTR+5T3l/adMA0ufqGRWy5VfM3wTVtK9ocwzpex524vGFiftyJy4eMXdf4x1vsuHHwtD0Gedoeg4+JefghuP3WPm6/tY+TT5zQcL+LF8N3vjWu4bo1ljep3sJ7g3e8eRK7P32g+OC15wBbb5trjU52czrKaH4tGE7soYet4je/6uei74/likvHsmJ5sNPOA7z3g8s57JUr2W77bPpht5vPqU+csowZB6/ikh+N5QfnjePUz0zgtM+O5znPX80rXr2KFx2yiomTNrz3x/UhMh8/n2CmTp2ac+bMWa9lPrhoOdf85mp22WvfEcXfOXcOO+85te3tGz6BJyRveOsKnvCELBLpuX3cdeejoyFbbj3I08tRr++dM46/Pdjg0/V2g1w5e1HLsqfvO4WF946e2MlTBpm63wDzbu7j/r88un6nnQceSbj3eMYAd981hk+fvPYLzydPWTasF5Dh/q+g9kVreMnPupTb7EW+3fauS/xoih0/IXndG1ew8RMok+kx3POnxz4v1iQl3ztnHH//2+jp2+3E1upG31yX+A0tthN9ZMLEZJttBrnrj2MeGRXfZNNBdn/6YJFwP2OAP98XnHHahPX+fFzX+NEU29efTJqYLFo0hidsMshLD1/FK161ir2etfY0i9H+nLr7rjH84Ptj+eH541hwzxg22ijZ4xkD3HRD3yMnQ8L6e3+sIv6hJStYdM88ZsyYPuKyRyoirsvMhpU2uV5H6zu5fsG+U7ivwQvtGtvvMPiYxHKPPQfYYstH/8ej6UWrytj7/xJFAlUzUn/vgtZXmux0ArOub04jLbdZH5k4MTnoxUOX+/PLx7Ks7mvoduNHW+waT96peC7svuejCfXmW/T286LWuryxdetNcUOK7WQfWbIEbpvX98g3MvPnjuH22x6bLNXr9PNxXeNHW+yECckXzlzK9INWM258y10Ao/85NTgIc67p4wfnjePC88auNWUJ1s8H/CriR2ty7bSQUW7xYrhxTj/XXdvHnGv7ua/JFQKI5Nq5i9hk09Yfltblq+TRHLvlVsmWW61m+gsfnTv5978F8+aO4U1HbUSjk5EW3hucfsp4pk4bYO+pq9looyGr0paBAbjnT2P41Ecaz+P9/Ccm8LKXr2JMRVeZX/QwXF/2keta9JFly2Deza3nd67ZbqTxoy2WSK6/9WEmT2kZPqr7tnpfJ/vIRhvBs/cd4Nn7Pjovd9UquPOOMfzzCyfT6LWv08/HdY0fbbErVsCLXzL0uQi9YswYmHbAANMOWMaF5zWe9nbfQs9+XBcm113SbI7TX++PIpG+pp/rru1n/i3FPOkxY5Ldnz7Y9AoB226bQybWaxx2xCoOO2LViD4p9lLsppslzz1woOnJSP1j4Sunj2dwMOjrK67AsM+0Aabut5p9pj06stlqPtoDf41HTmK6fX4ft906hjtu62P58uYvTPf/ZQzP3nVjnrrbALs+bbC4qsDTBtht90E2e+JjR1Mblfvn+4Lrru1nzjVFMn3b/OJr4b6+4iu+pn1ku+SyqxusqFN8Dd345K2h4kdd7LY5ZGK9Ri/1bfWe9dlHxo6F3XYfbHkiZiefj+saP9pit3kcnwC4TZOTIQE+9/EJvPmYFY/59lvt8Rcau6DRL6t98N0T2X+vKTx3741519Ebcd6545iycfL2d63gW99bwpxbH+YHly3mY59dxoSJj+3owzlpYkN03InLGx6zz35xGXPmP8w3v7uEf3/nCjaaDDO/M453vm0jnvPMjXnx8ybz2iM24sT3rv2/esn0yez/jCk855kb86ajJvOZkydy5c/6mTIFjnr9Sj596lI233KwYX2esMkgR/7rSiZNgp9d1s+nPjKRf3v1ZPZ/xsY855lTeOO/TOItr53UsNz99pzCgftszHvfPokfnDeOzZ44yLHHreC/z1vMdbc9zAWXLFnnPtLseLV7Yk2vxUqPV918TvXia8GG+DrSqM3jxyd77zPAt84ex0H7T+ETH5rAnx3JHhZHrrug0S+rDQwESxfDBz+yjKnTBth9zwHGNTiJ3K+Rh2+oY/a86at53vTVwApWriy+OlwzDefnV/STg2v/r+7+4xgOf+Uqdq0ZdX7i5o99gRo3noZzJT/8yUfLzixGv9eMet8+v4/bbx3D3N/1Uf917sBAsGwZ/MfHlrHPtOLKKP0NnsHr2kd6cYqEzwtpbd18TvXia8GG+DrSqs133TmGr35pPN87Zxwz/2ccR/zLSo4+dgU7PMmR7KGYXHdBs7lMK1bAW45ZOWS8XyMPX7vHbNw42HufAfbeZ4C3vH0lu223ccPtVq+GT5/WbKLvo2VC6xfqCNhiy2SLLVfz3Bc8GrvbdhvT6FzjFcvhjW/rfB/pxSkSPi+ktXXzOdWLrwUb4utIszbvuPMgn/niMv7fccv52pnjOX/mOM7/3jgOf+UqjnnXCnbcufG3s3JaSFc0m7/1eJ7X1avW9X912BGruHL2In5y2c+5cvaitkdA7COSpNFg+x2Sj312OT/7zSJe96aV/PjisRxy4GSOe8dEzj5zHNP3ncJL/ukgpu87hYsvbP27ABsKk+suOO7E5Ywbv2HN6+pV3ZqDtyHO/ZMkjV5bb5uc9PHl/OKaRbz531dy2Y/H8oVPPfbcoA+9f6IJNibXXXHYEas4+J+KEcyIZNvtBod9XVutH4cdsYpPnrKMbbcbXK//q26VK0lSK5tvkXzgw8vXOs8IisvNnvaZxr9IuiFxznWXLFoUPHXXAc4448oNZl5Xr+rWHLwNce6fJKk3/OXPjc8fW3hvcOF5Y3nZy1c1vDDDhsCR6y5YvRqun93Pvvs/fi5KL0mSNhzNzgHq74cT3jOJg/afwtlnjOPhh9ZzxUYBk+suuHXeGJYsDvbdb2DojSVJkkaZVr8h8Y3vLuGpuwzwhU9P5AVTN+ZTH5nAgns2nGtlm1x3wexZxWycqfs5ci1JknpP03ODXrmK509fzbf/dykXXb6Ig1+yinO/PY6DD5jCe46ZyM03Pfoz9BdfOPZxebUR51x3wexZ/ezw5AG23ja582/dro0kSdLwDXVu0B57DnLKfy3jfScs55xvjGfmd8ZxycXjmHbAavbcazXfPWf8Iz+0tvDe4EPvn/jIfnuZI9fr2eAgzLmmj2n7OyVEkiQ9/m29bXGFkavmPMwJH13GPX8awze/uvavVT9erjZicr2e3XH7GP7x9zGezChJkjYok6fAm/99JT/97SKg8QmRzX7FupeYXK9nj8y3NrmWJEkboLFjYdvtHr+/RGxyvZ7NntXHVtsMssOTer/zSJIkjUTDq41MeHz8ErEnNK5HmTDnmn6mHbCa6P1vPSRJkkZkzUmLp31mAgvvLZKiXXcf4J9f0dsnM4Ij1+vV3XeN4f6/jGFfT2aUJEkbuMOOWMWVsxdx6eU/57gTV/C7G/q5YGbvX47P5Ho9mj2ruLbjvgc431qSJGmNt71jBfs/dzWf+NBE7ryjt9PT3q59j7l2Vj+bbjbIU5462O2qSJIkjRp9ffD5/1rK+AnJce+YxMoV3a7RyJlcr0dzZvWz7/4DzreWJEmqs/U2yadPXca8uX2c+tnevd51R5PriDgkIm6LiDsi4oQG66dHxEMRcWN5+0jNuvdGxC0RMTcivhcRvXuUgYULggX3eH1rSZKkZg4+ZDWv+bcVfOur47nqF7153Y2OJdcR0QecCbwE2AP414jYo8Gmv8rMvcvbx8vY7YB3AVMzc0+gDziqU3VdH+ZcW3SQffczuZYkSWrmhI8sZ9enDfDBd0/kgb/23tf9I0quI2JyG5tNA+7IzDszcyUwEzh8GMX0AxMjoh+YBCwcfk1Hj9mz+piycbLbHs63liRJambCRDjty0tZvDj44HsmMthjqVNkDv/HTCLi7sx80hDbHAkckplvLR+/HtgvM4+t2WY6cAGwgCJ5Pj4zbynXvRv4FLAMuDwzX9uknKOBowG22mqrfWbOnDns9qyLgcFkyeLFjJ84qeV2b3vL/myz7TI+/ombHrN8xfKljJ/QOraZdYntZtm2uTdiu1m2be6N2G6W3Yux3SzbNvdGbDfLHo1t/tHF23HmGU/j6H+/nSNeec9a6wcGk4GVy5kypZ0x32rNmDHjusyc2mhd08ksEXFcs1VAO61oNI5fn8lfDzw5MxdHxKHARcAuEbEpxSj3TsA/gO9HxOsy8ztr7TDzbOBsgKlTp+b06dPbqFp1Hly0nGt+czU779nw+BbbPBDcc89GHPWGMWttd+fcOS1jW1mX2G6WbZt7I7abZdvm3ojtZtm9GNvNsm1zb8R2s+zR2OZ3PR1u/f0qvvXNXTj0iG3Y4xmPHcJ+aMkKFt0zj/Wd+w2l1bSQTwObAlPqbpOHiFtjAbBDzePtqZvakZkPZ+bi8v4lwNiI2Bw4GPhjZv41M1cBFwLPaatFo9Aj17f2x2MkSZLaEgGf+sIyNn1i8t53TGLp0m7XqD2tTsO8HrgoM6+rXxERb21j37MpRqF3Au6lOCHxNXX72Rr4S2ZmREyjSNofBO4G9o+ISRTTQl4IzGmjzFFp9qx+Jk5M9niGybUkSVK7Nnti8oUvLeXfXr0Rn/zwRD596rJuV2lIrUag7wX+VM59rjfk2H9mrgaOBS4D5gPnZeYtEXFMRBxTbnYkMDcibgL+CzgqC9cA51Mk+DeX9Ty73UaNNrNn9bP3PgOMG9ftmkiSJPWW/Z87wNHHruD8743jJz8a/Zfna1XDPYCNgDdHxDk8dg71qnZ2Xk71uKRu2Vk1988AzmgS+1Hgo+2UM5o99A+4bf4Y3vm+Hv6pIUmSpC561/ErmPXrfj70/kns9axFbLf98C/Isb60Grn+KnAp8DTgurpbz07RWN+un91PZjDtAK9vLUmSNBJjx8KpZy5lcBDe9/8msXoUp1VNk+vM/K/M3B34ZmbunJk71dx2Xo917GmzZ/Uzdlyy197Ot5YkSRqpJz05+fhnl3H97H7e/e8T+ecXPJEXvvAF7LgjnHtut2v3qCGv+pGZb18fFXm8unZWH3vtPcCEid2uiSRJUm/75yNWsc+01Vzxk7H8eWEfmcGf/gRHHz16EuyO/fy5YMkSuOV3fey7/yj+7kKSJKmHLFwwhvqfU1m6FE46qTv1qWdy3UE3zulnYCC8vrUkSVJF/nxfo98phLvvXs8VacLkuoNmX9NHX1/yrKmOXEuSJFVhm20bXynkSU9azxVpwuS6g2bP6mePZwwwef3/5L0kSdLj0nEnLmfCxMcm2JMmwac+1aUK1TG57pAVy+GmG/rYdz+nhEiSJFXlsCNW8clTlrH1tgNEJE9+Mpx9Nrz2td2uWWH0/8xNj/rdjX2sXBGezChJklSxw45YxQv+aTGL7pnHjBnTu1ybx3LkukNmzyo+t+wzzZFrSZKkDYXJdYfMntXHbrsPsMmmo/fnOSVJklQtk+sOWLUKbpjT75QQSZKkDYzJdQfMm9vH0qXOt5YkSdrQmFx3wJxZfQBM9UohkiRJGxST6w649rf97LTzAFts6XxrSZKkDYnJdcUGBmDOtf1M9SfPJUmSNjgm1xW7/dYxLHrY+daSJEkbIpPriq25vvU0k2tJkqQNjsl1xeZc08922w+y7fbOt5YkSdrQmFxXKLP48ZipjlpLkiRtkEyuK/THP4zhwQfGsO9+JteSJEkbIpPrCl1bXt96X68UIkmStEEyua7QnFn9bL7FIDvuPNjtqkiSJKkLTK4rkln8eMy++68motu1kSRJUjeYXFfk3gXBn+8b45QQSZKkDZjJdUXWXN/aH4+RJEnacJlcV2T2rH6esMkgu+zmfGtJkqQNlcl1RWbP6mPqtAHGeEQlSZI2WKaCFXjwwXH86Y/+eIwkSdKGzuS6ArfM3RSAaQd4MqMkSdKGzOS6AnNv3oSNNkp2f7rJtSRJ0obM5LoCc+duyrP3XU1/f7drIkmSpG4yuV4H554Le+0+jj/dNZkbr+/j4gvHdrtKkiRJ6iLHWkfo3HPh6KNh6dLi88mih8fwofdPBOCwI1Z1s2qSJEnqEkeuR+ikk2Dp0scuW74sOO0zE7pTIUmSJHVdR5PriDgkIm6LiDsi4oQG66dHxEMRcWN5+0jNuk0i4vyIuDUi5kfEAZ2s63DdfXfj5fctjPVbEUmSJI0aHZsWEhF9wJnAi4AFwOyIuDgz59Vt+qvMfFmDXZwOXJqZR0bEOGBSp+o6Ek96EvzpT2sv32bbXP+VkSRJ0qjQyZHracAdmXlnZq4EZgKHtxMYERsDBwLfAMjMlZn5j05VdCQ+9SmYVJfuT5iYHHfi8u5USJIkSV0XmZ0ZaY2II4FDMvOt5ePXA/tl5rE120wHLqAY2V4IHJ+Zt0TE3sDZwDzgmcB1wLszc0mDco4GjgbYaqut9pk5c2ZH2tPIT3+6JV/72k789a8T2GKL5bzxTX/goBf+ZVj7WLF8KeMnjGxQfl1iu1m2be6N2G6WbZt7I7abZfdibDfLts29EdvNsnuxzQODycDK5UyZMnnEZY/UjBkzrsvMqY3WdTK5fhXwT3XJ9bTMfGfNNhsDg5m5OCIOBU7PzF0iYiowC3huZl4TEacDD2fmh1uVOXXq1JwzZ05H2tPMg4uWc81vrmaXvfYdUfydc+ew854N/zcdje1m2ba5N2K7WbZt7o3Ybpbdi7HdLNs290ZsN8vuxTY/tGQFi+6Zx4wZ00dc9khFRNPkupPTQhYAO9Q83p5idPoRmflwZi4u718CjI2IzcvYBZl5Tbnp+cCzO1hXSZIkaZ11MrmeDewSETuVJyQeBVxcu0FEbB0RUd6fVtbnwcz8M3BPROxWbvpCiikikiRJ0qjVsauFZObqiDgWuAzoA75Zzqc+plx/FnAk8PaIWA0sA47KR+epvBM4t0zM7wTe1Km6SpIkSVXo6C80llM9LqlbdlbN/TOAM5rE3giMfAKPJEmStJ75C42SJElSRUyuJUmSpIqYXEuSJEkVMbmWJEmSKmJyLUmSJFXE5FqSJEmqiMm1JEmSVBGTa0mSJKkiJteSJElSRUyuJUmSpIqYXEuSJEkVMbmWJEmSKmJyLUmSJFXE5FqSJEmqiMm1JEmSVBGTa0mSJKkiJteSJElSRUyuJUmSpIqYXEuSJEkVMbmWJEmSKmJyLUmSJFXE5FqSJEmqiMm1JEmSVBGTa0mSJKkiJteSJElSRUyuJUmSpIqYXEuSJEkVMbmWJEmSKmJyLUmSJFXE5FqSJEmqiMm1JEmSVBGTa0mSJKkiJteSJElSRUyuJUmSpIqYXEuSJEkV6WhyHRGHRMRtEXFHRJzQYP30iHgoIm4sbx+pW98XETdExP91sp6SJElSFfo7teOI6APOBF4ELABmR8TFmTmvbtNfZebLmuzm3cB8YONO1VOSJEmqSidHrqcBd2TmnZm5EpgJHN5ucERsD7wU+HqH6idJkiRVKjKzMzuOOBI4JDPfWj5+PbBfZh5bs8104AKKke2FwPGZeUu57nzgM8CUcnnD0e2IOBo4GmCrrbbaZ+bMmR1pTzMDg8mSxYsZP3HSiOJXLF/K+AnrP7abZdvm3ojtZtm2uTdiu1l2L8Z2s2zb3Bux3Sy7F9s8MJgMrFzOlCmTR1z2SM2YMeO6zJzaaF3HpoUA0WBZfSZ/PfDkzFwcEYcCFwG7RMTLgPsz87oyAW8qM88GzgaYOnVqTp/ecvPKPbhoOdf85mp23rPh8R3SnXPndCW2m2Xb5t6I7WbZtrk3YrtZdi/GdrNs29wbsd0suxfb/NCSFSy6Zx7rO/cbSienhSwAdqh5vD3F6PQjMvPhzFxc3r8EGBsRmwPPBQ6LiLsoppMcFBHf6WBdJUmSpHXWyeR6NsUo9E4RMQ44Cri4doOI2Doiorw/razPg5l5YmZun5k7lnE/z8zXdbCukiRJ0jrr2LSQzFwdEccClwF9wDcz85aIOKZcfxZwJPD2iFgNLAOOyk5NApckSZI6rJNzrtdM9bikbtlZNffPAM4YYh9XAld2oHqSJElSpfyFRkmSJKkiJteSJElSRUyuJUmSpIqYXEuSJEkVMbmWJEmSKmJyLUmSJFXE5FqSJEmqiMm1JEmSVBGTa0mSJKkiJteSJElSRUyuJUmSpIqYXEuSJEkVMbmWJEmSKmJyLUmSJFXE5FqSJEmqiMm1JEmSVBGTa0mSJKkiJteSJElSRUyuJUmSpIqYXEuSJEkVMbmWJEmSKmJyLUmSJFXE5FqSJEmqiMm1JEmSVBGTa0mSJKkiJteSJElSRUyuJUmSpIqYXEuSJEkVMbmWJEmSKmJyLUmSJFXE5FqSJEmqiMm1JEmSVBGTa0mSJKkiJteSJElSRUyuJUmSpIp0NLmOiEMi4raIuCMiTmiwfnpEPBQRN5a3j5TLd4iIX0TE/Ii4JSLe3cl6SpIkSVXo79SOI6IPOBN4EbAAmB0RF2fmvLpNf5WZL6tbthp4X2ZeHxFTgOsi4ooGsZIkSdKo0cmR62nAHZl5Z2auBGYCh7cTmJn3Zeb15f1FwHxgu47VVJIkSapAZGZndhxxJHBIZr61fPx6YL/MPLZmm+nABRQj2wuB4zPzlrr97AhcBeyZmQ83KOdo4GiArbbaap+ZM2d2ojlNDQwmSxYvZvzESSOKX7F8KeMnrP/YbpZtm3sjtptl2+beiO1m2b0Y282ybXNvxHaz7F5s88BgMrByOVOmTB5x2SM1Y8aM6zJzaqN1HZsWAkSDZfWZ/PXAkzNzcUQcClwE7PLIDiImUyTf72mUWANk5tnA2QBTp07N6dOnr3vNh+HBRcu55jdXs/OeDY/vkO6cO6crsd0s2zb3Rmw3y7bNvRHbzbJ7MbabZdvm3ojtZtm92OaHlqxg0T3zWN+531A6OS1kAbBDzePtKUanH5GZD2fm4vL+JcDYiNgcICLGUiTW52bmhR2spyRJklSJTibXs4FdImKniBgHHAVcXLtBRGwdEVHen1bW58Fy2TeA+Zl5WgfrKEmSJFWmY9NCMnN1RBwLXAb0Ad/MzFsi4phy/VnAkcDbI2I1sAw4KjMzIp4HvB64OSJuLHf5H+XotiRJkjQqdXLO9ZqpHpfULTur5v4ZwBkN4q6m8ZxtSZIkadTyFxolSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTkWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTkWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqYnItSZIkVcTkWpIkSaqIybUkSZJUEZNrSZIkqSIm15IkSVJFTK4lSZKkiphcS5IkSRUxuZYkSZIqEpnZ7TpUJiL+CvypC0VvDjzQY7HdLNs290ZsN8u2zb0R282yezG2m2Xb5t6I7WbZG2Kb18WTM3OLhmsy09s63oA5vRbbq/W2zR4v2zx6Ynu13h4v2zxaY3u13r3a5k7dnBYiSZIkVcTkWpIkSaqIyXU1zu7B2G6WbZt7I7abZdvm3ojtZtm9GNvNsm1zb8R2s+wNsc0d8bg6oVGSJEnqJkeuJUmSpIqYXK+DiPhmRNwfEXNHELtDRPwiIuZHxC0R8e5hxE6IiGsj4qYy9mMjKL8vIm6IiP8bQexdEXFzRNwYEXOGGbtJRJwfEbeWbT+gzbjdyvLW3B6OiPcMo9z3lsdqbkR8LyImDCP23WXcLe2U2ahfRMRmEXFFRPy+/LvpMGJfVZY9GBFTh1nuKeWx/l1E/CAiNhlG7CfKuBsj4vKI2HY4ZdesOz4iMiI2H0bZJ0fEvTX/70OHU25EvDMibiuP2+eHUe7/1pR5V0TcOIzYvSNi1prnRURMaxTbIv6ZEfHb8rn1o4jYuElsw9eOdvpYi9gh+1iL2CH7WIvYIftYs9ia9UP1r2ZlD9nHWpU9VB9rUe6QfaxF7JB9rEVsu/2r4ftLm/2rWWw7/atZbDv9q1lsW69hzeJr1jftYy3Kbqd/NS23jf7VrNx2+lez2LZew1rEt9XHym0fk4O0079axLb1HrledftyJb18Aw4Eng3MHUHsNsCzy/tTgNuBPdqMDWByeX8scA2w/zDLPw74LvB/I6j7XcDmIzxm/w28tbw/DthkBPvoA/5McY3JdrbfDvgjMLF8fB7wxjZj9wTmApOAfuCnwC7D7RfA54ETyvsnAJ8bRuzuwG7AlcDUYZb7YqC/vP+5YZa7cc39dwFnDafscvkOwGUU159v2GealH0ycHwb/59GsTPK/9P48vGWw6lzzfpTgY8Mo9zLgZeU9w8FrhxmvWcDLyjvvxn4RJPYhq8d7fSxFrFD9rEWsUP2sRaxQ/axZrHD6F/Nyh6yj7WIHbKPtar3UH2sRblD9rEWse32r4bvL232r2ax7fSvZrHt9K9msW29hjWLb6ePtSi7nf7VLLad/jVkHtCifzUrt63XsBbxbfWxcv1jcpB2+leL2LbeI9fnzZHrdZCZVwF/G2HsfZl5fXl/ETCfIglsJzYzc3H5cGx5a3vyfERsD7wU+PqwKr2Oyk+xBwLfAMjMlZn5jxHs6oXAHzJzOD8Y1A9MjIh+ikR5YZtxuwOzMnNpZq4Gfgm8olVAk35xOMUHC8q/L283NjPnZ+ZtQ1W0SezlZb0BZgHbDyP24ZqHG9Gij7V4LnwR+MAIY4fUJPbtwGczc0W5zf3DLTciAng18L1hxCawZqTmCbToY03idwOuKu9fAbyySWyz144h+1iz2Hb6WIvYIftYi9gh+9gQr5Xt9K91ea1tFjtkHxuq3FZ9rEXskH2sRWy7/avZ+0s7/athbJv9q1lsO/2rWWxbr2FDvKe27GPr8n7cIrad/tWy3CH6V7PYtl7DWsS31cea5CBtvUc2im33PXJ9MrkeBSJiR+BZFJ/+2o3pK7/uuR+4IjPbjgX+k+LFYnAYMbUSuDwirouIo4cRtzPwV+Bb5Vc6X4+IjUZQ/lE0SXoaycx7gS8AdwP3AQ9l5uVths8FDoyIJ0bEJIpP8zsMs74AW2XmfWV97gO2HME+1tWbgZ8MJyAiPhUR9wCvBT4yzNjDgHsz86bhxNU4tvxK95utviJsYFfg+RFxTUT8MiL2HUHZzwf+kpm/H0bMe4BTyuP1BeDEYZY5FzisvP8q2uhnda8dw+pjI3ndaSN2yD5WHzucPlYbO5L+1aDebfexuthh9bEmx6utPlYX+x6G0cfqYtvuX03eX9rqX+vy3tRGbNP+1Sy23f7VKL7dPtai3kP2ryaxbfWvIY5Xy/7VJPY9tNm/msS328f+k7VzkHZfvxrFjjom110WEZOBC4D31H3KbikzBzJzb4pP8dMiYs82y3sZcH9mXjeS+paem5nPBl4C/L+IOLDNuH6Kr8K/kpnPApZQfP3TtogYR/Hk/f4wYjal+FS8E7AtsFFEvK6d2MycT/FV5BXApcBNwOqWQaNQRJxEUe9zhxOXmSdl5g5l3LHDKG8ScBLDTMhrfAV4CrA3xQeiU4cR2w9sSvE15fuB88pRnOH4V4bxAa70duC95fF6L+U3NMPwZorn03UUX+evbLXxSF87OhXbTh9rFNtuH6uNLcsZVv9qUHbbfaxBbNt9rMWxHrKPNYhtu481iG27f430/aWTsUP1r2ax7favBvF70WYfa1J2W/2rSWxb/WuIY92yfzWJbbt/NYkfso+tSw5SUf6yfuQomJvSyzdgR0Yw57qMHUsxl+u4dazDR2ljfmq57WeABRTzpv8MLAW+sw5lnzyMsrcG7qp5/Hzgx8Ms73Dg8mHGvAr4Rs3jNwBfHmF7Pw28Y7j9ArgN2Ka8vw1w23D7FG3MJ2sUC/wb8Ftg0nBja9Y9eah+XhsPPINiROOu8raa4puDrUdQdsvnWINjfSkwvebxH4AthnG8+oG/ANsP83/8EDxyedMAHl6H470rcG2L2LVeO9rtY41i2+1jzWLb6WOtyh2qj9XHjqB/DVV2q/9Fo2PdVh9rcbyG7GNNym2rj7XR3pb9q27bjwLHt9u/GsW227+axbbTv1qVO1T/ahL/4eH0sSHKbtq/mhzrtl/Dmhyvtl7DGpQ7rNewIdrcsI/RJAdpp381ix1J/+r0zZHrLik/hX4DmJ+Zpw0zdosoz5iOiInAwcCt7cRm5omZuX1m7kgxveLnmdnWKG5Z3kYRMWXNfYqTTdq6Wkpm/hm4JyJ2Kxe9EJjXbtmlkYwo3g3sHxGTyuP+Qop5iG2JiC3Lv08CjhhB+QAXU7xBUP794Qj2MWwRcQjwQeCwzFw6zNhdah4eRpt9DCAzb87MLTNzx7KvLaA4yerPbZa9Tc3DV9BmHytdBBxU7mdXihNnHxhG/MHArZm5YBgxUMxPfEF5/yBgOFNKavvZGOBDwFlNtmv22jFkH1vH152Gse30sRaxQ/axRrHD6V8tyh6yj7U4XhcxRB8b4li37GMtYofsYy3a227/avb+0k7/GvF7U7PYNvtXs9i2XsOaxN/QTh9rUXY7/avZ8bqIoftXq2M9VP9qFtvWa1iLNg/Zx1rkIEP2r3XNX9arbmf3vXyjSLLuA1ZRPPHeMozY51HMXf4dcGN5O7TN2L2AG8rYuTS5okEb+5nOMK8WQjFv+qbydgtw0jDj9wbmlHW/CNh0GLGTgAeBJ4ygrR+jePGYC/wP5VnYbcb+iuJDwE3AC0fSL4AnAj+jeLH6GbDZMGJfUd5fQTEacdkwYu8A7qnpY83Olm8Ue0F5vH4H/IjiBLQRPRdocYWZJmX/D3BzWfbFlCMabcaOoxgJmQtcDxw0nDoD3waOGcH/+HnAdWU/uQbYZ5jx76a4ssPtwGcpR5AaxDZ87Winj7WIHbKPtYgdso+1iB2yjzWLHUb/alb2kH2sReyQfaxVvRmij7Uod8g+1iK23f7V8P2F9vpXs9h2+lez2Hb6V7PYtl7DmsW308dalN1O/2oW207/alrnNvpXs3Lbeg1rEd9WH6vZz3QeveJHW++RTWLbeo9cnzd/oVGSJEmqiNNCJEmSpIqYXEuSJEkVMbmWJEmSKmJyLUmSJFXE5FqSJEmqiMm1JK2DiMiIOLXm8fERcXJF+/52RBxZxb6GKOdVETE/In5Rt3zHiFgWETfW3MaNYP9vjIhtq6uxJI1eJteStG5WAEdExObdrkitiOgbxuZvofjl0RkN1v0hM/euubX8WfYm3ggMK7mOiP4RlCNJXWdyLUnrZjVwNvDe+hX1I88Rsbj8Oz0ifhkR50XE7RHx2Yh4bURcGxE3R8RTanZzcET8qtzuZWV8X0ScEhGzI+J3EfHvNfv9RUR8l+IHLOrr86/l/udGxOfKZR+h+PGIsyLilHYaHBEvjojfRsT1EfH9iJi8Zl9lneZGxNlROBKYCpxbjnxPjIi71nwYiYipEXFlef/kMu5y4Jzyl+AuKPc5OyKeW273gpqR9Bui/NVYSRoNTK4lad2dCbw2Ip4wjJhnUvyi2TOA1wO7ZuY04OvAO2u225HiJ4lfSpEAT6AYaX4oM/cF9gXeFhE7ldtPo/jl1D1qCyunZXyO4meN9wb2jYiXZ+bHKX419bWZ+f4G9XxKTSJ7ZpkUfwg4ODOfXcYeV257Rmbum5l7AhOBl2Xm+TX73zszlw1xXPYBDs/M1wCnA18s2/nK8tgAHA/8v8zcG3g+MNQ+JWm98Ws3SVpHmflwRJwDvIv2E73ZmXkfQET8Abi8XH4zUDs947zMHAR+HxF3Ak8DXgzsVTMq/gRgF2AlcG1m/rFBefsCV2bmX8syzwUOBC4aop5/KJNYyriXAXsAv44IKH6q+bfl6hkR8QFgErAZcAvFz04Px8U1CfjBwB5lOQAbl6PUvwZOK9twYWYuGGYZktQxJteSVI3/BK4HvlWzbDXlN4RRZIi1JwOuqLk/WPN4kMe+NmddOQkE8M7MvKx2RURMB5Y0qV80WT5cAVyRmf9aV/YE4MvA1My8pzypc0KTfTxyXBpsU1v/McABDUa7PxsRPwYOBWZFxMGZeevwmyJJ1XNaiCRVIDP/BpxHMWVjjbsopjkAHA6MHcGuXxURY8p52DsDtwGXAW+PiLEAEbFrRGw0xH6uAV4QEZuXJzv+K/DLEdRnFvDciHhqWfakiNiVR5PkB8o52LVXOVkE1M6LvotHj8srW5R1OXDsmgcRsXf59ymZeXNmfo5iysnTRtAOSeoIk2tJqs6pQO1VQ75GkdBeC+xH81HlVm6jSIJ/AhyTmcsp5h7PA66PiLnAVxnim8hyCsqJwC+Am4DrM/OHw61MOa3kjcD3IuJ3FMn20zLzHxTtvZliqsnsmrBvU8wXvzEiJgIfA06PiF8BAy2KexcwtTxpcx5wTLn8PeVJkzdRTMP5yXDbIUmdEpn13zhKkiRJGglHriVJkqSKmFxLkiRJFTG5liRJkipici1JkiRVxORakiRJqojJtSRJklQRk2tJkiSpIibXkiRJUkX+P5wfHYPq4R0BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit SFS: 86.304 seconds\n",
      "Scoring: f1\n",
      "Smallest feature subset (size = 20) with max. score (0.5940966496414545):\n",
      "('Name',\n",
      " 'Age in Days upon Outcome',\n",
      " 'Animal Type_Cat',\n",
      " 'Animal Type_Dog',\n",
      " 'Animal Type_Other',\n",
      " 'Sex upon Outcome_Intact Female',\n",
      " 'Sex upon Outcome_Intact Male',\n",
      " 'Sex upon Outcome_Neutered Male',\n",
      " 'Sex upon Outcome_Spayed Female',\n",
      " 'Breed_Australian Cattle Dog',\n",
      " 'Breed_Bat',\n",
      " 'Breed_Beagle',\n",
      " 'Breed_Boxer',\n",
      " 'Breed_Chihuahua',\n",
      " 'Breed_Collie',\n",
      " 'Breed_Domestic',\n",
      " 'Breed_Husky',\n",
      " 'Color_Gray',\n",
      " 'Color_Red',\n",
      " 'Color_Tricolor')\n",
      "\n",
      "Top 5 feature subsets in descending order of average score and ascending order of size:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_idx</th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>feature_names</th>\n",
       "      <th>ci_bound</th>\n",
       "      <th>std_dev</th>\n",
       "      <th>std_err</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14...</td>\n",
       "      <td>[0.8163265306122449, 0.6666666666666667, 0.511...</td>\n",
       "      <td>0.594097</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.073856</td>\n",
       "      <td>0.099441</td>\n",
       "      <td>0.033147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14...</td>\n",
       "      <td>[0.8163265306122449, 0.6666666666666667, 0.511...</td>\n",
       "      <td>0.593998</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.072797</td>\n",
       "      <td>0.098015</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14...</td>\n",
       "      <td>[0.8163265306122449, 0.6666666666666667, 0.511...</td>\n",
       "      <td>0.593998</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.072797</td>\n",
       "      <td>0.098015</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14...</td>\n",
       "      <td>[0.8163265306122449, 0.6666666666666667, 0.511...</td>\n",
       "      <td>0.593998</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.072797</td>\n",
       "      <td>0.098015</td>\n",
       "      <td>0.032672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14...</td>\n",
       "      <td>[0.8163265306122449, 0.6666666666666667, 0.523...</td>\n",
       "      <td>0.593985</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.07278</td>\n",
       "      <td>0.097993</td>\n",
       "      <td>0.032664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          feature_idx  \\\n",
       "#                                                       \n",
       "20  (0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14...   \n",
       "35  (0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14...   \n",
       "36  (0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14...   \n",
       "37  (0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14...   \n",
       "21  (0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14...   \n",
       "\n",
       "                                            cv_scores avg_score  \\\n",
       "#                                                                 \n",
       "20  [0.8163265306122449, 0.6666666666666667, 0.511...  0.594097   \n",
       "35  [0.8163265306122449, 0.6666666666666667, 0.511...  0.593998   \n",
       "36  [0.8163265306122449, 0.6666666666666667, 0.511...  0.593998   \n",
       "37  [0.8163265306122449, 0.6666666666666667, 0.511...  0.593998   \n",
       "21  [0.8163265306122449, 0.6666666666666667, 0.523...  0.593985   \n",
       "\n",
       "                                        feature_names  ci_bound   std_dev  \\\n",
       "#                                                                           \n",
       "20  (Name, Age in Days upon Outcome, Animal Type_C...  0.073856  0.099441   \n",
       "35  (Name, Age in Days upon Outcome, Animal Type_C...  0.072797  0.098015   \n",
       "36  (Name, Age in Days upon Outcome, Animal Type_C...  0.072797  0.098015   \n",
       "37  (Name, Age in Days upon Outcome, Animal Type_C...  0.072797  0.098015   \n",
       "21  (Name, Age in Days upon Outcome, Animal Type_C...   0.07278  0.097993   \n",
       "\n",
       "     std_err  \n",
       "#             \n",
       "20  0.033147  \n",
       "35  0.032672  \n",
       "36  0.032672  \n",
       "37  0.032672  \n",
       "21  0.032664  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC()\n",
    "clf_name = svm.__class__.__name__\n",
    "sfs[clf_name][\"forward\"] = sequential_fs(svm, forward=True)\n",
    "sfs[clf_name][\"forward\"][\"top5\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a0a7d70e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit SFS: 148.685 seconds\n",
      "Scoring: f1\n",
      "Smallest feature subset (size = 9) with max. score (0.5968141648660857):\n",
      "('Name',\n",
      " 'Age in Days upon Outcome',\n",
      " 'Animal Type_Other',\n",
      " 'Sex upon Outcome_Intact Female',\n",
      " 'Sex upon Outcome_Intact Male',\n",
      " 'Color_Black',\n",
      " 'Color_Brown',\n",
      " 'Color_Gray',\n",
      " 'Color_Other')\n",
      "\n",
      "Top 5 feature subsets in descending order of average score and ascending order of size:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_idx</th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>feature_names</th>\n",
       "      <th>ci_bound</th>\n",
       "      <th>std_dev</th>\n",
       "      <th>std_err</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(0, 1, 4, 5, 6, 28, 30, 31, 33)</td>\n",
       "      <td>[0.8163265306122449, 0.6666666666666667, 0.511...</td>\n",
       "      <td>0.596814</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_O...</td>\n",
       "      <td>0.072972</td>\n",
       "      <td>0.09825</td>\n",
       "      <td>0.03275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(0, 1, 4, 5, 6, 8, 28, 30, 31, 33)</td>\n",
       "      <td>[0.8163265306122449, 0.6666666666666667, 0.511...</td>\n",
       "      <td>0.596814</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_O...</td>\n",
       "      <td>0.072972</td>\n",
       "      <td>0.09825</td>\n",
       "      <td>0.03275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(0, 1, 2, 4, 5, 6, 8, 28, 30, 31, 33)</td>\n",
       "      <td>[0.8163265306122449, 0.6666666666666667, 0.511...</td>\n",
       "      <td>0.596814</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.072972</td>\n",
       "      <td>0.09825</td>\n",
       "      <td>0.03275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(0, 1, 2, 4, 5, 6, 8, 10, 28, 30, 31, 33)</td>\n",
       "      <td>[0.8163265306122449, 0.6666666666666667, 0.511...</td>\n",
       "      <td>0.596814</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.072972</td>\n",
       "      <td>0.09825</td>\n",
       "      <td>0.03275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(0, 1, 2, 4, 5, 6, 8, 10, 12, 28, 30, 31, 33)</td>\n",
       "      <td>[0.8163265306122449, 0.6666666666666667, 0.511...</td>\n",
       "      <td>0.596814</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.072972</td>\n",
       "      <td>0.09825</td>\n",
       "      <td>0.03275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      feature_idx  \\\n",
       "#                                                   \n",
       "9                 (0, 1, 4, 5, 6, 28, 30, 31, 33)   \n",
       "10             (0, 1, 4, 5, 6, 8, 28, 30, 31, 33)   \n",
       "11          (0, 1, 2, 4, 5, 6, 8, 28, 30, 31, 33)   \n",
       "12      (0, 1, 2, 4, 5, 6, 8, 10, 28, 30, 31, 33)   \n",
       "13  (0, 1, 2, 4, 5, 6, 8, 10, 12, 28, 30, 31, 33)   \n",
       "\n",
       "                                            cv_scores avg_score  \\\n",
       "#                                                                 \n",
       "9   [0.8163265306122449, 0.6666666666666667, 0.511...  0.596814   \n",
       "10  [0.8163265306122449, 0.6666666666666667, 0.511...  0.596814   \n",
       "11  [0.8163265306122449, 0.6666666666666667, 0.511...  0.596814   \n",
       "12  [0.8163265306122449, 0.6666666666666667, 0.511...  0.596814   \n",
       "13  [0.8163265306122449, 0.6666666666666667, 0.511...  0.596814   \n",
       "\n",
       "                                        feature_names  ci_bound  std_dev  \\\n",
       "#                                                                          \n",
       "9   (Name, Age in Days upon Outcome, Animal Type_O...  0.072972  0.09825   \n",
       "10  (Name, Age in Days upon Outcome, Animal Type_O...  0.072972  0.09825   \n",
       "11  (Name, Age in Days upon Outcome, Animal Type_C...  0.072972  0.09825   \n",
       "12  (Name, Age in Days upon Outcome, Animal Type_C...  0.072972  0.09825   \n",
       "13  (Name, Age in Days upon Outcome, Animal Type_C...  0.072972  0.09825   \n",
       "\n",
       "    std_err  \n",
       "#            \n",
       "9   0.03275  \n",
       "10  0.03275  \n",
       "11  0.03275  \n",
       "12  0.03275  \n",
       "13  0.03275  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfs[clf_name][\"backward\"] = sequential_fs(svm, forward=False)\n",
    "sfs[clf_name][\"backward\"][\"top5\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a53ed6",
   "metadata": {},
   "source": [
    "#### iv. k-NN (K-Nearest Neighbours) Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fcbeedfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit SFS: 32.430 seconds\n",
      "Scoring: f1\n",
      "Smallest feature subset (size = 24) with max. score (0.6277571642694647):\n",
      "('Name',\n",
      " 'Age in Days upon Outcome',\n",
      " 'Animal Type_Cat',\n",
      " 'Animal Type_Other',\n",
      " 'Sex upon Outcome_Neutered Male',\n",
      " 'Sex upon Outcome_Spayed Female',\n",
      " 'Breed_Beagle',\n",
      " 'Breed_Boxer',\n",
      " 'Breed_Chihuahua',\n",
      " 'Breed_Collie',\n",
      " 'Breed_Domestic',\n",
      " 'Breed_Hound',\n",
      " 'Breed_Husky',\n",
      " 'Breed_Poodle',\n",
      " 'Breed_Rare',\n",
      " 'Breed_Retriever',\n",
      " 'Breed_Shepherd',\n",
      " 'Breed_Terrier',\n",
      " 'Breed_Very Rare',\n",
      " 'Color_Gray',\n",
      " 'Color_Other',\n",
      " 'Color_Red',\n",
      " 'Color_Tricolor',\n",
      " 'Color_White')\n",
      "\n",
      "Top 5 feature subsets in descending order of average score and ascending order of size:\n"
     ]
    },
    {
     "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>feature_idx</th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>feature_names</th>\n",
       "      <th>ci_bound</th>\n",
       "      <th>std_dev</th>\n",
       "      <th>std_err</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(0, 1, 2, 4, 7, 8, 12, 13, 14, 15, 17, 18, 19,...</td>\n",
       "      <td>[0.7659574468085106, 0.6530612244897959, 0.622...</td>\n",
       "      <td>0.627757</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.052299</td>\n",
       "      <td>0.070416</td>\n",
       "      <td>0.023472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(0, 1, 2, 4, 7, 8, 12, 13, 14, 15, 16, 17, 18,...</td>\n",
       "      <td>[0.7346938775510203, 0.6530612244897959, 0.622...</td>\n",
       "      <td>0.626364</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.039282</td>\n",
       "      <td>0.052889</td>\n",
       "      <td>0.01763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(0, 1, 2, 4, 7, 8, 12, 13, 14, 15, 16, 17, 18,...</td>\n",
       "      <td>[0.7659574468085106, 0.6956521739130435, 0.604...</td>\n",
       "      <td>0.623596</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.052324</td>\n",
       "      <td>0.07045</td>\n",
       "      <td>0.023483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(0, 1, 2, 4, 7, 8, 18, 19, 23, 24, 27, 33, 38,...</td>\n",
       "      <td>[0.7659574468085106, 0.6808510638297872, 0.565...</td>\n",
       "      <td>0.62187</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.054764</td>\n",
       "      <td>0.073735</td>\n",
       "      <td>0.024578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(0, 1, 2, 4, 7, 8, 12, 18, 19, 23, 24, 27, 33,...</td>\n",
       "      <td>[0.7659574468085106, 0.6808510638297872, 0.565...</td>\n",
       "      <td>0.62187</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.054764</td>\n",
       "      <td>0.073735</td>\n",
       "      <td>0.024578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          feature_idx  \\\n",
       "#                                                       \n",
       "24  (0, 1, 2, 4, 7, 8, 12, 13, 14, 15, 17, 18, 19,...   \n",
       "28  (0, 1, 2, 4, 7, 8, 12, 13, 14, 15, 16, 17, 18,...   \n",
       "27  (0, 1, 2, 4, 7, 8, 12, 13, 14, 15, 16, 17, 18,...   \n",
       "14  (0, 1, 2, 4, 7, 8, 18, 19, 23, 24, 27, 33, 38,...   \n",
       "15  (0, 1, 2, 4, 7, 8, 12, 18, 19, 23, 24, 27, 33,...   \n",
       "\n",
       "                                            cv_scores avg_score  \\\n",
       "#                                                                 \n",
       "24  [0.7659574468085106, 0.6530612244897959, 0.622...  0.627757   \n",
       "28  [0.7346938775510203, 0.6530612244897959, 0.622...  0.626364   \n",
       "27  [0.7659574468085106, 0.6956521739130435, 0.604...  0.623596   \n",
       "14  [0.7659574468085106, 0.6808510638297872, 0.565...   0.62187   \n",
       "15  [0.7659574468085106, 0.6808510638297872, 0.565...   0.62187   \n",
       "\n",
       "                                        feature_names  ci_bound   std_dev  \\\n",
       "#                                                                           \n",
       "24  (Name, Age in Days upon Outcome, Animal Type_C...  0.052299  0.070416   \n",
       "28  (Name, Age in Days upon Outcome, Animal Type_C...  0.039282  0.052889   \n",
       "27  (Name, Age in Days upon Outcome, Animal Type_C...  0.052324   0.07045   \n",
       "14  (Name, Age in Days upon Outcome, Animal Type_C...  0.054764  0.073735   \n",
       "15  (Name, Age in Days upon Outcome, Animal Type_C...  0.054764  0.073735   \n",
       "\n",
       "     std_err  \n",
       "#             \n",
       "24  0.023472  \n",
       "28   0.01763  \n",
       "27  0.023483  \n",
       "14  0.024578  \n",
       "15  0.024578  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier() \n",
    "clf_name = knn.__class__.__name__\n",
    "sfs[clf_name][\"forward\"] = sequential_fs(knn, forward=True)\n",
    "sfs[clf_name][\"forward\"][\"top5\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7f4aad12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit SFS: 46.430 seconds\n",
      "Scoring: f1\n",
      "Smallest feature subset (size = 21) with max. score (0.6235119541220221):\n",
      "('Name',\n",
      " 'Age in Days upon Outcome',\n",
      " 'Animal Type_Other',\n",
      " 'Sex upon Outcome_Intact Female',\n",
      " 'Sex upon Outcome_Neutered Male',\n",
      " 'Sex upon Outcome_Spayed Female',\n",
      " 'Sex upon Outcome_Unknown',\n",
      " 'Breed_Australian Cattle Dog',\n",
      " 'Breed_Bat',\n",
      " 'Breed_Beagle',\n",
      " 'Breed_Chihuahua',\n",
      " 'Breed_Dachshund',\n",
      " 'Breed_Domestic',\n",
      " 'Breed_Husky',\n",
      " 'Breed_Pit Bull',\n",
      " 'Breed_Retriever',\n",
      " 'Breed_Siamese',\n",
      " 'Color_Brown',\n",
      " 'Color_Gray',\n",
      " 'Color_Orange',\n",
      " 'Color_Tricolor')\n",
      "\n",
      "Top 5 feature subsets in descending order of average score and ascending order of size:\n"
     ]
    },
    {
     "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>feature_idx</th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>feature_names</th>\n",
       "      <th>ci_bound</th>\n",
       "      <th>std_dev</th>\n",
       "      <th>std_err</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(0, 1, 4, 5, 7, 8, 9, 10, 11, 12, 14, 16, 17, ...</td>\n",
       "      <td>[0.7999999999999999, 0.7450980392156864, 0.523...</td>\n",
       "      <td>0.623512</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_O...</td>\n",
       "      <td>0.066884</td>\n",
       "      <td>0.090054</td>\n",
       "      <td>0.030018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(0, 1, 4, 5, 7, 8, 9, 10, 11, 12, 14, 16, 17, ...</td>\n",
       "      <td>[0.7843137254901961, 0.76, 0.5238095238095237,...</td>\n",
       "      <td>0.622849</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_O...</td>\n",
       "      <td>0.067032</td>\n",
       "      <td>0.090253</td>\n",
       "      <td>0.030084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
       "      <td>[0.7999999999999999, 0.6938775510204083, 0.487...</td>\n",
       "      <td>0.622786</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_C...</td>\n",
       "      <td>0.069049</td>\n",
       "      <td>0.092969</td>\n",
       "      <td>0.03099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(0, 1, 4, 7, 8, 9, 11, 14, 17, 20, 23, 25, 32)</td>\n",
       "      <td>[0.7843137254901961, 0.6938775510204083, 0.536...</td>\n",
       "      <td>0.6226</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_O...</td>\n",
       "      <td>0.059476</td>\n",
       "      <td>0.080079</td>\n",
       "      <td>0.026693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(0, 1, 4, 7, 8, 9, 11, 14, 16, 17, 20, 23, 25,...</td>\n",
       "      <td>[0.7843137254901961, 0.6938775510204083, 0.536...</td>\n",
       "      <td>0.6226</td>\n",
       "      <td>(Name, Age in Days upon Outcome, Animal Type_O...</td>\n",
       "      <td>0.059476</td>\n",
       "      <td>0.080079</td>\n",
       "      <td>0.026693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          feature_idx  \\\n",
       "#                                                       \n",
       "21  (0, 1, 4, 5, 7, 8, 9, 10, 11, 12, 14, 16, 17, ...   \n",
       "23  (0, 1, 4, 5, 7, 8, 9, 10, 11, 12, 14, 16, 17, ...   \n",
       "31  (0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14...   \n",
       "13     (0, 1, 4, 7, 8, 9, 11, 14, 17, 20, 23, 25, 32)   \n",
       "14  (0, 1, 4, 7, 8, 9, 11, 14, 16, 17, 20, 23, 25,...   \n",
       "\n",
       "                                            cv_scores avg_score  \\\n",
       "#                                                                 \n",
       "21  [0.7999999999999999, 0.7450980392156864, 0.523...  0.623512   \n",
       "23  [0.7843137254901961, 0.76, 0.5238095238095237,...  0.622849   \n",
       "31  [0.7999999999999999, 0.6938775510204083, 0.487...  0.622786   \n",
       "13  [0.7843137254901961, 0.6938775510204083, 0.536...    0.6226   \n",
       "14  [0.7843137254901961, 0.6938775510204083, 0.536...    0.6226   \n",
       "\n",
       "                                        feature_names  ci_bound   std_dev  \\\n",
       "#                                                                           \n",
       "21  (Name, Age in Days upon Outcome, Animal Type_O...  0.066884  0.090054   \n",
       "23  (Name, Age in Days upon Outcome, Animal Type_O...  0.067032  0.090253   \n",
       "31  (Name, Age in Days upon Outcome, Animal Type_C...  0.069049  0.092969   \n",
       "13  (Name, Age in Days upon Outcome, Animal Type_O...  0.059476  0.080079   \n",
       "14  (Name, Age in Days upon Outcome, Animal Type_O...  0.059476  0.080079   \n",
       "\n",
       "     std_err  \n",
       "#             \n",
       "21  0.030018  \n",
       "23  0.030084  \n",
       "31   0.03099  \n",
       "13  0.026693  \n",
       "14  0.026693  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfs[clf_name][\"backward\"] = sequential_fs(knn, forward=False)\n",
    "sfs[clf_name][\"backward\"][\"top5\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d8a578f",
   "metadata": {},
   "source": [
    "### 6. Insights\n",
    "\n",
    "Optimal feature subset using filter technique based on mutual information criterion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "83a5e057",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Animal Type_Other',\n",
       " 'Sex upon Outcome_Unknown',\n",
       " 'Breed_Bat',\n",
       " 'Name',\n",
       " 'Sex upon Outcome_Spayed Female',\n",
       " 'Animal Type_Dog',\n",
       " 'Breed_Poodle',\n",
       " 'Sex upon Outcome_Neutered Male',\n",
       " 'Breed_Shepherd',\n",
       " 'Animal Type_Cat']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter_subset = top_10_features_filter\n",
    "filter_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c21385e",
   "metadata": {},
   "source": [
    "For the sequential search wrapper technique, we can select the better of forward and backward SFS average scores for each of the 4 classifiers.\n",
    "\n",
    "- It is possible that forward and backward SFS sometimes find the same feature subset.\n",
    "- Although not always the case, backward SFS (more computationally expensive) yields better results than the forward counterpart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a5b991f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier\n",
      "\n",
      "Forward SFS avg. score: 0.613577131139243\n",
      "Backward SFS avg. score: 0.6244344171822142\n",
      "Better: backward\n",
      "-----\n",
      "\n",
      "GaussianNB\n",
      "\n",
      "Forward SFS avg. score: 0.5640204124324624\n",
      "Backward SFS avg. score: 0.5640204124324624\n",
      "Better: forward\n",
      "-----\n",
      "\n",
      "SVC\n",
      "\n",
      "Forward SFS avg. score: 0.5940966496414545\n",
      "Backward SFS avg. score: 0.5968141648660857\n",
      "Better: backward\n",
      "-----\n",
      "\n",
      "KNeighborsClassifier\n",
      "\n",
      "Forward SFS avg. score: 0.6277571642694647\n",
      "Backward SFS avg. score: 0.6235119541220221\n",
      "Better: forward\n",
      "-----\n",
      "\n"
     ]
    }
   ],
   "source": [
    "wrapper_subsets = {}\n",
    "\n",
    "for clf_name in sfs:\n",
    "    print(f\"{clf_name}\\n\")\n",
    "    f_score = sfs[clf_name][\"forward\"][\"avg_score\"]\n",
    "    b_score = sfs[clf_name][\"backward\"][\"avg_score\"]\n",
    "    print(f\"Forward SFS avg. score: {f_score}\")\n",
    "    print(f\"Backward SFS avg. score: {b_score}\")\n",
    "    \n",
    "    better = \"forward\" if f_score >= b_score else \"backward\"\n",
    "    wrapper_subsets[clf_name] = sfs[clf_name][better][\"subset\"]\n",
    "    print(f\"Better: {better}\\n-----\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0892a807",
   "metadata": {},
   "source": [
    "- As expected, feature selection with filters is much faster than wrappers.\n",
    "- On average, sequential search (wrapper) using SVM takes the longest time of all the classifiers.\n",
    "- Decision Tree and k-NN classifiers yield very similar average scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1b9a1722",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'DecisionTreeClassifier': ['Name',\n",
      "                            'Age in Days upon Outcome',\n",
      "                            'Animal Type_Other',\n",
      "                            'Sex upon Outcome_Intact Female',\n",
      "                            'Sex upon Outcome_Neutered Male',\n",
      "                            'Sex upon Outcome_Spayed Female',\n",
      "                            'Sex upon Outcome_Unknown',\n",
      "                            'Breed_Boxer',\n",
      "                            'Breed_Chihuahua',\n",
      "                            'Breed_Pit Bull',\n",
      "                            'Breed_Rare',\n",
      "                            'Breed_Very Rare',\n",
      "                            'Color_Blue',\n",
      "                            'Color_Brown',\n",
      "                            'Color_Gray',\n",
      "                            'Color_Orange',\n",
      "                            'Color_Yellow'],\n",
      " 'GaussianNB': ['Animal Type_Other', 'Breed_Bat', 'Color_Red'],\n",
      " 'KNeighborsClassifier': ['Name',\n",
      "                          'Age in Days upon Outcome',\n",
      "                          'Animal Type_Cat',\n",
      "                          'Animal Type_Other',\n",
      "                          'Sex upon Outcome_Neutered Male',\n",
      "                          'Sex upon Outcome_Spayed Female',\n",
      "                          'Breed_Beagle',\n",
      "                          'Breed_Boxer',\n",
      "                          'Breed_Chihuahua',\n",
      "                          'Breed_Collie',\n",
      "                          'Breed_Domestic',\n",
      "                          'Breed_Hound',\n",
      "                          'Breed_Husky',\n",
      "                          'Breed_Poodle',\n",
      "                          'Breed_Rare',\n",
      "                          'Breed_Retriever',\n",
      "                          'Breed_Shepherd',\n",
      "                          'Breed_Terrier',\n",
      "                          'Breed_Very Rare',\n",
      "                          'Color_Gray',\n",
      "                          'Color_Other',\n",
      "                          'Color_Red',\n",
      "                          'Color_Tricolor',\n",
      "                          'Color_White'],\n",
      " 'SVC': ['Name',\n",
      "         'Age in Days upon Outcome',\n",
      "         'Animal Type_Other',\n",
      "         'Sex upon Outcome_Intact Female',\n",
      "         'Sex upon Outcome_Intact Male',\n",
      "         'Color_Black',\n",
      "         'Color_Brown',\n",
      "         'Color_Gray',\n",
      "         'Color_Other']}\n"
     ]
    }
   ],
   "source": [
    "pprint(wrapper_subsets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f63cc555",
   "metadata": {},
   "source": [
    "We can count the frequency of occurrence of different features in the subsets returned by filter and wrapper techniques:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f42cff52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Animal Type_Other', 5),\n",
       " ('Name', 4),\n",
       " ('Sex upon Outcome_Spayed Female', 3),\n",
       " ('Sex upon Outcome_Neutered Male', 3),\n",
       " ('Age in Days upon Outcome', 3),\n",
       " ('Color_Gray', 3),\n",
       " ('Sex upon Outcome_Unknown', 2),\n",
       " ('Breed_Bat', 2),\n",
       " ('Breed_Poodle', 2),\n",
       " ('Breed_Shepherd', 2),\n",
       " ('Animal Type_Cat', 2),\n",
       " ('Sex upon Outcome_Intact Female', 2),\n",
       " ('Breed_Boxer', 2),\n",
       " ('Breed_Chihuahua', 2),\n",
       " ('Breed_Rare', 2),\n",
       " ('Breed_Very Rare', 2),\n",
       " ('Color_Brown', 2),\n",
       " ('Color_Red', 2),\n",
       " ('Color_Other', 2),\n",
       " ('Animal Type_Dog', 1)]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq = Counter(list(itertools.chain(filter_subset, *wrapper_subsets.values())))\n",
    "freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
    "freq[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb8f7512",
   "metadata": {},
   "source": [
    "- Based on the mutual information (entropy based) criterion, the filter technique suggests that `'Animal Type_Other', 'Sex upon Outcome_Unknown', 'Breed_Bat', 'Name'` are some of the most discriminatory features. These features also appear frequently in some wrapper technique subsets.\n",
    "- We can compute the percentage of negative outcomes for different `Animal Type`, `Sex upon Outcome` and `Breed` categories and look for explanations for the above observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3c094dc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage negative outcome (1) by Animal Type:\n",
      "\n",
      "3.40%\t| Animal Type_Dog\n",
      "5.19%\t| Animal Type_Cat\n",
      "70.81%\t| Animal Type_Other\n"
     ]
    }
   ],
   "source": [
    "def neg_outcome_perc(feature: str) -> None:\n",
    "    seen = {}\n",
    "    print(f\"Percentage negative outcome (1) by {feature}:\\n\")\n",
    "    for cat in [col for col in df.columns if f\"{feature}_\" in col]:\n",
    "        counts = y_train[X_train[X_train[cat] == 1].index].value_counts()\n",
    "        num = counts.loc[1] if 1 in counts.index else 0\n",
    "        seen[cat] = 100 * num/(sum(counts))\n",
    "    for k, v in sorted(seen.items(), key=lambda x: x[1]):\n",
    "        print(f\"{v:.2f}%\\t| {k}\")\n",
    "    \n",
    "        \n",
    "neg_outcome_perc(\"Animal Type\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d2573e",
   "metadata": {},
   "source": [
    "- As expected, dogs and cats are far more common as pets (likely to be adopted/returned to owner), and hence have a negligible chance of negative outcome (class = 1) compared to other types of animals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e3b79a2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage negative outcome (1) by Breed:\n",
      "\n",
      "0.00%\t| Breed_Dachshund\n",
      "0.00%\t| Breed_Husky\n",
      "1.45%\t| Breed_Retriever\n",
      "1.87%\t| Breed_Chihuahua\n",
      "2.04%\t| Breed_Collie\n",
      "2.33%\t| Breed_Beagle\n",
      "2.34%\t| Breed_Terrier\n",
      "2.70%\t| Breed_Hound\n",
      "3.85%\t| Breed_Boxer\n",
      "5.00%\t| Breed_Poodle\n",
      "5.13%\t| Breed_Siamese\n",
      "5.15%\t| Breed_Domestic\n",
      "5.32%\t| Breed_Shepherd\n",
      "6.85%\t| Breed_Australian Cattle Dog\n",
      "7.88%\t| Breed_Pit Bull\n",
      "11.68%\t| Breed_Very Rare\n",
      "11.89%\t| Breed_Rare\n",
      "100.00%\t| Breed_Bat\n"
     ]
    }
   ],
   "source": [
    "neg_outcome_perc(\"Breed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6858a245",
   "metadata": {},
   "source": [
    "- In the given dataset, bats are noted as `Animal Type_Other` with `Breed_Bat`. 100% of bats at the animal shelter are shown to face a negative outcome.\n",
    "- In Texas, bats, by law, are defined as high-risk animals for rabies and touching a bat for any reason is not allowed (Animal Control must be called if a bat is found).\n",
    "- Furthermore, domestic animals exposed to a rabid bat require a series of rabies vaccination boosters and a quarantine period, and in some instances euthanasia if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7aa12095",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage negative outcome (1) by Sex upon Outcome:\n",
      "\n",
      "1.62%\t| Sex upon Outcome_Spayed Female\n",
      "2.88%\t| Sex upon Outcome_Neutered Male\n",
      "8.18%\t| Sex upon Outcome_Intact Female\n",
      "9.32%\t| Sex upon Outcome_Intact Male\n",
      "50.00%\t| Sex upon Outcome_Unknown\n"
     ]
    }
   ],
   "source": [
    "neg_outcome_perc(\"Sex upon Outcome\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec61379",
   "metadata": {},
   "source": [
    "- Animals with a recorded negative outcome are far more likely to have sex marked as unknown, and the importance of this feature is highlighted by feature selection techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "88e63123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2536\n",
       "1      62\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Name is present\n",
    "y_train[X_train[X_train[\"Name\"] == 1].index].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8873e54c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    935\n",
       "1    215\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Name is absent\n",
    "y_train[X_train[X_train[\"Name\"] == 0].index].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f37225b",
   "metadata": {},
   "source": [
    "- With regards to `Name`, it appears that an animal with a name is far more likely to have a positive outcome (2536:62) than without (935:215). This is plausible, since a named animal has a high chance of being a pet which can be returned to its owner.\n",
    "- Actions to improve the adoption/return rate of animals:\n",
    "  - Consider putting up fewer animals of similar kind up for adoption at any given time. When given too many choices, it makes it difficult for adopters to make a decision.\n",
    "  - Groom the animals to make them more appealing, since appearance plays an important role in influencing a positive outcome."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21088ddf",
   "metadata": {},
   "source": [
    "## 1C: Modelling and Evaluation\n",
    "\n",
    "With the discriminating features selected, we can proceed with model building and evaluation. The cleaned dataset had already been split into training (75%) and testing (25%) sets before the feature selection process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "81aa8537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3471\n",
       "1     277\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c68f084",
   "metadata": {},
   "source": [
    "- The training set target class only has 277 instances of negative outcome (minority class = 1) and 3471 positives (majority class = 0).\n",
    "- For such kinds of data, it doesn't make sense to rely on classification accuracy/error as a metric, as the model will be biased towards the majority class.\n",
    "- A *confusion matrix* summarises every classifier's performance by revealing the True Positives, True Negatives, False Positives and False Negatives. Some measures based on these values include: precision, recall (sensitivity), specificity (True Negative rate), etc.\n",
    "- As described earlier, the primary metric to be used for scoring here is **`f1`** due to the **high imbalance in the target class**.\n",
    "- The F1 measure is the harmonic mean of precision and recall. In the given scenario, the **cost of False Positive** (predicted class = 1, actual class = 0) is greater than that of False Negative (predicted = 0, actual = 1), since it is not desirable to predict a bad outcome (death) for an animal that could be adopted/returned to owner.\n",
    "- When working on problems with imbalanced datasets where detecting positives is more important than detecting negatives, F1 is more suitable than other balanced evaluation measures like balanced accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e884f13d",
   "metadata": {},
   "source": [
    "Below, we store the different training and test sets for the 4 classifiers based on the highest scoring feature subsets selected by the wrapper technique (sequential search). Only the appropriate feature subsets will be used for different classifiers during hyperparameter optimisation and model evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "681ef4ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sub, X_test_sub = {}, {}\n",
    "for clf in [dt, gnb, svm, knn]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    X_train_sub[clf_name] = X_train[wrapper_subsets[clf_name]]\n",
    "    X_test_sub[clf_name] = X_test[wrapper_subsets[clf_name]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63a0a018",
   "metadata": {},
   "source": [
    "### 1. Classifier/hyperparameter tuning\n",
    "\n",
    "The \"best\" arguments (called hyperparameters) used when training classifiers can be found using a systematic trial and error approach. All searches use a 10-fold **cross-validation** where in each of 10 experiments, 9 \"folds\" are used for training and 1 for testing. This is preferable to a naive hold-out strategy as it increases the generalisation capabilities of a model by reducing the chances of overfitting.\n",
    "\n",
    "During the entire process of tuning hyperparameters, no access to the testing data set is granted. Model parameters are optimised solely using the training set (the split was done after encoding categorical features and before applying filter/wrapper feature selection).\n",
    "\n",
    "#### i. Decision Tree\n",
    "\n",
    "- `max_depth` refers to the maximum depth of the tree.\n",
    "- `min_samples_leaf`  is minimum number of samples required to be at a leaf node.\n",
    "- `GridSearchCV` is used here to optimise the Decision Tree hyperparameters using a stratified (by default) 10-fold cross-validated grid-search over a parameter grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "16502496",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: DecisionTreeClassifier(criterion='entropy')\n",
      "Average F1: 0.615\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    \"max_depth\": [None, 2, 5, 7],\n",
    "    \"min_samples_leaf\": [1, 2, 3, 5, 10]\n",
    "}\n",
    "clf_name = dt.__class__.__name__\n",
    "dt_grid = GridSearchCV(dt, param_grid, scoring=\"f1\", cv=10)\n",
    "dt_grid.fit(X_train_sub[clf_name], y_train)\n",
    "dt_best = dt_grid.best_estimator_\n",
    "\n",
    "print(f\"Best: {dt_best}\")\n",
    "print(f\"Average F1: {dt_grid.best_score_:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e628d23",
   "metadata": {},
   "source": [
    "#### ii. Naive Bayes\n",
    "\n",
    "3 different Naive Bayes algorithms can be tried:\n",
    "- Gaussian: standard Naive Bayes where the likelihood of the features is assumed to be Gaussian.\n",
    "- Bernoulli: assumes that the data that is distributed according to multivariate Bernoulli distributions.\n",
    "- Multinomial: naive Bayes algorithm for multinomially distributed data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "64c35639",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_strat = StratifiedKFold(n_splits=10, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "163a4545",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB()\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "Average F1: 0.564 +/- 0.095\n",
      "-----\n",
      "BernoulliNB()\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "Average F1: 0.564 +/- 0.095\n",
      "-----\n",
      "MultinomialNB()\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "Average F1: 0.414 +/- 0.061\n",
      "-----\n",
      "Best: GaussianNB()\n"
     ]
    }
   ],
   "source": [
    "nb_best, max_score = None, 0\n",
    "clf_name = gnb.__class__.__name__\n",
    "for nb in [GaussianNB(), BernoulliNB(), MultinomialNB()]:\n",
    "    scores = cross_val_score(nb, X_train_sub[clf_name], y_train, cv=cv_strat, scoring=\"f1\", n_jobs=-1)\n",
    "    mean = scores.mean()\n",
    "    if mean > max_score:\n",
    "        max_score = mean\n",
    "        nb_best = nb\n",
    "    print(f\"{nb}\\n{cv_strat}\\nAverage F1: {mean:.3f} +/- {scores.std():.3f}\\n-----\")\n",
    "print(f\"Best: {nb_best}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b554b17b",
   "metadata": {},
   "source": [
    "#### iii. SVM\n",
    "\n",
    "We can experiment with different types of kernel functions which are used to transform the data without actually applying them (\"kernel trick\"):\n",
    "- `rbf`: Radial Basis Function\n",
    "- `linear`: `x.x'`.\n",
    "- `sigmoid`: `tanh(1 + x.x')`\n",
    "- `poly`: polynomial `(1 + x.x')^t`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3dc1e7f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC()\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "Average F1: 0.582 +/- 0.086\n",
      "-----\n",
      "SVC(kernel='linear')\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "Average F1: 0.559 +/- 0.090\n",
      "-----\n",
      "SVC(kernel='sigmoid')\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "Average F1: 0.319 +/- 0.127\n",
      "-----\n",
      "SVC(kernel='poly')\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "Average F1: 0.588 +/- 0.102\n",
      "-----\n",
      "Best: SVC(kernel='poly')\n"
     ]
    }
   ],
   "source": [
    "svm_best, max_score = None, 0\n",
    "clf_name = svm.__class__.__name__\n",
    "for kernel in [\"rbf\", \"linear\", \"sigmoid\", \"poly\"]:\n",
    "    clf = SVC(kernel=kernel)\n",
    "    scores = cross_val_score(clf, X_train_sub[clf_name], y_train, cv=cv_strat, scoring=\"f1\", n_jobs=-1)\n",
    "    mean = scores.mean()\n",
    "    if mean > max_score:\n",
    "        max_score = mean\n",
    "        svm_best = clf\n",
    "    print(f\"{clf}\\n{cv_strat}\\nAverage F1: {mean:.3f} +/- {scores.std():.3f}\\n-----\")\n",
    "print(f\"Best: {svm_best}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0c8af47",
   "metadata": {},
   "source": [
    "#### iv. k-NN\n",
    "\n",
    "A number of different nearest neighbour values can be tried:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "bbd57b4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier(n_neighbors=2)\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "Average F1: 0.574 +/- 0.095\n",
      "-----\n",
      "KNeighborsClassifier()\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "Average F1: 0.596 +/- 0.096\n",
      "-----\n",
      "KNeighborsClassifier(n_neighbors=10)\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "Average F1: 0.578 +/- 0.098\n",
      "-----\n",
      "KNeighborsClassifier(n_neighbors=12)\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "Average F1: 0.578 +/- 0.103\n",
      "-----\n",
      "KNeighborsClassifier(n_neighbors=15)\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "Average F1: 0.597 +/- 0.094\n",
      "-----\n",
      "Best: KNeighborsClassifier(n_neighbors=15)\n"
     ]
    }
   ],
   "source": [
    "knn_best, max_score = None, 0\n",
    "clf_name = knn.__class__.__name__\n",
    "for n in [2, 5, 10, 12, 15]:\n",
    "    clf = KNeighborsClassifier(n_neighbors=n)\n",
    "    ts = cross_val_score(clf, X_train_sub[clf_name], y_train, cv=cv_strat, scoring=\"f1\", n_jobs=-1)\n",
    "    mean = ts.mean()\n",
    "    if mean > max_score:\n",
    "        max_score = mean\n",
    "        knn_best = clf\n",
    "    print(f\"{clf}\\n{cv_strat}\\nAverage F1: {mean:.3f} +/- {ts.std():.3f}\\n-----\")\n",
    "print(f\"Best: {knn_best}\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8343dd3",
   "metadata": {},
   "source": [
    "### 2. Evaluation of best classifiers\n",
    "\n",
    "Based on the parameter setting optimisation above, the best classifiers can now be evaluated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d0e6d25d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[DecisionTreeClassifier(criterion='entropy'),\n",
       " GaussianNB(),\n",
       " SVC(kernel='poly'),\n",
       " KNeighborsClassifier(n_neighbors=15)]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_clfs = [dt_best, nb_best, svm_best, knn_best]\n",
    "best_clfs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80e941d4",
   "metadata": {},
   "source": [
    "First, we can plot confusion matrices for each of the classifiers for preliminary evaluation, finally using the test data set for the first time to check the performance on unseen data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "958dc8c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TJd1LNfmn9fyog70mewJX1u/LV4B9s5pRvTzPc6j3bVQlFskamp8TS217XHxO1M91p7od5wL3UxW9qwLvqFf7Xt3W24CreOKkti9RndnjTqoxwa2T+laj6gm+u97GfKr5KFB9vtwU1RCOt1N9PrS37yqqwva8evtPB/4yxMsymK8wcDH+y5bPyqOoJj4ONI593Og0t5sussCfEZQ0fm39jKn5g1+1z4MZnu02nn1JZm4/yk2SJA2i09xuemYPNulBksbckh4JSdL4UGpuWyRLapQk6GvmSDBJ0gBKzW2LZEmN05/l9UhIUslKzG2LZEmNUuphO0kqVam53agieeaMybnJhiv0uhnqsmsvW2nolTSuPcxCFuUj5SWmlmJmTwxmdvnM7IE1qkjeZMMVuPCMDXvdDHXZHk/artdNUJddkGeP4NFBX5Y3tq1EZvbEYGaXb2SZDaXmdqOKZElKoL/ACSCSVKpSc9siWVLjlDi2TZJKVmJuWyRLapTMMg/bSVKpSs1ti2RJjdNfYI+EJJWsxNy2SJbUKNWphMrrkZCkUpWa2+U9I0mSJGmELJIlNUw1tq2TiySpFzrL7SG3GvGdiJgbEVe0LJsREb+LiOvqf9dsue+IiLg+Iq6JiD1alj87Ii6v7zsmIoY1NsRPFUmNsuRUQp1cJEljr9PcHobjgT3blh0OnJ2ZmwNn17eJiK2AfYGt68ccGxGT68d8AzgY2Ly+tG9zQH6qSGqcvoyOLkPpda+EJJWqG5mdmX8CFrQt3hs4ob5+AvCqluWnZOYjmXkjcD2wQ0TMAlbLzPMyM4HvtTxmUBbJkholCfqY1NFlGI6nh70SklSiTnO7Q+tm5hyA+t916uXrA7e2rDe7XrZ+fb19+ZAskiU1Tn9O6ugylF73SkhSqTrM7JkRcXHL5eARNGGgrukcZPmQPAWcpEbpwamEluqViIjWXonzW9Zb0vvwKB32SkhSiUaQ2/Myc/vlfMydETGrzutZwNx6+Wxgw5b1NgBur5dvMMDyIdmTLKkkje6VkCSN2GnAgfX1A4FftCzfNyKmRcSmVEPhLqw7Qe6PiJ3q+SNvannMoOxJltQoyfAmdCxDo3slJKlEI8ztZYqIk4FdqTpAZgNHAp8BTo2Ig4BbgH0AMvPKiDgVuApYDBySmX31pt5BNSdlOvCb+jIki2RJjTPGp3Nb0ivxGZ7YK/GDiPgi8CQe75Xoi4j7I2In4AKqXomvjmWDJalpupHbmfmGZdy12zLWPwo4aoDlFwPbLO/+LZIlNUomXfthkF73SkhSibqZ271kkSypYYL+AYf9jlyveyUkqUzdy+1eskiW1ChJmT0SklSqUnPbIllS44zxKeAkSSNUYm5bJEtqlCTo78IsaUlSd5Sa2+WV/ZIkSdII2ZMsqXFKPGwnSSUrMbctkiU1SgL9BU4AkaRSlZrbFsmSGiboK/BUQpJUrjJz2yJZUqOU2iMhSaUqNbctkiU1Tok9EpJUshJz2yJZUqNkRpE9EpJUqlJzu7xnJEmSJI2QPcmSGqfEnzeVpJKVmNsWyZIaJYH+Ase2SVKpSs1ti2RJDRNF9khIUrnKzG2LZEmNUp1KqLweCUkqVam5bZEsqXFK/HlTSSpZibltkSypUZIoskdCkkpVam6XV/ZLkiRJI2RPsqTG6ff7uySNKyXmtkWypEbJhL4CD9tJUqlKzW2LZEmNU+LYNkkqWYm5bZEsqVGqCSDlHbaTpFKVmtsWyZIap6/AX26SpJKVmNsWyZIapdST0ktSqUrNbYtkSQ1T5mE7SSpXmbld3jOSJEmSRsieZEmN01/g2DZJKlmJuW2RLKlRSj3fpiSVqtTctkiW1Dgljm2TpJKVmNsWyZIapTrfZnk9EpJUqlJz2yJZUuOUOLZNkkpWYm5bJEtqlFLPtylJpSo1t8sbQCJJkiSNkD3JkhqnxAkgklSyEnPbIllSs2SZE0AkqViF5rZFsqRGScqcACJJpSo1ty2SJTVOiT0SklSyEnPbIllSo5Q6S1qSSlVqblskj7Kj37chF5y1GmvMXMxxf7gGgD/9cnVOPHo9br1uRY45/Vq22PYhAO64dSr/8YKnssFmjwDw1Gcv5D2fnQ3AR/bbjAVzV6BvMWyz40Le9anZTJ7cm+ekzp1wwVU89MBk+vuhb3Hw7r226HWTxoUSw1bNtDyZvcTc2SvwH7s+lTcedgf7vOMuAB5dFHz9o+tz2XmrEAFvPnwOu7zs3jF/Plo+7//iLey4+/3cM28Kb3vRlgC89b9uZ6cX38eji4I5N0/l6PdtxML7/AAeSom53dWpiBGxZ0RcExHXR8Th3dxXU7zk9Qs46vs3LLVsk6c+zMe+dRNP32nhE9aftfEjfOOsa/jGWdc8ViADfPT/buJ/z7qG4/5wDffOn8Kff7lGt5uuLvnQPk/mnS/e0gJZjWdmVwbLbID//fj6POdF9y+17OSvrMsaMxfznXP/yTf/+E+esdMDXWuzRs+ZP5zBR/ffdKlll/5pVQ5+4Za8Y/ctue2Gaez77jt71Dr1Wtd6kiNiMvB14MXAbOCiiDgtM6/q1j6b4Ok7LeSOW6cutWyjzR9Z7u2svGo/AH2LYfGioMDx8NKASv1506Yzsx83WGb/9TerM2ujRay4Uv9Sy884ZQbf/vM/AZg0CVZfq2/0G6tRd8UFq7DuBouWWnbpH1d97PrVl6zMLi+/Z4xbNf6Umtvd7EneAbg+M2/IzEXAKcDeXdzfuHTHLVN554u34AP/7ylcfsHKS933kTdsxuufsQ3TV+n3P+l4lcGnTr6Br/32Wvbaf36vWzNu9BMdXTQiZvYQHn5wEqceuw5vPOyOpZY/cG91KP6Ez63HIS/Zgk8evAl33+VoxhLs8YYFXPT71XrdjHGhxMzuZpG8PnBry+3Z9bKlRMTBEXFxRFx81/yJ9c17xjqPctJFV3Hs767lbR+/jc+8c2MW3v/4W/Kpk2/g5L9dyaOLgr+fu0oPW6pOvW/vp/CuPbbgo/tvyivfPI9tdvQQ7JCyGtvWyWU4IuJ9EXFlRFwRESdHxIoRMSMifhcR19X/rtmy/hH18INrImKPrj3v3jOzh/C9z6/Hq//jLqavvHQvct9imDdnKls9ZyFfP/NanvbshXzzE0/qUSs1Wt5w6J30LYbf/3SNXjel+TrM7eHoZWZ3s0ge6NnnExZkHpeZ22fm9muvNbEGxk+dlqw2o/qQ2fwZD/GkTRZx2w3Tll5nxWTnl9zLeWes3osmaoQW3LkCAPfOX4G//HZ1nvrMB3vcouZbMku6G4EbEesDhwLbZ+Y2wGRgX+Bw4OzM3Bw4u75NRGxV3781sCdwbD0soURm9hD++beV+PYnn8SbdtiKn31rbU756rr84jszWW1GH9Om9/G8vaqJeru8/B6uu3x6j1urkdh9nwXssPt9fPZdG+N4x6F1mttD6XVmd7NIng1s2HJ7A+D2Lu5v3Lln/mT66o6YOTdP5bYbp7LeRot4aOEk5t9ZHarrWwwXnr0aGz5l+cc1q7emTe9j+sp9j11/9gvu56Z/rtjjVo0P3exJppqLMT0ipgArUeXS3sAJ9f0nAK+qr+8NnJKZj2TmjcD1VMMSSmRmD+GLP7+e7114Fd+78Cpe/da72Pfdd7L3v88jAnZ68X1c9tfqiN/fz12Vjbcws8er7Xe9j9cdMpePv3lTHnmovJ9a7pYSM7ubg6YuAjaPiE2B26gq+/26uL9G+PQ7Nuay81bh3gVT2P/ZW3HAYXew6pp9HPuf63Pv/Cn81wGb8eStH+JTJ9/A5eevwvc+vx6Tp8DkScmhn5nNamv2cfddU/j4mzfj0UVBXx9s97wHePmb5vX6qWk5rbn2Yo789k0ATJ6S/OFna3LxOY5tG0o3J4Bk5m0R8QXgFuAh4MzMPDMi1s3MOfU6cyJinfoh6wPnt2xiwCEIhTCzh8jswRz0n7fzuXdvzP8eOZnV11rMYV+8ZYyegUbi8GNv5hk7P8DqMxZz0sVXceLR67Lvu+aywrTk0z/8FwD/vGRljjl8gx63tNlGkNszI+LiltvHZeZxj223x5ndtSI5MxdHxLuAM6i6x7+TmVd2a39NccQ3bh5w+ZLDcK12edm9A55Hc821F/PV31w76m3T2Lrjlmm848Vb9roZ41J2XiQPGrj1uLW9gU2Be4AfRcQbB9nesIYglMDMXtpAmd3qgA8sPXlv3Q0e5eifXT9q7dLY+Mw7N37CsjNOXqsHLRn/OszteZm5/bLu7HVmd3X6bWaeDpzezX1IUotBAxfYHbgxM+8CiIifAs8F7oyIWXWPxCxgbr3+hBqCYGZLapieZraDbSQ1ThdPAXcLsFNErBQRAewGXA2cBhxYr3Mg8Iv6+mnAvhExrR6GsDlw4ag+WUkqQImZ7YkcJTVKZvd+3jQzL4iIHwOXAouBvwHHAasAp0bEQVShvE+9/pURcSpwVb3+IZk5sc57JklD6FZu9zqzLZIlNc4IxiQPY9t5JHBk2+JHqHooBlr/KOCorjVIkgrQrdzuZWZbJEtqmDJ/3lSSylVmblskS2qcbvYkS5JGX4m5bZEsqVGW/HKTJGl8KDW3PbuFJEmS1MaeZEnNktVMaUnSOFFoblskS2qcYZ4/U5LUECXmtkWypEZJypwAIkmlKjW3LZIlNUyZpxKSpHKVmdsWyZIap8SxbZJUshJz2yJZUuOUeNhOkkpWYm57CjhJkiSpjT3Jkhols8weCUkqVam5bZEsqXFKnAAiSSUrMbctkiU1TokTQCSpZCXmtkWypMYp8bCdJJWsxNy2SJbUKEkUGbaSVKpSc9siWVLjFHjUTpKKVmJuWyRLapZCZ0lLUrEKzW3PkyxJkiS1sSdZUvOUeNxOkkpWYG5bJEtqnBIP20lSyUrMbYtkSY1T4vk2JalkJea2RbKkRknK7JGQpFKVmtsWyZKaJYECw1aSilVoblskS2qcEg/bSVLJSsxtTwEnSZIktbEnWVLzFNgjIUlFKzC3LZIlNUwUOQFEkspVZm5bJEtqngJ7JCSpaAXmtkWypGbJMk8lJEnFKjS3l1kkR8RXGeR7QWYe2pUWSVKBPRLdZmZL6qkCc3uwnuSLx6wVkrSU8nokxoCZLamHysvtZRbJmXlC6+2IWDkzF3a/SZKk5WVmS9LoGvI8yRGxc0RcBVxd3942Io7tesskTVzZ4UVmtqTeKDCzh/NjIl8G9gDmA2TmP4Dnd7FNkiY6i+SR+DJmtqSxVmBmD+vsFpl5a8RSY036utMcSRNeAgXOkh5LZrakMVVobg+nSL41Ip4LZERMBQ6lPownSd2Q46CHocHMbEljrsTcHs5wi7cDhwDrA7cB29W3Jak7HG4xEma2pLFXYGYP2ZOcmfOA/cegLZJUKfCw3VgxsyX1RIG5PZyzW2wWEb+MiLsiYm5E/CIiNhuLxkmSlo+ZLUmjYzjDLX4AnArMAp4E/Ag4uZuNkjSxRXZ2EWBmS+qBEjN7OEVyZOaJmbm4vpzEuBhJImlc6nQ8sqm0hJktaWwVmtnLHJMcETPqq3+IiMOBU6ie0uuBX49B2yRNSFHk2LZuM7Ml9U6ZuT3YxL1LqAJ2ybN+W8t9CfxPtxolaYLrYg9DRKwBfAvYpt7TvwPXAD8ENgFuAl6XmXfX6x8BHER1ruFDM/OM7rVuRMxsSb3TpdzuZWYvs0jOzE073agkjUh3D8N9BfhtZr62Po/wSsBHgLMz8zN1L+zhwIcjYitgX2BrqvG9Z0XEFpnZuB/nMLMl9VT3crtnmT2sX9yLiG2ArYAVlyzLzO91skNJGlL3eiRWo/qJ5jcDZOYiYFFE7A3sWq92AnAO8GFgb+CUzHwEuDEirgd2AM7rTgtHh5ktacx1Ibd7ndlDFskRcWTdkK2A04G9gHMBA1fS6Ovuz5tuBtwFfDcitqUaovAeYN3MnAOQmXMiYp16/fWB81seP7te1lhmtqQx173c7mlmD+fsFq8FdgPuyMy3ANsC0zrdoSR10cyIuLjlcnDb/VOAZwHfyMxnAgupDtMty0Cp3/Q52Wa2pPGi0Zk9nOEWD2Vmf0Qsrru951JV9pLUFSM4f+a8zNx+kPtnA7Mz84L69o+pAvfOiJhV90jMosq5Jetv2PL4DYDbO27d2DCzJY25DnO70Zk9nJ7ki+uZhd+k6ua+FLiw0x1K0pC6dM7NzLwDuDUitqwX7QZcBZwGHFgvOxD4RX39NGDfiJgWEZsCm9P8/DOzJY29AjN7yJ7kzHxnffV/I+K3wGqZeVmnO5SkHns38P16lvQNwFuoOgxOjYiDgFuAfQAy88qIOJUqlBcDhzTxzBatzGxJhelZZg/2YyLPGuy+zLy0051K0mC6+XOlmfl3YKDDe7stY/2jgKO616LRYWZL6qVu5XYvM3uwnuSjB7kvgReNRgNaXXflKrz0qc8f7c2qYSat3OiOOI2CeHA4I7kGUeAvN42Bsc/sK1Zhry13Ge3NqmEmrfhor5ugLouHRyFzC8ztwX5M5IVj2RBJAoY9Vk1LM7Ml9UyhuT3C7h5JkiSpPMP6xT1JGlMF9khIUtEKzG2LZEmN082Je5Kk0Vdibg853CIqb4yIj9W3N4qIHbrfNEkTVpfOkzwRmNmSeqLAzB7OmORjgZ2BN9S37we+3rUWSZJF8kiY2ZLGXoGZPZzhFjtm5rMi4m8AmXl3fUJnSRp1kWUethtDZrakMVVqbg+nSH40IiZT1/wRsTbQ39VWSZrYCjzf5hgysyWNvQJzezjDLY4BfgasExFHAecCn+pqqyRJnTKzJWkUDNmTnJnfj4hLqH7+L4BXZebVXW+ZpImrwMN2Y8XMltQTBeb2kEVyRGwEPAj8snVZZt7SzYZJmrhKHNs2VsxsSb1QYm4PZ0zyr6m+HwSwIrApcA2wdRfbJWkiKzBsx5CZLWnsFZjbwxlu8fTW2xHxLOBtXWuRpImt0FnSY8XMljTmCs3t5f7Fvcy8NCKe043GSBJQZI9Er5jZksZEgbk9nDHJ72+5OQl4FnBX11okSQWG7VgxsyX1RIG5PZye5FVbri+mGu/2k+40R5LKPGw3hsxsSWOuxNwetEiuT0i/SmZ+cIzaI0nqkJktSaNnmT8mEhFTMrOP6lCdJKnBzGxJGl2D9SRfSBW2f4+I04AfAQuX3JmZP+1y2yRNVAUethsDZrak3ikwt4czJnkGMB94EY+fezMBA1fS6Cv0VEJjyMyWNLYKze3BiuR16lnSV/B40C5R4EshqTFMmE6Y2ZJ6p8CUGaxIngyswtJBu0SBL4WkxjBhOmFmS+qdAlNmsCJ5TmZ+YsxaIklUFV6Jh+3GgJktqSdKze1lnt2CgXsjJEnNZGZL0igarCd5tzFrhSS1KrBHYgyY2ZJ6p8DcXmaRnJkLxrIhkgQUO0u628xsST1TaG4P5xRwkjS2CgxbSSpagbltkSypeQoMW0kqWoG5bZEsqXFKPGwnSSUrMbctkiU1T4FhK0lFKzC3BzsFnCRJkjQh2ZMsqVmSInskJKlYhea2RbKkxilxbJsklazE3LZIltQ8BYatJBWtwNy2SJbUOCX2SEhSyUrMbYtkSc1TYNhKUtEKzG2LZEnNUugEEEkqVqG5bZEsqVGivkiSxodSc9vzJEuSJElt7EmW1DwFHraTpKIVmNsWyZIap8RZ0pJUshJz2yJZUvMUGLaSVLQCc9sxyZKaJzu8DFNETI6Iv0XEr+rbMyLidxFxXf3vmi3rHhER10fENRGxx6g8P0kqTYGZbZEsqVmyOmzXyWU5vAe4uuX24cDZmbk5cHZ9m4jYCtgX2BrYEzg2IiaPxtOUpGIUmtkWyZKap4s9yRGxAfAy4Fsti/cGTqivnwC8qmX5KZn5SGbeCFwP7NDZk5KkghWY2RbJkkoyMyIubrkcPMA6XwY+BPS3LFs3M+cA1P+uUy9fH7i1Zb3Z9TJJ0tj4Mj3KbCfuSWqcEcySnpeZ2y9zuxEvB+Zm5iURsetwmjLAsgKnp0jSyHSY2zMj4uKW28dl5nGPbbPHmW2RLKl5uleGPg94ZUS8FFgRWC0iTgLujIhZmTknImYBc+v1ZwMbtjx+A+D2rrVOksarznJ70I4NepzZDreQ1DjdmriXmUdk5gaZuQnV5I7fZ+YbgdOAA+vVDgR+UV8/Ddg3IqZFxKbA5sCFo/x0JWncKzGz7UmW1CzLeWqgUfIZ4NSIOAi4BdgHIDOvjIhTgauAxcAhmdk35q2TpCYb+9wek8y2SJbUPGMQtpl5DnBOfX0+sNsy1jsKOKr7LZKkcazLud2LzLZIltQoQZk/bypJpSo1tx2TLEmSJLWxJ1lS8xTYIyFJRSswty2SJTVOZIFpK0kFKzG3LZLH0HfPvpCHFk6mry/o7wve89pnssrqj3LEF//JOus/zNzbVuTT73sqD9y3Qq+bqhGYNCk55meXMe/OqXz84Kdx0IdvYscX3s3iR4M5t6zIFw9/Cgvv97/eMvXm7BbSExx/9kU8uHAy/f1BX1/wntdsxwHvuZmdd5tPf39w7/wVOPqIzVkwd1qvm6oOrTC1n8//8CpWmJpMnpyc+9sZnPTlDdj0qQt59ydvYsWV+5g7exqfe9+TefABc3uZCs3trr3jEfEdYMkvpWzTrf2MN4e/6Rncd8/jRfDr/mM2fz9/DX70zQ3Z5z9uZZ//mM13j960hy3USO194Bxu+dd0VlqlOuvM3/6yBt/9wsb09wX//sGbeP3bZ/Odz2/S20Y2XIkTQMYDc/uJDj/w6dx39+OZ/ZNvrc+JX9kYgFcecDv7HXIrXzvyKb1qnkbo0UXB4fs/jYcfnMzkKf184dSruPic1XnHx2/mW5/aiMsvXI2X7DOX1/zHHE780oZDb3ACKzG3uzlx73hgzy5uvwg77Tafs36+LgBn/Xxddt59fo9bpJGYud4j7LDr3Zxx6rqPLbv03DXo76t+KfOff1+Vmest6lXzxo/s8KKROh5ze1APLny8b2nF6X3+3Y17wcMPTgZgypRkypQkM9hg04e4/MJVAbj03NX5tz0X9LKR40OBmd21nuTM/FNEbNKt7Y9HmfDJb19OEvzmh+vx21NnscZai7j7rqkA3H3XVFaf8WiPW6mReNtHb+Tbn9uY6SsPfO7yl7x2Ln/89cwxbtX4U2KPxHhgbi8tgaO+fQWZ8JsfzuI3p64HwIHvvYndXjWXhfdP4fA3Pb23jdSITZqUHHPaFTxp44f51Unrcs0/VuGma1dip93v5vyzZrDLSxcwc5adG0MpMbcdYDOGPrDftiyYO43VZyziqO9cwewbVup1kzSKdnjhAu6ZvwLXX7kKT9/h3ifcv+87ZtO3OPjDaRbJQyowbDX+HPaGZzyW2Z/67hXcesN0rrh4dU748iac8OVNeN3Bt/KKN97OSV/duNdN1Qj09wfvevnTWXnVxfzX/17Lxls8yJc+vBnvOPIm9nv3bZx/9posftQz5g6pwNzu+bseEQdHxMURcfGi/od73ZyuWjK5494FUznvrLXY4hn3c8/8qay5dvUNdc21F3HvAiftjVdbPet+dtrtbo7/wyUc/uVr2Xane/ngF64FYPdXz2WHFy7gc4dtTnXadWl8Wiqzc+Jk9l9/txZbPuP+pe4/51dr87yXOESuFAvvn8JlF6zG9s+/l9k3TOejBz6NQ/d+On/85VrMucXJmRNRz4vkzDwuM7fPzO2nTlqx183pmmnT+5i+8uLHrj/zeXdz87Urcf7vZ7D7q+4EYPdX3cn5Z6/Vy2ZqBI4/emMO2GV73vzCZ/OZ927BP85fnc9/YAuevcvd7HPwbfz325/GIw9P7nUzmy+rw3adXNR9S2V2TJzMftbz7uGm61bmSRs/9Ng6O71oAbNvmN6rJmoUrD7jUVZetXqfp07r55nPu49bb1iR1deqhj5GJPsecjun/2CdXjaz+QrNbIdbjJE111rEf37tagAmT07O+dXaXHLuDK69YlWO+NLVvOQ1d3DXnGl86r1P63FLNdreeeSNrDC1n6OOvxKoJu997WNP7nGrGm4chKfKtuZaj/JfX78KgMmTq17jS/68Jh895mo22PQhMmHubdP4qme2GNfWXOdRPvD5fzFpchIBfz59Bhf+fk32fvMdvPyAqgPrr2esyZk/WrvHLR0HCsztyC6d/DkiTgZ2BWYCdwJHZua3B3vM6lNm5s6r7N2V9qg5sm/gSW0qx/kP/op7++Z1NK5klbU2zG32el9H+73g+4ddkpnbd/RgLXdurz55Zu60yivHqHXqmUedUF668x8+nXv753c8FrDT3G56Znfz7BZv6Na2JRWuwF9uGg/MbUkdKzC3HW4hqXHGw1g1SdLjSsxti2RJzTJOTjIvSaoVmts9P7uFJEmS1DT2JEtqnOjvdQskScujxNy2SJbUPAUetpOkohWY2xbJkhqnxAkgklSyEnPbIllSsyRFnkpIkopVaG5bJEtqnBJ7JCSpZCXmtkWypOYpMGwlqWgF5rangJMkSZLa2JMsqVGCMg/bSVKpSs1ti2RJzZJZ5AQQSSpWobltkSypcUrskZCkkpWY2xbJkpqnwLCVpKIVmNsWyZIap8QeCUkqWYm5bZEsqVkS6C8wbSWpVIXmtkWypOYpL2slqWwF5rbnSZYkSZLa2JMsqXFKHNsmSSUrMbctkiU1T4Hn25SkohWY2xbJkhqnxB4JSSpZibltkSypWZIiJ4BIUrEKzW2LZEmNEkAUeNhOkkpVam5bJEtqnv5eN0CStFwKzG1PASdJkiS1sSdZUuOUeNhOkkpWYm5bJEtqlkIngEhSsQrNbYtkSQ2TRZ5vU5LKVWZuOyZZUuNEdnYZcrsRG0bEHyLi6oi4MiLeUy+fERG/i4jr6n/XbHnMERFxfURcExF7dO9ZS9L41Y3M7jWLZEnNk9nZZWiLgcMy82nATsAhEbEVcDhwdmZuDpxd36a+b19ga2BP4NiImNyFZyxJ41t3MrunLJIlNUtC9Hd2GXLTmXMy89L6+v3A1cD6wN7ACfVqJwCvqq/vDZySmY9k5o3A9cAOo/p8JWm86zC3h9Lro38WyZImpIjYBHgmcAGwbmbOgaqQBtapV1sfuLXlYbPrZZKk7uvp0T+LZEnN0/lwi5kRcXHL5eCBNh8RqwA/Ad6bmfcN0pIYqHUjf4KSVJguDLfo9dE/z24hqXk6L0PnZeb2g60QEStQFcjfz8yf1ovvjIhZmTknImYBc+vls4ENWx6+AXB7x62TpFJ1ltszI+LiltvHZeZxA6042NG/iGg9+nd+y8NGdPTPIllS43TrpPQREcC3gasz84std50GHAh8pv73Fy3LfxARXwSeBGwOXNiVxknSONZhbg/ZsQFPPPpXRfnAqw6wrOMPFItkSc3TvVnPzwMOAC6PiL/Xyz5CVRyfGhEHAbcA+1TNyCsj4lTgKqqxcYdkZl+3GidJ41b3Ojd6dvTPIllSsyQwjFnPHW0681wG7mkA2G0ZjzkKOKo7LZKkAnQpt3t99M8iWVKjBNm14RaSpNHXxdzu6dE/i2RJzWORLEnjSxdyu9dH/zwFnCRJktTGnmRJzWNPsiSNLwXmtkWypGbp4sQ9SVIXFJrbFsmSGseJe5I0vpSY2xbJkpqnwLCVpKIVmNsWyZIaJosMW0kqV5m5bZEsqVmSIsNWkopVaG57CjhJkiSpjT3JkpqnwFnSklS0AnPbIllS45Q4S1qSSlZiblskS2qeAsNWkopWYG5bJEtqlgT6ywtbSSpWobltkSypYco8lZAklavM3LZIltQ8BYatJBWtwNz2FHCSJElSG3uSJTVPgT0SklS0AnPbIllSsxQ6AUSSilVobjeqSL6vb/68M+79zs29bscYmgnM63Uj1HUT8X3euPOHJmSBZ6Uv0H398+eded93J1Jmw8T8/zzRTMT3eASZDaXmdqOK5Mxcu9dtGEsRcXFmbt/rdqi7fJ87UOBhuxJNtMwG/z9PBL7HHSowtxtVJEtSqYftJKlYhea2RbKk5imwR0KSilZgblsk99ZxvW6AxoTv8/IqMGxVDP8/l8/3uBMF5rbnSe6hzPQ/4gTg+yyVw//P5fM91hL2JEtqmDJ/3lSSylVmbtuT3CMRsWdEXBMR10fE4b1uj0ZfRHwnIuZGxBW9bsu4kkB/f2cXqUvM7PKZ2SPQaW43nEVyD0TEZODrwF7AVsAbImKr3rZKXXA8sGevGzEuZXZ2kbrAzJ4wjsfM7lyBmW2R3Bs7ANdn5g2ZuQg4Bdi7x23SKMvMPwELet2OcckiWc1iZk8AZvYIFZjZjknujfWBW1tuzwZ27FFbpIbJIs+3qXHNzJYGVWZuWyT3RgywrLy/LqkTCVngz5tqXDOzpcEUmtsOt+iN2cCGLbc3AG7vUVskSYMzs6UJyJ7k3rgI2DwiNgVuA/YF9uttk6QGKfCwncY1M1saSoG5bU9yD2TmYuBdwBnA1cCpmXllb1ul0RYRJwPnAVtGxOyIOKjXbRo3nLinBjGzJwYze4QKzGx7knskM08HTu91O9Q9mfmGXrdhXMocF+fP1MRiZpfPzB6BQnPbIllS84yDHgZJUosCc9siWVLjZIE9EpJUshJz2yJZUsOMj7FqkqQlysxtJ+5JkiRJbexJltQsSZGnEpKkYhWa2/YkFy4i+iLi7xFxRUT8KCJWGsG2jo+I19bXvxURWw2y7q4R8dwO9nFTRMwc7vK2dR5Yzn19PCI+sLxt1BjI/s4u0jhnZg+6vpndZAVmtkVy+R7KzO0ycxtgEfD21jsjYnInG83Mt2bmVYOssiuw3IErJZD92dFFKoCZrXGn09xuOovkieXPwFPqHoM/RMQPgMsjYnJEfD4iLoqIyyLibQBR+VpEXBURvwbWWbKhiDgnIravr+8ZEZdGxD8i4uyI2IQq2N9X94jsEhFrR8RP6n1cFBHPqx+7VkScGRF/i4j/A2KoJxERP4+ISyLiyog4uO2+o+u2nB0Ra9fLnhwRv60f8+eIeOqovJrqjkx7kqWKmW1mjw+d5nbDOSZ5goiIKcBewG/rRTsA22TmjXVo3ZuZz4mIacBfIuJM4JnAlsDTgXWBq4DvtG13beCbwPPrbc3IzAUR8b/AA5n5hXq9HwBfysxzI2Ijql+uehpwJHBuZn4iIl4GLBWgy/Dv9T6mAxdFxE8ycz6wMnBpZh4WER+rt/0u4Djg7Zl5XUTsCBwLvKiDl1FjZDz0MEjdZGab2eNNibltkVy+6RHx9/r6n4FvUx1SuzAzb6yXvwR4RtRj14DVgc2B5wMnZ2YfcHtE/H6A7e8E/GnJtjJzwTLasTuwVcRjnQ6rRcSq9T7+X/3YX0fE3cN4TodGxKvr6xvWbZ0P9AM/rJefBPw0Ilapn++PWvY9bRj7UC+Ngx4GqUvMbDN7fCowty2Sy/dQZm7XuqAOnoWti4B3Z+YZbeu9lGqo0WBiGOtANbRn58x8aIC2DPvrZ0TsShXeO2fmgxFxDrDiMlbPer/3tL8Gaq77ufuMs/LHg074GcS8UW2MNPbMbDN73BlBbjc6sx2TLKgOo70jIlYAiIgtImJl4E/AvvX4t1nACwd47HnACyJi0/qxM+rl9wOrtqx3JtVhNOr1tquv/gnYv162F7DmEG1dHbi7DtunUvWKLDEJWNKzsh/VIcH7gBsjYp96HxER2w6xD/VQZu6Zmdt3eNmz1+2XxoCZrUYZQW43OrMtkgXwLaqxa5dGxBXA/1EdZfgZcB1wOfAN4I/tD8zMu6jGpP00Iv7B44fOfgm8eskkEOBQYPuoJplcxeMztv8beH5EXEp1CPGWIdr6W2BKRFwG/A9wfst9C4GtI+ISqvFrn6iX7w8cVLfvSmDvYbwmktRUZrY0BiIL/BlBSZIkaSTsSZYkSZLaWCRLkiRJbSySJUmSpDYWyZIkSVIbi2RJkiSpjUWyJEmS1MYiWZIkSWpjkSxJkiS1sUiWJEmS2lgkS5IkSW0skiVJkqQ2FsmSJElSG4tkSZIkqY1FsiRJktTGIlmSJElqY5EsSZIktbFIliRJktpYJEuSJEltLJIlSZKkNhbJkiRJUhuLZEmSJKmNRbIkSZLUxiJZkiRJamORLEmSJLWxSJYkSZLaWCRLkiRJbSySJUmSpDYWyZIkSVIbi2RJkiSpjUWyJEmS1MYiWZIkSWpjkSxJkiS1sUiWJEmS2lgkS5IkSW0skiVJkqQ2FsmSJElSG4tkSZIkqY1FsiRJktTGIlmSJElqY5EsSZIktbFIliRJktpYJEuSJEltLJIlSZKkNhbJkiRJUhuLZEmSJKmNRbIkSZLUxiJZkiRJamORLEmSJLWxSJYkSZLaWCRLkiRJbSySJUmSpDYWyZIkSVIbi2RJkiSpjUWyJEmS1MYiWZIkSWpjkSxJkiS1mdLrBkhSqz1euHLOX9DX0WMvueyRMzJzz1FukiRpEJ3mdtMz2yJZUqPMW9DHBWds0NFjV5j1r5mj3BxJ0hA6ze2mZ7ZFsqSGSfqyv9eNkCQNW5m5bZEsqVES6Cd73QxJ0jCVmtsWyZIap5/yeiQkqWQl5rZnt5AkSZLa2JMsqVGSpC/LO2wnSaUqNbctkiU1Tolj2ySpZCXmtkWypEZJoK/AsJWkUpWa2xbJkhqnxB4JSSpZibltkSypURKKHNsmSaUqNbc9u0UhImJ6RPwyIu6NiB+NYDv7R8SZo9m2XoiI30TEgb1uhzrT3+FF5TPrljZesm55Xu+IeHNEnDvI/edExFtHr3WdiYhNIiIjoisdjhHxkYj4VsvtV0fErRHxQEQ8MyKujIhdu7HvTpSY2RbJYywi9ouIi+s/8jl1wP3bKGz6tcC6wFqZuU+nG8nM72fmS0ahPUuJiF3rMPlp2/Jt6+XnDHM7H4+Ik4ZaLzP3yswTOmxu6/6mRsTRETG7fs9ujIgv1fedERGfGOAxe0fEHUuCMyJ2iIjTI+KeiFgQERdGxFtG2japycy6cZd1SxWmEbFaRPwlIn4SEStExPF1+3doWecpETGs7sNuvd7dFhFbRMSPImJe/cXssoh4f0RM7va+M/NTmdn6ZeALwLsyc5XM/Ftmbp2Z53S7HROZRfIYioj3A18GPkUV8hsBxwJ7j8LmNwauzczFo7CtbrkLeG5ErNWy7EDg2tHaQVRG8+/6CGB7YAdgVeCFwN/q+44HDoiIaHvMAcD3M3NxROwM/B74I/AUYC3gHcBeo9jGoiRJX4cXNYNZNy6zrnXbawJnATcDr8/MR+u7FgCf7MY+e2GoHuCIeDJwAXAr8PTMXB3Yh+ozYdXut/AJNgauHOlGutHz3WluN51F8hiJiNWBTwCHZOZPM3NhZj6amb/MzA/W60yLiC9HxO315csRMa2+b9e6N/OwiJhb98y8pb7vv4GPAa+ve20Oau+FaD8sVPca3BAR99e9o/u3LG/tTXhuRFxUf4O+KCKe23LfORHxP3Vvw/0RcWZEzBzkZVgE/BzYt378ZOB1wPfbXquvRHVI6b6IuCQidqmX7wl8pOV5/qOlHUdFxF+AB4HNouVwXER8IyJ+3LL9z0bE2QMUtwN5DvCzzLw9Kzdl5vfq+34OzAB2adn2msDLgSXrfB44ITM/m5nz6m1ckpmvG8a+J6aEvg4v6j2zDhifWbfkMTOpvthfCbyx7cvICcAzIuIFy3js6hHx7fo9uy0iPlk/94Fe75dExDX1631sRPwx2oZQRMQXIuLu+n1r71h4clRH5e6NiF9ExIyWx70yqqEI99Svz9Na7rspIj4cEZcBCyNiSn37tvq9vSYidqtX/2/gr5n5/sycA5CZ12Tmfpl5zwDP/y0RcXW9nRsi4m2tr2tE/CoeP6L456i/5Cxr/0v+tuv/Lw8Ak4F/RMS/Wp7L7vX1SRFxeET8KyLmR8SpS16Tlv8TB0XELfX7O7oKzWyL5LGzM7Ai8LNB1vkosBOwHbAtVe/lf7bcvx6wOrA+cBDw9YhYMzOPpOqx+WF9GObbgzUkIlYGjgH2ysxVgecCfx9gvRnAr+t11wK+CPw6lu4d2Q94C7AOMBX4wGD7pioe31Rf34MqiG9vW+ciqtdgBvAD4EcRsWJm/rbteW7b8pgDgIOpvt3f3La9w6iC/c31h9BBwIGZ1SyDOrSWdRj4fOD9EfHOiHh664dNZj4EnNryfKD6IPxnZv4jIlaiet9/jIYtcUzyOGfWVcZb1lG3449Uvaf/npnt/60erNt11DIefwKwmOqo2TOBlwBPGDtcF+I/pjpStxZwDdV702rHevlM4HPAt9uK/TcB/w48qd7nMfW2twBOBt4LrA2cDvwyIqa2PPYNwMuANYAnA+8CnlP/jewB3FSvtzvLl99zqTpJVqP6W/lSRDyrvu8wYHbdpnWpvgRlRGw5yP4ByMxHMnOV+ua2mfnkAfZ9KPAq4AVUr8ndwNfb1nkB8LR6H6Oq09xuOovksbMWMG+IQ4T7A5/IzLmZeRfVt9gDWu5/tL7/0cw8HXgA2LLD9vQD20TE9Myck5kDHcJ5GXBdZp6YmYsz82Tgn8ArWtb5bmZe21IwbjfYTjPzr8CMOhjexOM9rq3rnJSZ8+t9Hg1MY+jneXxmXlk/5tHWOzLzQeCNVB98JwHvzszZLfevkZnLmiTyaeCzVO/NxcBtsfQkmROAfSJien37TfUygDWp/o/NGaLtWkrQ1+FFjWDWMS6zDmBDYIv6uS6rn+//gI3ae3YjYl2qYWTvrY8ezAW+RN2b3ualwJX1kYYlBe4dbevcnJnfzMw+qkydRVVcLnFiZl6RmQuB/wJeV/davx74dWb+rn59vgBMZ+ki/JjMvLV+L/uoXvetImKF+mjhv+r11mI58jszf52Z/6qPGP4ROJPHjzQ+Wj+Hjeu/6z/Xr/Fg+18ebwM+mpmzM/MR4OPAa2PpoRUfr9+bhzrY/hDKzGyL5LEzH5gZg48FehJL9wzcXC97bBttHzwPAquwnOpQeT3wdmBORPw6Ip46jPYsadP6Lbdbg2247TmR6pvzCxmgtymqw6xX14fR7qHqURrs0CZUY8aWKTMvBG4AguoDblgysy8zv56Zz6PqdTgK+M6Sw3f1B85dwN4RsRnV8Iwf1A+/m+oDetZw96e6RyI7u6gRzLrHjZusq/2Dqof8NxHxzGVs/xHgf+pLa5WzMbAC1et8T/18/o+q573dk2h5HnWxOLttnTta7n+wvtr6mre+DjfX+55J23tZ94bfytLvZeu+r6fqdf44MDciTomIJX+L81mO/I6IvSLi/Ho4xT1UXwaWvJ+fB64HzqyHYhw+jP0vj42Bn7W89ldTFeCtXywG/dsZiU5zu+ksksfOecDDVIdDluV2qj/0JTbiiYfnhmshsFLL7fVa78zMMzLzxVQB8E/gm8Noz5I23dZhm5Y4EXgncHpL+AFQHyL8MNWwhTUzcw3gXh4P42X9txr0v1tEHEL1bf124EOdNDozH8rMr1MVv1u13LXksOoBwJmZeWe9/oNU7/trOtnfRGZP8rhm1j1u3GVdZn4F+Azwu4jYZhmrfZeqoH91y7JbgUeAmXWP9RqZuVpmbj3A4+cAG7S0OVpvD9OGLdc3ouqpnUfbe1lve0OWfi+Xeg0z8weZ+W/145Lq6CFUkxeHld9Rjan/CVXP9br1+3k69fuZmfdn5mGZuRnVEYr3Lxl7PMj+l8etVMOK1mi5rJiZy3zeo63EzLZIHiOZeS/VhJOvR8SrImKlqE6rs1dEfK5e7WTgPyNi7XrM1seoDpl14u/A8yNio6gm0hyx5I6IWDeqiQ0rU4XaA1TfONudDmwR1amcpkTE66mKw1912CYAMvNGqrFRHx3g7lWpxpfdBUyJiI9Rje9a4k5gk1iOWd31GLVPUh2GPAD4UERsN8zHvjeqiUTT69fgwLqNf2tZ7XtUY9f+g8eHWizxIeDNEfHBJeMbozoV1CnDbf9EUx1/tEger8y6x42nrGtr9+eArwBn1cNF2u9fTNXz+eGWZXOohhccHdXp4yZFxJNj4El+vwaeXv99TAEOoe3LzTC8MSK2qud+fAL4cT0041TgZRGxW0SsQDUW+BHgrwNtJCK2jIgX1UXuw8CSIRgAR1KdpeTzEbFevf5ToppMt0bbpqZSfTm5C1hcD0d57JR3EfHy+rEB3Ffvo2+I/S+P/wWOioiN6/2tHRGjcTaZYek0t5vOInkMZeYXgfdTTVC5i+qb37uoZkFDFW4XA5cBlwOX0uHpdjLzd8AP621dwtJhP4kqOG6nOqXPC6h6O9q3MZ9qEsJhVIedPgS8PDPnddKmtm2fm5kD9RydAfyG6lRJN1OFRushoiU/HjA/Ii4daj91AJ8EfDYz/5GZ11FNmDgxHp9N/0DdqzOQh4CjqQ79zaMK89dk5g0tz+UmqgBeGTit7Xn+FXhRfbkhIhYAx1F9KEtFMuuW2vZ4ybr2dv8P8C3g7KhOhdbuZJ44XvdNVMXiVVRH3H7MAMMV6td1H6oJefOpvpBcTFXMDteJVKfhvINqouih9bavofqS8FWqzH4F8IrMXLSM7Uyj6jmfV29rHarXjXps8M7AJsCVEXEvVW/xxcD9bc/p/roNp9bPfT+W/jzYnKpn+gGqoy3HZnWO42Xufzl9pd7fmRFxP9Wk8x072I5axLLH5kvS2Nv6GVPzB79ad+gVB7DdxrMvycztR7lJkrqo7i2fDeyfmX/odXu0/DrN7aZndld+SlGSOrXksJ2kckXEHlSnmnsI+CDV2N3ze9oodazU3LZIltQoSdDnSDCpdDtTnQloyfCMV3Xn1GQaC6XmtkWypMbpz/J6JCQ9LjM/TjX5T4UoMbctkiU1SqmH7SSpVKXmdqOK5JkzJucmG67Q62aoy669bKWhV9K49jALWZSPdJiYQV+Wd9iuRGb2xGBml29kmQ2l5najiuRNNlyBC8/YcOgVNa7t8aTtet0EddkFeXavm6AxYGZPDGZ2+czsgTWqSJakBPoLnAAiSaUqNbctkiU1Tolj2ySpZCXmtkWypEbJLHNsmySVqtTctkiW1Dj9BfZISFLJSsxti2RJjVKdSqi8HglJKlWpuW2RLKlhyjxsJ0nlKjO3y3tGkiRJ0gjZkyypUUo9lZAklarU3C7vGUka9/oyOroMJSK+ExFzI+KKlmUzIuJ3EXFd/e+aLfcdERHXR8Q1EbFHy/JnR8Tl9X3HRER5M1YkaTmUmNkWyZIaJQn6mNTRZRiOB/ZsW3Y4cHZmbg6cXd8mIrYC9gW2rh9zbERMrh/zDeBgYPP60r5NSZowOs3tYTieHma2RbKkxunPSR1dhpKZfwIWtC3eGzihvn4C8KqW5adk5iOZeSNwPbBDRMwCVsvM8zIzge+1PEaSJqQSM9sxyZIapQenElo3M+cAZOaciFinXr4+cH7LerPrZY/W19uXS9KENMa5PWaZbZEsqVGS4Y1VW4aZEXFxy+3jMvO4Drc1UCNykOWSNCGNILcbndkWyZIaZwSzpOdl5vbL+Zg7I2JW3SMxC5hbL58NbNiy3gbA7fXyDQZYLkkTVoe53ejMdkyypInuNODA+vqBwC9alu8bEdMiYlOqyR4X1of57o+IneoZ0m9qeYwkqbvGLLPtSZbUKJl07ZebIuJkYFeqQ3yzgSOBzwCnRsRBwC3APlU78sqIOBW4ClgMHJKZffWm3kE163o68Jv6IkkTUrdyu9eZbZEsqWGC/gGHkI1cZr5hGXfttoz1jwKOGmD5xcA2o9g0SRrHupPbvc5si2RJjZJ0rydZkjT6Ss1ti2RJjTPGp4CTJI1QibltkSypUZKgv/NTwEmSxlipuW2RLKlxSuyRkKSSlZjb5T0jSZIkaYTsSZbUKAn0FzgBRJJKVWpuWyRLapigr0ungJMkdUOZuW2RLKlRSu2RkKRSlZrbFsmSGqfEHglJKlmJuW2RLKlRMqPIHglJKlWpuW2RLKlxSvzlJkkqWYm5Xd4zkiRJkkbInmRJjZJAf4Fj2ySpVKXmtkWypIaJIg/bSVK5ysxti2RJjVKdSqi8HglJKlWpuW2RLKlx+pwuIUnjSom5bZEsqVGSKLJHQpJKVWpuWyRLapz+AnskJKlkJeZ2ec9IkiRJGiF7kiU1Sib0FXjYTpJKVWpuWyRLapwSx7ZJUslKzG2LZEmNUk0AcSSYJI0Xpea2RbKkxukr8JebJKlkJea2RbKkRin1pPSSVKpSc9siWVLDlHnYTpLKVWZuWyRLapz+Ag/bSVLJSszt8sp+SZIkaYTsSZbUKKWeb1OSSlVqblskS2qcEse2SVLJSsxti2RJjVKdb7O8HglJKlWpuW2RLKlxSpwAIkklKzG3LZIlNUqp59uUpFKVmtsWyZIap8SxbZJUshJzu7xnJEmSJI2QPcmSmiXLnAAiScUqNLctkiU1SlLmBBBJKlWpuW2RLKlxSuyRkKSSlZjbFsmj7Oj3bcgFZ63GGjMXc9wfrgHgT79cnROPXo9br1uRY06/li22fQiAO26dyn+84KlssNkjADz12Qt5z2dnA/DB1zyFBXdOYeqKCcCnT/kXa8xc3INnpE6tMK2fo396PStMTSZPSf786zU48Qvr9bpZjVfqLGk102hl9hJHHrgpc26Z+ti2NP5MmpR89bfXMn/OCnzswM163ZxxodTc7mqRHBF7Al8BJgPfyszPdHN/TfCS1y/glW+Zx+ffs9FjyzZ56sN87Fs3ccyHN3zC+rM2foRvnDVwmH746zc/Fs4afx59JPjQPk/m4QcnM3lK8sWfX89Fv1+Vf166cq+b1nglhu14YGZXOs3sc09fnRVX7u9aWzU2XvXWedx63YqstEpfr5syrpSY2107u0VETAa+DuwFbAW8ISK26tb+muLpOy1k1TWX/o+10eaPsOFTHulRi9Q7wcMPTgZgygrJ5BWSzB43aRxY8stNnVzUOTP7cZ1k9kMLJ/HT/1ub/d57x2g2T2Ns5qxF7LDbffzmBzN63ZRxpdPcbrpungJuB+D6zLwhMxcBpwB7d3F/49Idt0zlnS/egg/8v6dw+QVL9zAe/b6NeMfuW/L9L61rcTVOTZqUHPu7a/jhZVfytz+twjV/sxdZjWVmD8OyMvuEz63Ha95+F9OmG9bj2dv/+3a+9clZZH/zCzh1XzeHW6wP3NpyezawYxf3N+7MWOdRTrroKlab0cd1l03n42/ZlOPO+Scrr9rPh792MzNnPcqDD0zif966CWf9eE1evM/dvW6yllN/f/DOF2/Jyqv1ceS3b2TjLR/i5mum97pZjVfiLOlxwMwewrIy+46bp3H7jdN4+3/fzh23Tu11M9WhHXe/j3vmTeH6y1fiGTs/0OvmjDsl5nY3e5IHerWe8BU7Ig6OiIsj4uK75k+s8T9TpyWrzaie8+bPeIgnbbKI226YBsDMWY8CsNIq/bzw1fdwzd9W6lk7NXIL75vMP85bhee88P5eN6X5kq4Ot4iI90XElRFxRUScHBErRsSMiPhdRFxX/7tmy/pHRMT1EXFNROzRtefde2b2EJaV2VddshLXXb4Sb9phKw571VO47YZpfPA1T+lxa7W8tnrOQnZ6yX2ccMFVHPGNm9n23x7gQ1+9udfNGh86zO3h6GVmd7NIng20znrYALi9faXMPC4zt8/M7ddea3IXm9M898yfTF/9GTPn5qncduNU1ttoEX2L4d751Wux+FG44KzV2OSpD/ewperE6jMWs/Jq1Rs8dcV+nrXLA9x6/Yo9blXzLZkl3Y3AjYj1gUOB7TNzG6oJavsChwNnZ+bmwNn1beoxufsCWwN7AsfWY3dLZGYPYVmZ/YoD53Py367kexdexdE/v571N3uEz//k+t42Vsvtu5+exRu334oDd9yKT79jY/5x7ip87t0b97pZ40KnuT2UXmd2N4dbXARsHhGbArdRNXq/Lu6vET79jo257LxVuHfBFPZ/9lYccNgdrLpmH8f+5/rcO38K/3XAZjx564f41Mk3cPn5q/C9z6/H5CkweVJy6Gdms9qafTz84CQ+st+T6Vsc9PXBs3Z5gL32n9/rp6blNGPdR/nAV25h0iSYNKk6rdQFZ63W62aNC12e0DEFmB4RjwIrURWCRwC71vefAJwDfJhqTO4pmfkIcGNEXE81dve8bjawR8zsDjNbUldzu2eZ3bUiOTMXR8S7gDOoKv/vZOaV3dpfUxzxjYEPzTxvr3ufsGyXl93LLi974vIVV+rn62dcO+pt09i68erpHPKSLXvdjHFnySzprmw787aI+AJwC/AQcGZmnhkR62bmnHqdORGxTv2Q9YHzWzYxu15WHDN7acuT2a3W23CR50guwGXnrcJl563S62aMG93K7V5ndlfPk5yZpwOnd3MfksqTnYftzIi4uOX2cZl53JIb9bi1vYFNgXuAH0XEGwfZ3rDG6ZbCzJbUqQ5zu9GZ7S/uSWqcEcySnpeZ2w9y/+7AjZl5F0BE/BR4LnBnRMyqeyRmAXPr9Yc1TleSJroOc7vRmd3NiXuS1DS3ADtFxEoREcBuwNXAacCB9ToHAr+or58G7BsR0+qxupsDF45xmyVpouppZtuTLKlRMrs3ASQzL4iIHwOXAouBvwHHAasAp0bEQVShvE+9/pURcSpwVb3+IZnpTC1JatGt3O51ZlskS2qcEYxJHsa280jgyLbFj1D1UAy0/lHAUV1rkCQVoFu53cvMtkiW1DDdO7uFJKkbysxti2RJjdPNnmRJ0ugrMbctkiU1ypJfbpIkjQ+l5rZFsqRmyWoSiCRpnCg0tz0FnCRJktTGnmRJjTOCHxORJPVAibltkSypUZIyJ4BIUqlKzW2LZEkNU+aphCSpXGXmtkWypMYpcQKIJJWsxNy2SJbUOCUetpOkkpWY2xbJkhols8ywlaRSlZrbngJOkiRJamNPsqTGKXECiCSVrMTctkiW1DglTgCRpJKVmNsWyZIap8SxbZJUshJz2yJZUqMkUWTYSlKpSs1ti2RJjVPgUTtJKlqJuW2RLKlZCj2VkCQVq9DctkiW1DwldklIUskKzG3PkyxJkiS1sSdZUuOUeNhOkkpWYm5bJEtqnBLPtylJJSsxty2SJTVKUmaPhCSVqtTctkiW1CwJFBi2klSsQnPbIllS45R42E6SSlZiblskS2qeAsNWkopWYG57CjhJkiSpjT3JkhomipwAIknlKjO3LZIlNU+Bh+0kqWgF5rZFsqRmyTJPJSRJxSo0t5dZJEfEVxnke0FmHtqVFklSgT0S3WZmS+qpAnN7sJ7ki8esFZK0lPJ6JMaAmS2ph8rL7WUWyZl5QuvtiFg5Mxd2v0mSJrwCeyS6zcyW1FMF5vaQp4CLiJ0j4irg6vr2thFxbNdbJklabma2JI2O4Zwn+cvAHsB8gMz8B/D8LrZJ0kSXHV4EZrakXigws4d1dovMvDViqbEmfd1pjqQJL4ECZ0mPJTNb0pgqNLeHUyTfGhHPBTIipgKHUh/Gk6RuyHHQw9BgZrakMVdibg9nuMXbgUOA9YHbgO3q25LUHQ63GAkzW9LYKzCzh+xJzsx5wP5j0BZJqhR42G6smNmSeqLA3B7O2S02i4hfRsRdETE3In4REZuNReMkTUyRnV1kZkvqjRIzezjDLX4AnArMAp4E/Ag4uZuNkjSBdTrUYhwE7hgxsyWNrUIzezhFcmTmiZm5uL6cxLh4apI0IZnZkjQKljkmOSJm1Ff/EBGHA6dQBe3rgV+PQdskTUhR5Ni2bjOzJfVOmbk92MS9S6gCdsmzflvLfQn8T7caJWmC62K/Z0SsAXwL2Kbe078D1wA/BDYBbgJel5l31+sfARxEda7hQzPzjO61bkTMbEm906Xc7mVmL7NIzsxNO92oJI1IdwcHfAX4bWa+tj6P8ErAR4CzM/MzdS/s4cCHI2IrYF9ga6rxvWdFxBaZ2bgf5zCzJfVU93K7Z5k9rF/ci4htgK2AFZcsy8zvdbJDSRpS93okVqP6ieY3A2TmImBRROwN7FqvdgJwDvBhYG/glMx8BLgxIq4HdgDO604LR4eZLWnMdSG3e53ZQxbJEXFk3ZCtgNOBvYBzAQNX0ujr7s+bbgbcBXw3IralGqLwHmDdzJwDkJlzImKdev31gfNbHj+7XtZYZrakMde93O5pZg/n7BavBXYD7sjMtwDbAtM63aEkDWUE50meGREXt1wObtv0FOBZwDcy85nAQqrDdMtsygDLmn6mCDNb0pgrMbOHM9zioczsj4jFdbf3XKrKXpKaZl5mbj/I/bOB2Zl5QX37x1SBe2dEzKp7JGZR5dyS9TdsefwGwO2j3ehRZmZLGi8andnD6Um+uJ5Z+E2qbu5LgQs73aEkDalLJ6bPzDuAWyNiy3rRbsBVwGnAgfWyA4Ff1NdPA/aNiGkRsSmwOc3PPzNb0tgrMLOH7EnOzHfWV/83In4LrJaZl3W6Q0nqsXcD369nSd8AvIWqw+DUiDgIuAXYByAzr4yIU6lCeTFwSBPPbNHKzJZUmJ5l9mA/JvKswe7LzEs73akkDSa6OOo3M/8ODHR4b7dlrH8UcFT3WjQ6zGxJvdSt3O5lZg/Wk3z0IPcl8KLRaECr665clZdu9YLR3qwaZtJKj/a6CeqyeGg4I7kGUeAvN42BHmT2Krz0qc8f7c2qYSattLjXTVCXjTizocjcHuzHRF44lg2RJGDYY9W0NDNbUs8UmtvD+jERSRpTBYatJBWtwNwehf51SZIkqSz2JEtqnG5O3JMkjb4Sc3vInuSovDEiPlbf3igiduh+0yRNWF06T/JEYGZL6okCM3s4wy2OBXYG3lDfvh/4etdaJEkWySNhZksaewVm9nCGW+yYmc+KiL8BZObd9QmdJWnURZZ52G4MmdmSxlSpuT2cIvnRiJhMXfNHxNpAf1dbJWliK/B8m2PIzJY09grM7eEMtzgG+BmwTkQcBZwLfKqrrZI0sTncYiTMbEljr8DMHrInOTO/HxGXUP38XwCvysyru94ySRNWiYftxoqZLakXSsztIYvkiNgIeBD4ZeuyzLylmw2TJC0/M1uSRsdwxiT/mqpTPIAVgU2Ba4Ctu9guSRNZgT0SY8jMljT2Cszt4Qy3eHrr7Yh4FvC2rrVI0sRW6CzpsWJmSxpzheb2cv/iXmZeGhHP6UZjJAkoskeiV8xsSWOiwNwezpjk97fcnAQ8C7iray2SpALDdqyY2ZJ6osDcHk5P8qot1xdTjXf7SXeaI0llHrYbQ2a2pDFXYm4PWiTXJ6RfJTM/OEbtkSR1yMyWpNGzzB8TiYgpmdlHdahOktRgZrYkja7BepIvpArbv0fEacCPgIVL7szMn3a5bZImqgIP240BM1tS7xSY28MZkzwDmA+8iMfPvZmAgStp9BV6KqExZGZLGluF5vZgRfI69SzpK3g8aJco8KWQ1BgmTCfMbEm9U2DKDFYkTwZWYemgXaLAl0JSY5gwnTCzJfVOgSkzWJE8JzM/MWYtkSSqCq/Ew3ZjwMyW1BOl5vZgRfJAvRGS1H0Fhu0YMLMl9U6Bub3MU8ABu41ZKyRJI2VmS9IoWmZPcmYuGMuGSBJQ7CzpbjOzJfVMobk9nFPASdLYKjBsJaloBea2RbKk5ikwbCWpaAXmtkWypMYp8bCdJJWsxNy2SJbUPAWGrSQVrcDctkiW1CxJkWErScUqNLctkiU1TomH7SSpZCXm9mDnSZYkSZImJHuSJTVPgT0SklS0AnPbIllS45R42E6SSlZiblskS2qeAsNWkopWYG5bJEtqlkJnSUtSsQrNbYtkSY0S9UWSND6UmtsWyZKap8AeCUkqWoG57SngJEmSpDb2JEtqnBJnSUtSyUrMbXuSJTVPdngZpoiYHBF/i4hf1bdnRMTvIuK6+t81W9Y9IiKuj4hrImKPUXl+klSaAjPbIllS83S5SAbeA1zdcvtw4OzM3Bw4u75NRGwF7AtsDewJHBsRkzt8VpJUrgIz2yJZUrNkddiuk8twRMQGwMuAb7Us3hs4ob5+AvCqluWnZOYjmXkjcD2wwyg8S0kqR6GZbZEsqXm625P8ZeBDQH/LsnUzcw5A/e869fL1gVtb1ptdL5MktSowsy2SJTXOCHqSZ0bExS2Xg5fabsTLgbmZeclwmzLAsuU7SChJE0CJme3ZLSSVZF5mbj/I/c8DXhkRLwVWBFaLiJOAOyNiVmbOiYhZwNx6/dnAhi2P3wC4vRsNl6QJqNGZbU+ypObp0nCLzDwiMzfIzE2oJnf8PjPfCJwGHFivdiDwi/r6acC+ETEtIjYFNgcuHPkTlKTCFJjZ9iRLapwenG/zM8CpEXEQcAuwD0BmXhkRpwJXAYuBQzKzb8xbJ0kNN8a5PSaZbZEsqVmW/9RAne0m8xzgnPr6fGC3Zax3FHBU91skSePUGOR2LzLbIllS8zg1TpLGlwJz2yJZUqMEZf68qSSVqtTctkiW1DwFhq0kFa3A3LZIltQ4kQWmrSQVrMTc9hRwkiRJUht7ksfQd393AQ8tnExff9C/OHjP65712H3/7y238tYP3si+z92Z++5ZoYet1EisMLWfz598BStMTSZPSc797Vqc9JUN2expC3n3/9zAClP76esLvn7kplx72aq9bm4zjdHZLaShfPfsC6vM7gv6+4L3vPaZHHDoTey023z6+4N7F6zAF4/YggVzp/W6qerQsjJ706dWmb3iSn3MvW1FPvf+p/DgA5ZMy1RobnftHY+I7wBLfk5wm27tZ7w5/M3bPqEInrnewzxz53uYe7tBO949uig4/ICtefjByUye0s8XTrmSi/+4Bge891a+f8wGXPynNXnOC+7moA/fwof337rXzW2sEieAjAfm9hMd/qZnLJXZP/72Bpx4zCYAvPKA29jvnbfwtY9v3qPWaaSWldnv+NiNfOszG3P5havzktfO5TVvvZ0Tv7xRr5vbaCXmdjeHWxwP7NnF7Rfj4A/fwHeO3pQCh/NMQMHDD04GYMqUZMoKSSZkwkqrVOczX2nVPubf6dGCQXXpF/c0pOMxtwf10MLH+5ZWnN5vbo97A2f2Bps9zOUXrgbApX9ZnX/bc0EvGzk+FJjZXetJzsw/RcQm3dr+eJQJn/zW5WTCb06dxW9/NIsdXzif+XOncuM1q/S6eRolkyYlx/z8Mp608cP86qT1uOYfq/J/n9yET373at56xM1EJIe97um9bmajldgjMR6Y20vLhE9++3KS4Dc/XI/fnjoLgDe99yZ22/tOFt4/hcMP9P/yeDdQZt907XR22v1uzj9rBrvsNZ+Z6z3S62Y2Xom57QCbMfSB/bdjwV3TWH3GIo761uXMvmE6+77tFj76VkO2JP39wbteuS0rr7qY//rGNWy8+YPste+dHHfUJvzljLXY5aXzeO+n/8VHDtyq101trgLDVuPPB/bblgVz68z+zhXMvmElrrh4db735U343pc34XUH38or3jiH73914143VSMwUGZ/6fCn8I6P3ch+75rN+WevyeJHPc/BkArM7Z6/6xFxcERcHBEXL8qHet2crlpwVzXm+N4FUznv7LV4+nPuZd31H+brP7uE7/7uAmau+wjH/ORS1py5qMct1WhYeP8ULrtgNbZ//j3s/v/u4i9nzADgz6evxZbbPtDj1jVYVj0SnVzUfUtldv/DvW5OVy2ZkHfvgqmcd9ZabPGM+5e6/5xfrc3zXjyvF01TF7Rm9uwbpvPRN2/Foa96Bn/85Uzm3OKcoUEVmtk9L5Iz87jM3D4zt58a03vdnK6ZNr2P6Sstfuz6M597D9desSr77bIzb3nxjrzlxTsy785pHPqaZ3H3vKk9bq06tfqMR1l51ep9njqtj2c+915uvWE68++cytN3vA+A7Xa+j9tuWrGXzZQ6tlRmTyr373ja9D6mr9yS2c+7m5uvXYknbfx4Z86OL5rP7BvL/dyaCJaV2avPeBSAiGTfQ2Zz+snr9bKZ6hGHW4yRNddaxH8ecxUAk6ck5/x6HS45d0aPW6XRtubai/jA569n0iSIScmfT1+LC/+wJgvvm8zb/usmJk9OFj0yiWM+ulmvm9ps46CHQWVbc61F/OfXrgZg8uTknF+tzSXnzuCjx1zF+ps8RCbMvX1FvnbkU3rcUo3EsjJ77wPn8PI33gHAX8+cwZk/XrvHLR0HCsztyC5NzY2Ik4FdgZnAncCRmfntwR6z+pS1c+fV9u5Ke9QcuejRXjdBXXb+Q7/m3r550cljV1lrw9xmr/d1tN8Lvn/YJZm5fUcP1nLn9upTZubOq5jZpcvFi3vdBHXZSDIbOs/tpmd2N89u8YZubVtS4TyvVk+Y25I6VmBuO9xCUuOMhwkdkqTHlZjbFsmSmmWcnGReklQrNLctkiU1TvT3ugWSpOVRYm73/BRwkiRJUtPYkyypeQo8bCdJRSswty2SJTVOiRNAJKlkJea2RbKkZkmKPJWQJBWr0Ny2SJbUOCX2SEhSyUrMbYtkSc1TYNhKUtEKzG2LZEmNEpTZIyFJpSo1ty2SJTVLZpFj2ySpWIXmtudJliRJktrYkyypcUo8bCdJJSsxty2SJTVPgWErSUUrMLctkiU1Tok9EpJUshJz2yJZUrMk0F9g2kpSqQrNbYtkSc1TXtZKUtkKzG2LZEmNU+JhO0kqWYm57SngJEmSpDb2JEtqngJPSi9JRSswty2SJTVOiYftJKlkJea2RbKkZkmKnAAiScUqNLctkiU1SgBR4GE7SSpVqbltkSypefp73QBJ0nIpMLctkiU1Tok9EpJUshJz21PASZIkSW3sSZbULIVOAJGkYhWa2xbJkhomizzfpiSVq8zcdriFpMaJ7Owy5HYjNoyIP0TE1RFxZUS8p14+IyJ+FxHX1f+u2fKYIyLi+oi4JiL26N6zlqTxq8TMtkiW1DyZnV2Gthg4LDOfBuwEHBIRWwGHA2dn5ubA2fVt6vv2BbYG9gSOjYjJXXjGkjS+FZjZFsmSmiUh+ju7DLnpzDmZeWl9/X7gamB9YG/ghHq1E4BX1df3Bk7JzEcy80bgemCHUX2+kjTedZjbQ262x5ltkSypebrXk/yYiNgEeCZwAbBuZs6pdp1zgHXq1dYHbm152Ox6mSSpVYGZ7cQ9SSWZGREXt9w+LjOPa18pIlYBfgK8NzPvi4hlbW+gO8qbnSJJvdHozLZIltQ8nZeh8zJz+8FWiIgVqML2+5n503rxnRExKzPnRMQsYG69fDawYcvDNwBu77h1klSqznK70ZntcAtJjROZHV2G3G7V/fBt4OrM/GLLXacBB9bXDwR+0bJ834iYFhGbApsDF47aE5WkQpSY2fYkS2qe7p1v83nAAcDlEfH3etlHgM8Ap0bEQcAtwD5VM/LKiDgVuIpqlvUhmdnXrcZJ0rjVndzuaWZbJEtqlgSGMeu5o01nnsvAY9YAdlvGY44CjupOiySpAF3K7V5ntkWypEYJhncYTpLUDKXmtkWypOYpMGwlqWgF5rZFsqTmKTBsJaloBea2Z7eQJEmS2tiTLKlZujhxT5LUBYXmtkWypMYpcQKIJJWsxNy2SJbUPAWGrSQVrcDctkiW1DBZZNhKUrnKzG2LZEnNkhQZtpJUrEJz2yJZUvMUOAFEkopWYG57CjhJkiSpjT3JkhqnxFnSklSyEnPbIllS8xQYtpJUtAJz2yJZUrMk0F9e2EpSsQrNbYtkSQ1T5qmEJKlcZea2RbKk5ikwbCWpaAXmtkWypOYpMGwlqWgF5rangJMkSZLa2JMsqVkKnQAiScUqNLcbVSTf1zdv3hl3f/vmXrdjDM0E5vW6Eeq6ifg+b9z5QxOywJ9uKtB9ffPnnXHvdyZSZsPE/P880UzE93gEmQ2l5najiuTMXLvXbRhLEXFxZm7f63aou3yfO1Dg2LYSTbTMBv8/TwS+xx0qMLcbVSRLUqmH7SSpWIXmtkWypOYpsEdCkopWYG5bJPfWcb1ugMaE7/PyKjBsVQz/P5fP97gTBea2RXIPZab/EScA3+flVeYvN6kM/n8un+9xJ8rMbc+TLEmSJLWxSO6RiNgzIq6JiOsj4vBet0ejLyK+ExFzI+KKXrdlXEmgv7+zi9QlZnb5zOwR6DS3G84iuQciYjLwdWAvYCvgDRGxVW9bpS44Htiz140YlzI7u0hdYGZPGMdjZneuwMy2SO6NHYDrM/OGzFwEnALs3eM2aZRl5p+ABb1ux7hkkaxmMbMnADN7hArMbCfu9cb6wK0tt2cDO/aoLVLDZJHn29S4ZmZLgyozty2SeyMGWFbeX5fUiYQs8OdNNa6Z2dJgCs1ti+TemA1s2HJ7A+D2HrVFap4CeyQ0rpnZ0lAKzG3HJPfGRcDmEbFpREwF9gVO63GbJEkDM7OlCcgiuQcyczHwLuAM4Grg1My8sret0miLiJOB84AtI2J2RBzU6zaNG07cU4OY2RODmT1CBWa2wy16JDNPB07vdTvUPZn5hl63YVzKHBfnz9TEYmaXz8wegUJz2yJZUvOMgx4GSVKLAnPbIllS42SBPRKSVLISc9siWVLDjI+xapKkJcrMbYtkSc2SFHkqIUkqVqG57dktJEmSpDYWyYWLiL6I+HtEXBERP4qIlUawreMj4rX19W9FxFaDrLtrRDy3g33cFBEzh7u8bZ0HlnNfH4+IDyxvGzUGsr+zizTOmdmDrm9mN1mBmW2RXL6HMnO7zNwGWAS8vfXOiJjcyUYz862ZedUgq+wKLHfgSglkf3Z0kQpgZmvc6TS3m84ieWL5M/CUusfgDxHxA+DyiJgcEZ+PiIsi4rKIeBtAVL4WEVdFxK+BdZZsKCLOiYjt6+t7RsSlEfGPiDg7IjahCvb31T0iu0TE2hHxk3ofF0XE8+rHrhURZ0bE3yLi/4AY6klExM8j4pKIuDIiDm677+i6LWdHxNr1sidHxG/rx/w5Ip46Kq+muiPTnmSpYmab2eNDp7ndcE7cmyAiYgqwF/DbetEOwDaZeWMdWvdm5nMiYhrwl4g4E3gmsCXwdGBd4CrgO23bXRv4JvD8elszMnNBRPwv8EBmfqFe7wfAlzLz3IjYiOqXq54GHAmcm5mfiIiXAUsF6DL8e72P6cBFEfGTzJwPrAxcmpmHRcTH6m2/CzgOeHtmXhcROwLHAi/q4GXUGBkPPQxSN5nZZvZ4U2JuWySXb3pE/L2+/mfg21SH1C7MzBvr5S8BnhH12DVgdWBz4PnAyZnZB9weEb8fYPs7AX9asq3MXLCMduwObBXxWKfDahGxar2P/1c/9tcRcfcwntOhEfHq+vqGdVvnA/3AD+vlJwE/jYhV6uf7o5Z9TxvGPtRL46CHQeoSM9vMHp8KzG2L5PI9lJnbtS6og2dh6yLg3Zl5Rtt6L6UaajSYGMY6UA3t2TkzHxqgLcP++hkRu1KF986Z+WBEnAOsuIzVs97vPe2vgZrrfu4+46z88aATfgYxb1QbI409M9vMHndGkNuNzmzHJAuqw2jviIgVACJii4hYGfgTsG89/m0W8MIBHnse8IKI2LR+7Ix6+f3Aqi3rnUl1GI16ve3qq38C9q+X7QWsOURbVwfursP2qVS9IktMApb0rOxHdUjwPuDGiNin3kdExLZD7EM9lJl7Zub2HV727HX7pTFgZqtRRpDbjc5si2QBfItq7NqlEXEF8H9URxl+BlwHXA58A/hj+wMz8y6qMWk/jYh/8Pihs18Cr14yCQQ4FNg+qkkmV/H4jO3/Bp4fEZdSHUK8ZYi2/haYEhGXAf8DnN9y30Jg64i4hGr82ifq5fsDB9XtuxLYexiviSQ1lZktjYHIAn9GUJIkSRoJe5IlSZKkNhbJkiRJUhuLZEmSJKmNRbIkSZLUxiJZkiRJamORLEmSJLWxSJYkSZLaWCRLkiRJbf4/l87gsCRIAtIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x864 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(12, 12))\n",
    "i, j = 0, 0\n",
    "for clf in best_clfs:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    clf.fit(X_train_sub[clf_name], y_train)\n",
    "    disp = ConfusionMatrixDisplay.from_estimator(clf, X_test_sub[clf_name], y_test, ax=ax[i][j])\n",
    "    disp.ax_.set_title(f\"Confusion Matrix: {clf_name}\")    \n",
    "    j += 1\n",
    "    if j == 2:\n",
    "        i += 1\n",
    "        j = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "963e329d",
   "metadata": {},
   "source": [
    "The `DecisionTreeClassifier` has the highest number of True Positives (42) of all 4 models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5dfa77d",
   "metadata": {},
   "source": [
    "Next, we can perform 10-fold stratified cross-validation for a more robust evaluation. As mentioned earlier, this has the following advantages:\n",
    "- Stratification is the process of rearranging the data so as to ensure that each fold is a good representative of the whole.\n",
    "- Cross-validation can flag problems such as selection bias and reduce overfitting chances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c0c78ef0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(criterion='entropy')\n",
      "Average F1: 0.617 +/- 0.057\n",
      "-----\n",
      "GaussianNB()\n",
      "Average F1: 0.560 +/- 0.081\n",
      "-----\n",
      "SVC(kernel='poly')\n",
      "Average F1: 0.587 +/- 0.062\n",
      "-----\n",
      "KNeighborsClassifier(n_neighbors=15)\n",
      "Average F1: 0.607 +/- 0.072\n",
      "-----\n"
     ]
    }
   ],
   "source": [
    "for clf in best_clfs:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    ts = cross_val_score(clf, X[wrapper_subsets[clf_name]], y, cv=cv_strat, n_jobs=-1, scoring=\"f1\")\n",
    "    print(f\"{clf}\\nAverage F1: {ts.mean():.3f} +/- {ts.std():.3f}\\n-----\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ba69dec",
   "metadata": {},
   "source": [
    "As shown above, the **Decision Tree** classifier (with default hyperparameter values for `max_depth` and `min_samples_leaf`) using the feature subset obtained using sequential search (wrapper) yields the best result (based on average F1 score)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "633e4f8c",
   "metadata": {},
   "source": [
    "## 1D: ROC Analysis\n",
    "\n",
    "An ROC (Receiver Operating Characteristic) curve plots the **sensitivity versus specificity** for a binary classifier system as its discrimination threshold is varied. First, a dummy classifier must be defined, which acts as a baseline and always predicts the majority class without learning anything from the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "caeebcaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DummyClassifier(strategy='most_frequent')\n",
      "Accuracy: 0.926\n",
      "F1: 0.000\n"
     ]
    }
   ],
   "source": [
    "dummy_clf = DummyClassifier(strategy=\"most_frequent\")\n",
    "dummy_clf.fit(X_train, y_train)\n",
    "print(dummy_clf)\n",
    "print(f\"Accuracy: {dummy_clf.score(X_test, y_test):.3f}\")\n",
    "print(f\"F1: {f1_score(y_test, dummy_clf.predict(X_test)):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "609e4e71",
   "metadata": {},
   "source": [
    "The high accuracy and 0 F1 score reflects the class imbalance issue in the dataset. Next, ROC curves can be plotted for target class = 1 with each of the chosen classifiers. The area under the curve (AUROC/AUC) is used to compare the models, since  in general, higher the AUC, higher is the model's capability of distinguishing between the 1 and 0 target classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "06359410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = RocCurveDisplay.from_estimator(dummy_clf, X_test, y_test, pos_label=1, linestyle=\"--\")\n",
    "for clf in [dt_best, nb_best, svm_best, knn_best]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    disp = RocCurveDisplay.from_estimator(clf, X_test_sub[clf_name], y_test, pos_label=1, ax=disp.ax_)\n",
    "\n",
    "disp.ax_.grid()\n",
    "disp.figure_.set_size_inches(9, 8)\n",
    "disp.ax_.set_title(\"ROC AUC Curves\", fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d61854",
   "metadata": {},
   "source": [
    "From the ROC curves above, we learn that the `DecisionTreeClassifier` using entropy as criterion has the maximum area under the curve (`AUC`), implying the greatest ability to separate the target classes. The performance is satisfactory, as a score of 0.85 is quite close to perfect score of 1. It is considerably better than the dummy classifier that always predicts the majority class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0409b633",
   "metadata": {},
   "source": [
    "## 1E: Discussion of Results\n",
    "\n",
    "The evaluation in both part C and D suggest that the `DecisionTreeClassifier(criterion='entropy')` is best suited for the given classification task on animal shelter data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2bcf2832",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_clf = dt_best\n",
    "clf_name = best_clf.__class__.__name__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "74316018",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(classifier, X: pd.DataFrame, y: pd.Series) -> None:\n",
    "    res = cross_validate(classifier, X, y, cv=cv_strat, scoring=[\"f1\", \"roc_auc\"], n_jobs=-1)\n",
    "    f1, auc = res[\"test_f1\"], res[\"test_roc_auc\"]\n",
    "    print(f\"{classifier}\\n{cv_strat}\\n\")\n",
    "    print(f\"Average F1: {f1.mean():.3f} +/- {f1.std():.3f}\")\n",
    "    print(f\"Average ROC AUC: {auc.mean():.3f} +/- {auc.std():.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4105c2c",
   "metadata": {},
   "source": [
    "We can prove that the performance of the classifier increases after feature selection:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a9fff38a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(criterion='entropy')\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "\n",
      "Average F1: 0.571 +/- 0.056\n",
      "Average ROC AUC: 0.774 +/- 0.034\n"
     ]
    }
   ],
   "source": [
    "# original dataset\n",
    "evaluate(best_clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e2ba92a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(criterion='entropy')\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "\n",
      "Average F1: 0.618 +/- 0.057\n",
      "Average ROC AUC: 0.806 +/- 0.045\n"
     ]
    }
   ],
   "source": [
    "# utilise feature subset selection\n",
    "evaluate(best_clf, X[wrapper_subsets[clf_name]], y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d04754f",
   "metadata": {},
   "source": [
    "Using a familiar stratified 10-fold cross-validation strategy, both the average F1 and ROC AUC scores for the chosen classifier are higher when using the feature subset selected using sequential search (wrapper technique), compared to original dataset with all features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5107e50f",
   "metadata": {},
   "source": [
    "- The relative performance of the 4 different classifiers is more or less in line with expectations. Decision trees often perform well on imbalanced datasets as they work by learning a hierarchy of questions which forces both classes to be addressed.\n",
    "- Looking at the average F1 score in part C, the Naive Bayes classifier performed quite poorly. Although Naive Bayes can usually overcome this problem in real-world scenarios, the decreased scores are possibly a result of the basic underlying assumption of conditional independence failing for the given dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efa32cdb",
   "metadata": {},
   "source": [
    "### Ensemble methods\n",
    "\n",
    "Additionally, we can take a look at ensemble methods for classification, which combine the predictions from multiple base classifiers to improve generalisability and robustness. There are two main types of ensembles: bagging (e.g. Random Forest) and boosting (e.g. AdaBoost, Gradient Tree Boosting).\n",
    "\n",
    "The three ensemble methods mentioned below all use a Decision Tree as the base estimator.\n",
    "\n",
    "- `RandomForestClassifier`: \"fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting\".\n",
    "- `AdaBoostClassifier`: \"fits a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases\".\n",
    "- `GradientBoostingClassifier`: \"additive model in a forward stage-wise fashion; it allows for the optimisation of arbitrary differentiable loss functions\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7f23ac77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(criterion='entropy')\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "\n",
      "Average F1: 0.580 +/- 0.061\n",
      "Average ROC AUC: 0.843 +/- 0.039\n",
      "\n",
      "-----\n",
      "\n",
      "AdaBoostClassifier(n_estimators=100)\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "\n",
      "Average F1: 0.595 +/- 0.056\n",
      "Average ROC AUC: 0.875 +/- 0.037\n",
      "\n",
      "-----\n",
      "\n",
      "GradientBoostingClassifier()\n",
      "StratifiedKFold(n_splits=10, random_state=0, shuffle=True)\n",
      "\n",
      "Average F1: 0.601 +/- 0.055\n",
      "Average ROC AUC: 0.884 +/- 0.032\n",
      "\n",
      "-----\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(criterion=\"entropy\")\n",
    "ab = AdaBoostClassifier(n_estimators=100)\n",
    "gb = GradientBoostingClassifier()\n",
    "\n",
    "for clf in [rf, ab, gb]:\n",
    "    evaluate(clf, X, y)\n",
    "    print(\"\\n-----\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "932bde9e",
   "metadata": {},
   "source": [
    "As shown above, the ROC AUC scores for the ensemble methods are higher than those obtained from simple estimators such as Decisiont Trees, Naive Bayes, SVM and k-NN explored earlier."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1fc577f",
   "metadata": {},
   "source": [
    "## 2: Machine Learning Concepts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e64b7e9",
   "metadata": {},
   "source": [
    "a) Consider the nightmare situation in which you struggled hard to obtain a very high accuracy (>95%) on your training data for a binary classification task, but when your client ran it on their test data, the accuracy was very low (<50%). This is despite the fact that your dataset is reasonably balanced (majority class < 65%) and you are using a fairly complex learning algorithms with many parameters to fit your dataset. How do you explain this situation? What are the possible causes for this? How can you improve the testing accuracy in this situation? What precautions should you take in your evaluation procedure to avoid this situation?\n",
    "\n",
    "**Ans.**\n",
    "\n",
    "- By definition, this is a case of **overfitting**, as the classification model performs very well on training data but fails to impress on unseen testing data.\n",
    "- Possible reasons:\n",
    "  - Data cleaning and feature subset selection was not performed to an adequate degree, and the classifier learned excessive noise in the dataset, thus reducing generalisation accuracy of the model. \n",
    "  - It might even be the case that there were more features than observations in the data, and/or the size of the training data was not sufficiently large, especially relative to the number of predictors.\n",
    "  - The selected classification model has high variance. It is important to consider the bias-variance trade-off when selecting an appropriate classifier.\n",
    "  - A simple hold-out strategy was used to split the training and testing data while training the model, which possibly led to an unfortunate split, due to which training and testing data were inherently different.\n",
    "- Improving testing accuracy and taking precautions during evaluation:\n",
    "  - Use a cross-validation strategy (e.g. K-fold) instead of simple train-test splits.\n",
    "  - Consider using ensemble classification techniques which reduce the chances of overfitting by combining a large number of base models.\n",
    "  - Consider the costs of False Positive and False Negative errors and try other appropriate metrics for evaluation, such as precision, recall, F1 score, ROC AUC, and so on, depending on type of data and the goal of classification."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "032e19c7",
   "metadata": {},
   "source": [
    "b) What is the kernel trick in SVM? What is the mathematical reasoning that makes it work? Why is the kernel trick important? Give some examples of the kernels together with the situation when you should use it.\n",
    "\n",
    "**Ans.**\n",
    "\n",
    "- The basic Support Vector Machine (SVM) algorithm is a convex optimisation problem which aims to maximise the margin of a line that perfectly separates two classes in a binary classification problem.\n",
    "- By applying a transformation to input data, an SVM classifier is able to map the points to a higher dimension.\n",
    "- A decision boundary to separate the classes can then be found (hyperplane in the higher dimensional space). This, however, is not the kernel trick.\n",
    "- The problem arises when there are a large number of features in our data, which will drastically increase the computational time/cost to apply the kernel transformation.\n",
    "- The **kernel trick** is that kernel functions have a special mathematical property that a function can take as input the original feature vectors and return the dot product of feature vectors in the higher dimensional space.\n",
    "\n",
    "$$Kernel \\; K(x, x') = \\phi(x) . \\phi(x')$$\n",
    "\n",
    "- If the dot products are known, it is no longer necessary to actually apply the computationally intensive transformations $\\phi(x)$ to the higher dimension!\n",
    "- The trick is important because in real-world scenarios, it is nearly impossible to find linearly separable classes in a classification task. Kernel SVM is able to solve a variety of complex classification tasks where the classes are far from linearly separable, by looking for a maximal margin hyperplane in higher dimensions.\n",
    "- Examples of kernels:\n",
    "\n",
    "| Name | Function | Use Case |\n",
    "| --- | --- | --- |\n",
    "| Linear | `x.x'` | Data is linearly separable, especially with a large number of features (e.g. text classification) | \n",
    "Polynomial | `(1 + x.x')^t`  | Polynomial model with boundary of defined but arbitrary order |\n",
    "| RBF (Radial Basis Function) - default in `scikit-learn` | $e^{- \\gamma ||x - x'||^2}$ | General purpose |\n",
    "| Sigmoid | `tanh (1 + x.x')` | Proxy for neural networks | \n",
    "\n",
    "\n",
    "- Note:\n",
    "  - On trying out different kernels (`rbf, poly, linear, sigmoid` on the Labeled Faces in the Wild (LFW) people dataset (`fetch_lfw_people() in sklearn.datasets`), it turns out that `rbf` is the best option. RBF works well in practice and it is relatively easy to tune.\n",
    "  \n",
    "  ```\n",
    "  param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],\n",
    "              'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], \n",
    "              'kernel': ['rbf', 'poly', 'linear', 'sigmoid']\n",
    "             }\n",
    "  clf = GridSearchCV(SVC(class_weight='balanced'), param_grid)\n",
    "  ```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a953985",
   "metadata": {},
   "source": [
    "c) What is the curse of dimensionality? Give a mathematical intuition as to why a k-nearest neighbour classifier may struggle on a very high-dimensional data. Give examples of various situations in which you would want to reduce the number of features you consider for classification.\n",
    "\n",
    "**Ans.**\n",
    "\n",
    "- The **curse of dimensionality** is the argument against the false intuition that performance of machine learning algorithms keeps increasing with the number of predictor features available.\n",
    "- In the extreme case where there are more features (columns) than observations (rows), the model will be prone to severe overfitting and will not generalise well for unseen data.\n",
    "- After a certain point, increasing the number of features actually affects the model significantly, as a lot of noise in the dataset is being learned instead of the core/important features.\n",
    "- K-nearest neighbour classifiers perform well with a small number of features. However, when the feature set size increases beyond a certain threshold, the volume of input space increases at an exponential rate (considering each feature to be a dimension in an N-dimensional input space). As k-NN is dependent on computing the distances between points in the feature space, these distances keep increasing in higher dimensions, consequently reducing the performance of a k-NN classifier.\n",
    "- There are a number of scenarios where dimension/feature reduction is desirable:\n",
    "  - Primary reason: the performance of machine learning algorithms often degrades with high dimensionality, apart from the expected increase in computational time.\n",
    "  - A model with a limited number of features is easier to explain to clients, as opposed to a \"black box\" with a large number of features which cannot be interpreted properly.\n",
    "  - Domain-specific costs: when there is a significant financial cost associated with data generation/collection, we cannot afford to run a large number of experiments, and cost-benefit analysis must be performed to optimise the number of features required to build a well-performing classifier subject to budget constraints."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b7ea16",
   "metadata": {},
   "source": [
    "d) You are working on a classification task to separate fraudulent transactions from the normal financial transactions. What evaluation measure(s) will you use for this application and why? What measure will you not use for this application and why?\n",
    "\n",
    "**Ans.**\n",
    "\n",
    "- When working on fraud detection tasks, it is important note the inherent **class imbalance** present in the data: the vast majority of transactions are legitimate/normal, and very few are actually fraudulent.\n",
    "- Due to this, a dummy classifier which always predicts the majority class may end up being over 99% accurate. This suggests that *accuracy* (or even the complementary metric: *classification error*) is not a good evaluation metric for such cases.\n",
    "- Instead, we should plot a *Confusion Matrix* which shows the following: TP (True Positive), FN (False Negative), TN (True Negative) and FN (False Negative), and consider alternative metrics like:\n",
    "  - Precision: TP / (TP+FP)\n",
    "  - Recall (Sensitivity): TP / (TP+FN)\n",
    "  - F1 score: 2 * Precision * Recall / (Precision + Recall)\n",
    "- When trying to separate a fraudulent transaction (class = 1) from a normal one (class = 0), the cost of False Negative (wrongly predict a fraudulent transaction to be genuine) is far greater than raising false alarms with False Positive errors. Hence, *recall* is the primary measure that we should consider for the given classification task.\n",
    "- Another interesting metric is the ROC Curve and AUC (area under the ROC curve) that plots the TP rate against the FP rate and is a rank based metric, but can be optimistic under a severe class imbalance."
   ]
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