{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:green;float:right\">Homepage: https://spkit.github.io</span>\n",
    "<br><span style=\"color:green;float:right\">Nikesh Bajaj : http://nikeshbajaj.in</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DecisionTrees without converting Catogorical Features using SpKit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Most of ML libraries force us to convert the catogorycal features into one-hot vector or any numerical value. However, it should not be the case, **Not atleast with Decision Trees**, due a simple reason, of how decision tree works. In **spkit library**, Decision tree can handle mixed type input features, 'Catogorical' and 'Numerical'. In this notebook, I would use a dataset *hurricNamed* from *vincentarelbundock* github repository, and use only a few features, mixed of catogorical and numerical features. Converting number of deaths to binary with threshold of 10, we handle this as Classification Problem. \n",
    "\n",
    "However, it is not shown that coverting features into one-hot vector or any label encoder affects the performance of model, but, it is useful, when you need to visulize the decision process. Very important when you need to extract and simplify the decision rule."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Libraries-and-Dataset\" data-toc-modified-id=\"Libraries-and-Dataset-0.1\"><span class=\"toc-item-num\">0.1&nbsp;&nbsp;</span>Libraries and Dataset</a></span></li></ul></li><li><span><a href=\"#Classification----binary-class---hurricNamed-Dataset\" data-toc-modified-id=\"Classification----binary-class---hurricNamed-Dataset-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Classification  - binary class - hurricNamed Dataset</a></span></li><li><span><a href=\"#Training,\" data-toc-modified-id=\"Training,-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Training,</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libraries and Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "#from sklearn.model_selection import train_test_split\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(100) # just to ensure the reproducible results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.0.9'"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import spkit\n",
    "spkit.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification  - binary class - hurricNamed Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "from spkit.ml import ClassificationTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = pd.read_csv('https://vincentarelbundock.github.io/Rdatasets/csv/DAAG/hurricNamed.csv')\n",
    "feature_names = ['Name', 'AffectedStates','LF.WindsMPH','mf']#,'BaseDamage' 'Year','Year','LF.WindsMPH', 'LF.PressureMB','LF.times',\n",
    "\n",
    "\n",
    "X = np.array(D[feature_names])\n",
    "X[:,1] = [st.split(',')[0] for st in X[:,1]] #Choosing only first name of state from AffectedStates feature\n",
    "y = np.array(D[['deaths']])[:,0]\n",
    "\n",
    "# Converting target into binary with threshold of 10 deaths\n",
    "y[y<10] =0\n",
    "y[y>=10]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([['Easy', 'FL', 120, 'f'],\n",
       "        ['King', 'FL', 130, 'm'],\n",
       "        ['Able', 'SC', 85, 'm'],\n",
       "        ['Barbara', 'NC', 85, 'f'],\n",
       "        ['Florence', 'FL', 85, 'f'],\n",
       "        ['Carol', 'NC', 120, 'f'],\n",
       "        ['Edna', 'MA', 120, 'f'],\n",
       "        ['Hazel', 'SC', 145, 'f'],\n",
       "        ['Connie', 'NC', 120, 'f'],\n",
       "        ['Diane', 'NC', 85, 'f'],\n",
       "        ['Ione', 'NC', 120, 'm'],\n",
       "        ['Flossy', 'LA', 105, 'f'],\n",
       "        ['Audrey', 'TX', 145, 'f'],\n",
       "        ['Helene', 'NC', 120, 'f'],\n",
       "        ['Debra', 'TX', 85, 'f'],\n",
       "        ['Gracie', 'SC', 120, 'f'],\n",
       "        ['Donna', 'FL', 145, 'f'],\n",
       "        ['Ethel', 'MS', 85, 'f'],\n",
       "        ['Carla', 'TX', 145, 'f'],\n",
       "        ['Cindy', 'TX', 85, 'f'],\n",
       "        ['Cleo', 'FL', 105, 'f'],\n",
       "        ['Dora', 'FL', 105, 'f'],\n",
       "        ['Hilda', 'LA', 120, 'f'],\n",
       "        ['Isbell', 'FL', 105, 'f'],\n",
       "        ['Betsy', 'FL', 120, 'f'],\n",
       "        ['Alma', 'FL', 105, 'f'],\n",
       "        ['Inez', 'FL', 85, 'f'],\n",
       "        ['Beulah', 'TX', 120, 'f'],\n",
       "        ['Gladys', 'FL', 105, 'f'],\n",
       "        ['Camille', 'LA', 190, 'f'],\n",
       "        ['Celia', 'TX', 120, 'f'],\n",
       "        ['Fern', 'TX', 85, 'f'],\n",
       "        ['Edith', 'LA', 105, 'f'],\n",
       "        ['Ginger', 'NC', 85, 'f'],\n",
       "        ['Agnes', 'FL', 85, 'f'],\n",
       "        ['Carmen', 'LA', 120, 'f'],\n",
       "        ['Eloise', 'FL', 120, 'f'],\n",
       "        ['Belle', 'NY', 85, 'f'],\n",
       "        ['Babe', 'LA', 85, 'f'],\n",
       "        ['Bob', 'LA', 85, 'm'],\n",
       "        ['David', 'FL', 105, 'm'],\n",
       "        ['Frederic', 'AL', 120, 'm'],\n",
       "        ['Allen', 'TX', 115, 'm'],\n",
       "        ['Alicia', 'TX', 115, 'f'],\n",
       "        ['Diana', 'NC', 110, 'f'],\n",
       "        ['Bob', 'SC', 75, 'm'],\n",
       "        ['Danny', 'LA', 90, 'm'],\n",
       "        ['Elena', 'MS', 115, 'f'],\n",
       "        ['Gloria', 'NC', 120, 'f'],\n",
       "        ['Juan', 'LA', 85, 'm'],\n",
       "        ['Kate', 'FL', 100, 'f'],\n",
       "        ['Bonnie', 'TX', 85, 'f'],\n",
       "        ['Charley', 'NC', 75, 'm'],\n",
       "        ['Floyd', 'FL', 75, 'm'],\n",
       "        ['Florence', 'LA', 80, 'f'],\n",
       "        ['Chantal', 'TX', 80, 'f'],\n",
       "        ['Hugo', 'SC', 140, 'm'],\n",
       "        ['Jerry', 'TX', 85, 'm'],\n",
       "        ['Bob', 'RI', 105, 'm'],\n",
       "        ['Andrew', 'FL', 170, 'm'],\n",
       "        ['Emily', 'NC', 115, 'f'],\n",
       "        ['Erin', 'FL', 100, 'f'],\n",
       "        ['Opal', 'FL', 115, 'f'],\n",
       "        ['Bertha', 'NC', 105, 'f'],\n",
       "        ['Fran', 'NC', 115, 'f'],\n",
       "        ['Danny', 'LA', 80, 'm'],\n",
       "        ['Bonnie', 'NC', 110, 'f'],\n",
       "        ['Earl', 'FL', 80, 'm'],\n",
       "        ['Georges', 'FL', 105, 'm'],\n",
       "        ['Bret', 'TX', 115, 'm'],\n",
       "        ['Floyd', 'NC', 105, 'm'],\n",
       "        ['Irene', 'FL', 80, 'f'],\n",
       "        ['Lili', 'LA', 90, 'f'],\n",
       "        ['Claudette', 'TX', 90, 'f'],\n",
       "        ['Isabel', 'NC', 105, 'f'],\n",
       "        ['Alex', 'NC', 80, 'm'],\n",
       "        ['Charley', 'FL', 150, 'm'],\n",
       "        ['Gaston', 'SC', 75, 'm'],\n",
       "        ['Frances', 'FL', 105, 'f'],\n",
       "        ['Ivan', 'AL', 120, 'm'],\n",
       "        ['Jeanne', 'FL', 120, 'f'],\n",
       "        ['Cindy', 'LA', 75, 'f'],\n",
       "        ['Dennis', 'FL', 120, 'm'],\n",
       "        ['Katrina', 'LA', 125, 'f'],\n",
       "        ['Ophelia', 'NC', 75, 'f'],\n",
       "        ['Rita', 'LA', 115, 'f'],\n",
       "        ['Wilma', 'FL', 120, 'f'],\n",
       "        ['Humberto', 'TX', 90, 'm'],\n",
       "        ['Dolly', 'TX', 85, 'f'],\n",
       "        ['Gustav', 'LA', 105, 'm'],\n",
       "        ['Ike', 'TX', 110, 'm'],\n",
       "        ['Irene', 'NC', 75, 'f'],\n",
       "        ['Isaac', 'LA', 80, 'm'],\n",
       "        ['Sandy', 'NY', 75, 'f']], dtype=object),\n",
       " array([0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "        1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1,\n",
       "        0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1,\n",
       "        0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "        0, 1, 1, 1, 0, 1], dtype=int64))"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X,y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training, "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not doing training and testing, as objective is to show that it works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = ClassificationTree(max_depth=4)\n",
    "clf.fit(X,y,feature_names=feature_names)\n",
    "yp = clf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "clf.plotTree()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, it can be seen that first feature 'LF.WindsMPH' is a numerical, thus the threshold is greater then equal to, however for catogorical features like 'Name' and 'AffectedStates'threshold is equal to only."
   ]
  }
 ],
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