DecisionTree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/home/atul/Downloads/weather.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>day</th>\n",
       "      <th>outlook</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind</th>\n",
       "      <th>play</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D1</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Hot</td>\n",
       "      <td>High</td>\n",
       "      <td>Weak</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D2</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Hot</td>\n",
       "      <td>High</td>\n",
       "      <td>Strong</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D3</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>Hot</td>\n",
       "      <td>High</td>\n",
       "      <td>Weak</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D4</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Mild</td>\n",
       "      <td>High</td>\n",
       "      <td>Weak</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>D5</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Cool</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Weak</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>D6</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Cool</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Strong</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>D7</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>Cool</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Strong</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>D8</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Mild</td>\n",
       "      <td>High</td>\n",
       "      <td>Weak</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>D9</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Cool</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Weak</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D10</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Mild</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Weak</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>D11</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Mild</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Strong</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>D12</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>Mild</td>\n",
       "      <td>High</td>\n",
       "      <td>Strong</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>D13</td>\n",
       "      <td>Overcast</td>\n",
       "      <td>Hot</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Weak</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>D14</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Mild</td>\n",
       "      <td>High</td>\n",
       "      <td>Strong</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    day   outlook  temp humidity    wind play\n",
       "0    D1     Sunny   Hot     High    Weak   No\n",
       "1    D2     Sunny   Hot     High  Strong   No\n",
       "2    D3  Overcast   Hot     High    Weak  Yes\n",
       "3    D4      Rain  Mild     High    Weak  Yes\n",
       "4    D5      Rain  Cool   Normal    Weak  Yes\n",
       "5    D6      Rain  Cool   Normal  Strong   No\n",
       "6    D7  Overcast  Cool   Normal  Strong  Yes\n",
       "7    D8     Sunny  Mild     High    Weak   No\n",
       "8    D9     Sunny  Cool   Normal    Weak  Yes\n",
       "9   D10      Rain  Mild   Normal    Weak  Yes\n",
       "10  D11     Sunny  Mild   Normal  Strong  Yes\n",
       "11  D12  Overcast  Mild     High  Strong  Yes\n",
       "12  D13  Overcast   Hot   Normal    Weak  Yes\n",
       "13  D14      Rain  Mild     High  Strong   No"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(columns=[\"day\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fraction_df(df,target_attribute_name):\n",
    "    fraction_class=[]\n",
    "    for i in df[target_attribute_name].unique():\n",
    "        fraction_class.append((df[target_attribute_name]==i).sum()/len(df))\n",
    "    return(fraction_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def entropy_S(df,target_attribute_name):\n",
    "    fraction_class=fraction_df(df,target_attribute_name)\n",
    "    entropy=0\n",
    "    total_count=len(df)\n",
    "    for i in fraction_class:\n",
    "        entropy=entropy-i*math.log2(i)\n",
    "    return(entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def entropy(df,node_attribute,target_attribute_name): #entropy for a particular node\n",
    "    ent=[]\n",
    "    for i in df[node_attribute].unique():\n",
    "        part_df=df[df[node_attribute]==i]\n",
    "        ent.append(entropy_S(part_df,target_attribute_name))\n",
    "    return(ent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gain(class_fraction,entropy_data,ent_arr):\n",
    "    return(entropy_data-np.dot(class_fraction,ent_arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_decision_tree=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decision_tree(df,target_attribute_name):\n",
    "    gain_list={}\n",
    "    nodes = []\n",
    "    attr_list =list(df.columns)\n",
    "    attr_list.remove(target_attribute_name)\n",
    "    entropy_data = entropy_S(df,target_attribute_name)\n",
    "    for i in attr_list:\n",
    "        entropy_arr=entropy(df,i,target_attribute_name)\n",
    "        class_fraction=fraction_df(df,i)\n",
    "        gain_list[i]=gain(class_fraction,entropy_data,entropy_arr)\n",
    "    nodes.append(max(gain_list, key=gain_list.get))\n",
    "    splitpt = nodes[0]\n",
    "    lst_decision_tree.append(splitpt)\n",
    "    for j in df[splitpt].unique():\n",
    "        part_df = df[df[splitpt]==j]\n",
    "        lst_decision_tree.append(j)\n",
    "        if(len(part_df[target_attribute_name].unique())>1):\n",
    "            part_df = part_df.drop(splitpt,axis=1)\n",
    "            decision_tree(part_df,target_attribute_name)\n",
    "            \n",
    "        else:\n",
    "            lst_decision_tree.append(part_df[target_attribute_name].unique()[0])\n",
    "    return(lst_decision_tree)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['outlook',\n",
       " 'Sunny',\n",
       " 'humidity',\n",
       " 'High',\n",
       " 'No',\n",
       " 'Normal',\n",
       " 'Yes',\n",
       " 'Overcast',\n",
       " 'Yes',\n",
       " 'Rain',\n",
       " 'wind',\n",
       " 'Weak',\n",
       " 'Yes',\n",
       " 'Strong',\n",
       " 'No']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree(df,'play')"
   ]
  }
 ],
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