Decision-TreeGI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the required libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm         Species\n",
       "0              5.1           3.5            1.4           0.2     Iris-setosa\n",
       "1              4.9           3.0            1.4           0.2     Iris-setosa\n",
       "2              4.7           3.2            1.3           0.2     Iris-setosa\n",
       "3              4.6           3.1            1.5           0.2     Iris-setosa\n",
       "4              5.0           3.6            1.4           0.2     Iris-setosa\n",
       "..             ...           ...            ...           ...             ...\n",
       "145            6.7           3.0            5.2           2.3  Iris-virginica\n",
       "146            6.3           2.5            5.0           1.9  Iris-virginica\n",
       "147            6.5           3.0            5.2           2.0  Iris-virginica\n",
       "148            6.2           3.4            5.4           2.3  Iris-virginica\n",
       "149            5.9           3.0            5.1           1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data \n",
    "df = pd.read_csv('Iris.csv')\n",
    "df=df.drop(columns=['Id'],axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_gini_index(dataset,feature,label):\n",
    "    dictionary = {}\n",
    "    i = 0\n",
    "    for values in dataset[feature]:\n",
    "#         print(values)\n",
    "        dictionary[values] = []\n",
    "    if not dictionary:\n",
    "        return 0\n",
    "    for values in dataset[feature]:\n",
    "#         print(values)\n",
    "        try:\n",
    "            dictionary[values].append(dataset[label][i])\n",
    "        except:\n",
    "            return 1\n",
    "        i+=1\n",
    "#     print(dictionary)\n",
    "    gini = 0\n",
    "    for value in dictionary.values():\n",
    "        temp = np.array(value)\n",
    "        unq,class_counts = np.unique(temp,return_counts=True)\n",
    "        p = class_counts/np.sum(class_counts)\n",
    "        gini_c = 1 - np.sum(p**2)\n",
    "        gini += np.sum(class_counts)/len(dataset[label])*gini_c\n",
    "    return gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the features and label\n",
    "features = df.columns[:-1]\n",
    "label = 'Species'\n",
    "parent=None\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define the create decision tree function\n",
    "def create_decision_tree(dataset, df, features, label, parent):\n",
    "    datum = np.unique(df[label],return_counts=True)\n",
    "    unique_data = np.unique(dataset[label])\n",
    "    if len(unique_data) == 1:\n",
    "        return unique_data[0]\n",
    "    elif len(dataset) == 0:\n",
    "        return unique_data[np.argmax(datum[1])]\n",
    "    elif len(features) == 0:\n",
    "        return parent\n",
    "    else:\n",
    "        parent = unique_data[np.argmax(datum[1])]\n",
    "        # Call the calculate_gini_index function\n",
    "        item_values = [calculate_gini_index(dataset, feature, label) for feature in features]\n",
    "        optimum_feature_index = np.argmax(item_values)\n",
    "        optimum_feature = features[optimum_feature_index]\n",
    "        decision_tree = {optimum_feature:{}}\n",
    "        features = [i for i in features if i != optimum_feature] \n",
    "        for value in np.unique (dataset [optimum_feature]):\n",
    "            min_data = dataset. where(dataset[optimum_feature] == value).dropna()\n",
    "            # Recusrsive call to create_decision_tree function\n",
    "            min_tree = create_decision_tree(min_data, df,features, label, parent)\n",
    "            decision_tree[optimum_feature][value] = min_tree\n",
    "        return(decision_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the decision tree model\n",
    "decision_tree = create_decision_tree(df,df,features,label,parent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(test_data,decision_tree):\n",
    "    for nodes in decision_tree.keys():\n",
    "        value = test_data[nodes]\n",
    "        decision_tree = decision_tree[nodes][value]\n",
    "        \n",
    "        prediction = 0\n",
    "        if type(decision_tree) is dict:\n",
    "            prediction = predict(test_data,decision_tree)\n",
    "        else:\n",
    "            prediction = decision_tree\n",
    "            break\n",
    "    return prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Iris-virginica'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict using the trained model\n",
    "sample_data = {'SepalLengthCm':5.9,'SepalWidthCm':3.0,'PetalLengthCm':5.1,'PetalWidthCm':1.8}\n",
    "test_data = pd.Series(sample_data)\n",
    "prediction = predict(test_data,decision_tree)\n",
    "prediction"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}