notebooks/supervised/classification/ensemble/classification_extra_trees.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ultimate-understanding",
   "metadata": {},
   "source": [
    "## Classification with extra-trees classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "crude-shape",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import ensemble, datasets, metrics, model_selection, preprocessing, pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "circular-dylan",
   "metadata": {},
   "source": [
    "Load the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "turkish-hampton",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _wine_dataset:\n",
      "\n",
      "Wine recognition dataset\n",
      "------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 178 (50 in each of three classes)\n",
      "    :Number of Attributes: 13 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      " \t\t- Alcohol\n",
      " \t\t- Malic acid\n",
      " \t\t- Ash\n",
      "\t\t- Alcalinity of ash  \n",
      " \t\t- Magnesium\n",
      "\t\t- Total phenols\n",
      " \t\t- Flavanoids\n",
      " \t\t- Nonflavanoid phenols\n",
      " \t\t- Proanthocyanins\n",
      "\t\t- Color intensity\n",
      " \t\t- Hue\n",
      " \t\t- OD280/OD315 of diluted wines\n",
      " \t\t- Proline\n",
      "\n",
      "    - class:\n",
      "            - class_0\n",
      "            - class_1\n",
      "            - class_2\n",
      "\t\t\n",
      "    :Summary Statistics:\n",
      "    \n",
      "    ============================= ==== ===== ======= =====\n",
      "                                   Min   Max   Mean     SD\n",
      "    ============================= ==== ===== ======= =====\n",
      "    Alcohol:                      11.0  14.8    13.0   0.8\n",
      "    Malic Acid:                   0.74  5.80    2.34  1.12\n",
      "    Ash:                          1.36  3.23    2.36  0.27\n",
      "    Alcalinity of Ash:            10.6  30.0    19.5   3.3\n",
      "    Magnesium:                    70.0 162.0    99.7  14.3\n",
      "    Total Phenols:                0.98  3.88    2.29  0.63\n",
      "    Flavanoids:                   0.34  5.08    2.03  1.00\n",
      "    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n",
      "    Proanthocyanins:              0.41  3.58    1.59  0.57\n",
      "    Colour Intensity:              1.3  13.0     5.1   2.3\n",
      "    Hue:                          0.48  1.71    0.96  0.23\n",
      "    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n",
      "    Proline:                       278  1680     746   315\n",
      "    ============================= ==== ===== ======= =====\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML Wine recognition datasets.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n",
      "\n",
      "The data is the results of a chemical analysis of wines grown in the same\n",
      "region in Italy by three different cultivators. There are thirteen different\n",
      "measurements taken for different constituents found in the three types of\n",
      "wine.\n",
      "\n",
      "Original Owners: \n",
      "\n",
      "Forina, M. et al, PARVUS - \n",
      "An Extendible Package for Data Exploration, Classification and Correlation. \n",
      "Institute of Pharmaceutical and Food Analysis and Technologies,\n",
      "Via Brigata Salerno, 16147 Genoa, Italy.\n",
      "\n",
      "Citation:\n",
      "\n",
      "Lichman, M. (2013). UCI Machine Learning Repository\n",
      "[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n",
      "School of Information and Computer Science. \n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "  (1) S. Aeberhard, D. Coomans and O. de Vel, \n",
      "  Comparison of Classifiers in High Dimensional Settings, \n",
      "  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \n",
      "  Mathematics and Statistics, James Cook University of North Queensland. \n",
      "  (Also submitted to Technometrics). \n",
      "\n",
      "  The data was used with many others for comparing various \n",
      "  classifiers. The classes are separable, though only RDA \n",
      "  has achieved 100% correct classification. \n",
      "  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n",
      "  (All results using the leave-one-out technique) \n",
      "\n",
      "  (2) S. Aeberhard, D. Coomans and O. de Vel, \n",
      "  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \n",
      "  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n",
      "  Mathematics and Statistics, James Cook University of North Queensland. \n",
      "  (Also submitted to Journal of Chemometrics).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "wine = datasets.load_wine()\n",
    "print(wine.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "advised-pride",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = pd.DataFrame(wine.data, columns=wine.feature_names)\n",
    "y = wine.target"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "turkish-proceeding",
   "metadata": {},
   "source": [
    "Stratify the data by the target label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "emerging-emphasis",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train samples: 89\n",
      "test samples 89\n"
     ]
    },
    {
     "data": {
      "image/png": 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4Pk/LLEka2Ax+SaqYwoI/IubUJ3Rb1qvtGxHxYkQsrb9OLOr8kqRtK/KKfy4wbRvt38vM9vrr7gLPL0nahsKCPzMfANYU9f6SpF1TRh//hRHxeL0raOTOd5ck9admB/91wCSgndpkb9/d3o4R0RkRXRHR1dPT06TyJGnP19Tgz8xXMnNTZv4e+Htgyg72nZ2ZHZnZ0dbm1P+S1F+aGvwRMbbX6snAsu3tK0kqRmETrUXEj4CjgdER0Q38LXB0RLQDCTwHnFfU+SVJ21ZY8GfmGdtovr6o80mSGuM3dyWpYgx+SaoYg1+SKsbgl6SKMfglqWIMfkmqGINfkirG4JekijH4JaliDH5JqhiDX5IqxuCXpIox+CWpYgx+SaoYg1+SKsbgl6SKMfglqWIMfkmqGINfkirG4Jekiiks+CNiTkSsiohlvdo+GBH3RsTK+s+RRZ1fkrRtRV7xzwWmbdV2KXB/Zh4I3F9flyQ1UWHBn5kPAGu2ap4O3FhfvhE4qajzS5K2rdl9/GMy8+X68m+BMdvbMSI6I6IrIrp6enqaU50kVUBpN3czM4HcwfbZmdmRmR1tbW1NrEyS9mzNDv5XImIsQP3nqiafX5Iqr9nB/1PgnPryOcCCJp9fkiqvyOGcPwIeAQ6KiO6IOBe4ApgaESuBT9XXJUlNNLioN87MM7az6biizilJ2jm/uStJFWPwS1LFGPySVDEGvyRVjMEvSRVj8EtSxRj8klQxBr8kVYzBL0kVY/BLUsUY/JJUMQa/JFWMwS9JFWPwS1LFGPySVDEGvyRVjMEvSRVj8EtSxRj8klQxBr8kVUxhD1vfkYh4DlgHbAI2ZmZHGXVIUhWVEvx1x2Tm70o8vyRVkl09klQxZQV/AgsjYnFEdG5rh4jojIiuiOjq6elpcnmStOcqK/g/kZkfBU4ALoiIP996h8ycnZkdmdnR1tbW/AolaQ9VSvBn5ov1n6uAO4EpZdQhSVXU9OCPiA9ExLDNy8CngWXNrkOSqqqMUT1jgDsjYvP552Xm/y6hDkmqpKYHf2b+BvhIs88rSapxOKckVYzBL0kVY/BLUsUY/JJUMQa/JFWMwS9JFWPwS1LFGPySVDEGvyRVjMEvSRVj8EtSxRj8klQxBr8kVYzBL0kVY/BLUsUY/JJUMQa/JFWMwS9JFWPwS1LFGPySVDGlBH9ETIuIX0fEMxFxaRk1SFJVNT34I2IQ8H3gBGAycEZETG52HZJUVWVc8U8BnsnM32TmvwO3AtNLqEOSKikys7knjPgcMC0z/1N9/SzgTzPzwq326wQ666sHAb9uaqHNNRr4XdlFaJf4uxvY9vTf3wGZ2bZ14+AyKmlEZs4GZpddRzNERFdmdpRdh/rO393AVtXfXxldPS8CH+61Pq7eJklqgjKC/5+AAyNiYkT8AfAfgJ+WUIckVVLTu3oyc2NEXAj8IzAImJOZTza7jt1MJbq09lD+7ga2Sv7+mn5zV5JULr+5K0kVY/BLUsUY/CVy6oqBKyLmRMSqiFhWdi3qu4j4cEQsioinIuLJiLi47JqayT7+ktSnrngamAp0UxvtdEZmPlVqYWpIRPw5sB64KTMPKbse9U1EjAXGZuaSiBgGLAZOqsr/f17xl8epKwawzHwAWFN2Hdo1mflyZi6pL68DlgP7l1tV8xj85dkfeKHXejcV+g9P2l1ExATgcOBXJZfSNAa/pMqKiH2A+cCXMvO1sutpFoO/PE5dIZUoIlqohf4tmfmTsutpJoO/PE5dIZUkIgK4HliemVeVXU+zGfwlycyNwOapK5YDtzl1xcARET8CHgEOiojuiDi37JrUJ0cBZwHHRsTS+uvEsotqFodzSlLFeMUvSRVj8EtSxRj8klQxBr8kVYzBL0kVY/Cr8iJiRESc34TznBQRk4s+j7QzBr8EI4CGgz9qduX/nZMAg1+lcxy/Ki8iNs+M+mtgEXAYMBJoAb6WmQvqE3n9I7WJvI4ATgTOBs4EeqhNuLc4M78TEZOA7wNtwBvAfwY+CPwMeLX+mpGZzzbrM0q9Nf1h69Ju6FLgkMxsj4jBwNDMfC0iRgOPRsTmqTQOBM7JzEcj4khgBvARav9ALKE2pzvUHuD9V5m5MiL+FPhBZh5bf5+fZeYdzfxw0tYMfundAvhv9Qet/J7aVNlj6tuez8xH68tHAQsycwOwISL+AbbM9vhnwO216WAA2LtZxUuNMPild/sCtS6aIzLz7Yh4Dmitb3u9geP3AtZmZnsx5Unvnzd3JVgHDKsvDwdW1UP/GOCA7RzzEPCXEdFav8r/C4D6nO7/GhGnwpYbwR/Zxnmk0hj8qrzMXA08VH9wejvQERFPULt5u2I7x/wTtWm0HwfuAZ6gdtMWan81nBsR/wI8yTuP1LwVmBUR/1y/ASyVwlE90i6KiH0yc31EDAUeADo3P8dV2p3Zxy/tutn1L2S1Ajca+hoovOKXpIqxj1+SKsbgl6SKMfglqWIMfkmqGINfkirm/wPjXfPkVn2NFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, train_size=0.5, stratify=y)\n",
    "\n",
    "df_train = pd.DataFrame(y_train, columns=['target'])\n",
    "df_train['type'] = 'train'\n",
    "\n",
    "df_test = pd.DataFrame(y_test, columns=['target'])\n",
    "df_test['type'] = 'test'\n",
    "\n",
    "df_set = df_train.append(df_test)\n",
    "\n",
    "_ = sns.countplot(x='target', hue='type', data=df_set)     \n",
    "\n",
    "print('train samples:', len(X_train))\n",
    "print('test samples', len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "national-clinic",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(max_depth=3, n_estimators=50)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ensemble.ExtraTreesClassifier(n_estimators=50, max_depth=3)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "plain-serial",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2\n",
       "0  29   0   0\n",
       "1   2  33   1\n",
       "2   0   0  24"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted = model.predict(X_test)\n",
    "\n",
    "confusion_matrix = pd.DataFrame(metrics.confusion_matrix(y_test, predicted))\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "stock-maldives",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.heatmap(confusion_matrix, annot=True, cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "therapeutic-quest",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.966\n",
      "precision: 0.968\n",
      "recall: 0.966\n",
      "f1 score: 0.966\n"
     ]
    }
   ],
   "source": [
    "print(\"accuracy: {:.3f}\".format(metrics.accuracy_score(y_test, predicted)))\n",
    "print(\"precision: {:.3f}\".format(metrics.precision_score(y_test, predicted, average='weighted')))\n",
    "print(\"recall: {:.3f}\".format(metrics.recall_score(y_test, predicted, average='weighted')))\n",
    "print(\"f1 score: {:.3f}\".format(metrics.f1_score(y_test, predicted, average='weighted')))"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}