3 Classification/Classification-Decision-Trees-drug.jupyterlite.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<p style=\"text-align:center\">\n",
    "  \n",
    "</p>\n",
    "\n",
    "# Decision Trees\n",
    "\n",
    "Estimated time needed: **15** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "*   Develop a classification model using Decision Tree Algorithm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "In this lab exercise, you will learn a popular machine learning algorithm, Decision Trees. You will use this classification algorithm to build a model from the historical data of patients, and their response to different medications. Then you will use the trained decision tree to predict the class of an unknown patient, or to find a proper drug for a new patient.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ol>\n",
    "        <li><a href=\"https://#about_dataset\">About the dataset</a></li>\n",
    "        <li><a href=\"https://#downloading_data\">Downloading the Data</a></li>\n",
    "        <li><a href=\"https://#pre-processing\">Pre-processing</a></li>\n",
    "        <li><a href=\"https://#setting_up_tree\">Setting up the Decision Tree</a></li>\n",
    "        <li><a href=\"https://#modeling\">Modeling</a></li>\n",
    "        <li><a href=\"https://#prediction\">Prediction</a></li>\n",
    "        <li><a href=\"https://#evaluation\">Evaluation</a></li>\n",
    "        <li><a href=\"https://#visualization\">Visualization</a></li>\n",
    "    </ol>\n",
    "</div>\n",
    "<br>\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Import the Following Libraries:\n",
    "\n",
    "<ul>\n",
    "    <li> <b>numpy (as np)</b> </li>\n",
    "    <li> <b>pandas</b> </li>\n",
    "    <li> <b>DecisionTreeClassifier</b> from <b>sklearn.tree</b> </li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if you uisng you own version comment out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import piplite\n",
    "#await piplite.install(['pandas'])\n",
    "#await piplite.install(['matplotlib'])\n",
    "#await piplite.install(['numpy'])\n",
    "#await piplite.install(['scikit-learn'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import sklearn.tree as tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from pyodide.http import pyfetch\n",
    "\n",
    "#async def download(url, filename):\n",
    "#    response = await pyfetch(url)\n",
    "#    if response.status == 200:\n",
    "#        with open(filename, \"wb\") as f:\n",
    "#            f.write(await response.bytes())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div id=\"about_dataset\">\n",
    "    <h2>About the dataset</h2>\n",
    "    Imagine that you are a medical researcher compiling data for a study. You have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Drug x and y. \n",
    "    <br>\n",
    "    <br>\n",
    "    Part of your job is to build a model to find out which drug might be appropriate for a future patient with the same illness. The features of this dataset are Age, Sex, Blood Pressure, and the Cholesterol of the patients, and the target is the drug that each patient responded to.\n",
    "    <br>\n",
    "    <br>\n",
    "    It is a sample of multiclass classifier, and you can use the training part of the dataset \n",
    "    to build a decision tree, and then use it to predict the class of an unknown patient, or to prescribe a drug to a new patient.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div id=\"downloading_data\"> \n",
    "    <h2>Downloading the Data</h2>\n",
    "    To download the data, we will use !wget to download it from IBM Object Storage.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "path= 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/drug200.csv'\n",
    "#await download(path,\"drug200.csv\")\n",
    "#path=\"drug200.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Did you know?** When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, read the data using pandas dataframe:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Na_to_K</th>\n",
       "      <th>Drug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>25.355</td>\n",
       "      <td>drugY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>13.093</td>\n",
       "      <td>drugC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>10.114</td>\n",
       "      <td>drugC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>7.798</td>\n",
       "      <td>drugX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>F</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>18.043</td>\n",
       "      <td>drugY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex      BP Cholesterol  Na_to_K   Drug\n",
       "0   23   F    HIGH        HIGH   25.355  drugY\n",
       "1   47   M     LOW        HIGH   13.093  drugC\n",
       "2   47   M     LOW        HIGH   10.114  drugC\n",
       "3   28   F  NORMAL        HIGH    7.798  drugX\n",
       "4   61   F     LOW        HIGH   18.043  drugY"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data = pd.read_csv(path, delimiter=\",\")\n",
    "my_data[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div id=\"practice\"> \n",
    "    <h3>Practice</h3> \n",
    "    What is the size of data? \n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 6)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# write your code here\n",
    "my_data.shape\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Age          200 non-null    int64  \n",
      " 1   Sex          200 non-null    object \n",
      " 2   BP           200 non-null    object \n",
      " 3   Cholesterol  200 non-null    object \n",
      " 4   Na_to_K      200 non-null    float64\n",
      " 5   Drug         200 non-null    object \n",
      "dtypes: float64(1), int64(1), object(4)\n",
      "memory usage: 9.5+ KB\n"
     ]
    }
   ],
   "source": [
    "my_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age            0\n",
       "Sex            0\n",
       "BP             0\n",
       "Cholesterol    0\n",
       "Na_to_K        0\n",
       "Drug           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checl missing/ null value\n",
    "my_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "my_data.shape\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div href=\"pre-processing\">\n",
    "    <h2>Pre-processing</h2>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Using <b>my_data</b> as the Drug.csv data read by pandas, declare the following variables: <br>\n",
    "\n",
    "<ul>\n",
    "    <li> <b> X </b> as the <b> Feature Matrix </b> (data of my_data) </li>\n",
    "    <li> <b> y </b> as the <b> response vector </b> (target) </li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Remove the column containing the target name since it doesn't contain numeric values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23, 'F', 'HIGH', 'HIGH', 25.355],\n",
       "       [47, 'M', 'LOW', 'HIGH', 13.093],\n",
       "       [47, 'M', 'LOW', 'HIGH', 10.114],\n",
       "       [28, 'F', 'NORMAL', 'HIGH', 7.798],\n",
       "       [61, 'F', 'LOW', 'HIGH', 18.043]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values\n",
    "\n",
    "X[0:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you may figure out, some features in this dataset are categorical, such as **Sex** or **BP**. Unfortunately, Sklearn Decision Trees does not handle categorical variables. We can still convert these features to numerical values using **pandas.get_dummies()**\n",
    "to convert the categorical variable into dummy/indicator variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23, 0, 0, 0, 25.355],\n",
       "       [47, 1, 1, 0, 13.093],\n",
       "       [47, 1, 1, 0, 10.114],\n",
       "       [28, 0, 2, 0, 7.798],\n",
       "       [61, 0, 1, 0, 18.043]], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "le_sex = preprocessing.LabelEncoder()\n",
    "le_sex.fit(['F','M'])\n",
    "X[:,1] = le_sex.transform(X[:,1]) \n",
    "\n",
    "\n",
    "le_BP = preprocessing.LabelEncoder()\n",
    "le_BP.fit([ 'LOW', 'NORMAL', 'HIGH'])\n",
    "X[:,2] = le_BP.transform(X[:,2])\n",
    "\n",
    "\n",
    "le_Chol = preprocessing.LabelEncoder()\n",
    "le_Chol.fit([ 'NORMAL', 'HIGH'])\n",
    "X[:,3] = le_Chol.transform(X[:,3]) \n",
    "\n",
    "X[0:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can fill the target variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['drugY'],\n",
       "       ['drugC'],\n",
       "       ['drugC'],\n",
       "       ['drugX'],\n",
       "       ['drugY']], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = my_data[[\"Drug\"]].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<hr>\n",
    "\n",
    "<div id=\"setting_up_tree\">\n",
    "    <h2>Setting up the Decision Tree</h2>\n",
    "    We will be using <b>train/test split</b> on our <b>decision tree</b>. Let's import <b>train_test_split</b> from <b>sklearn.cross_validation</b>.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now <b> train_test_split </b> will return 4 different parameters. We will name them:<br>\n",
    "X_trainset, X_testset, y_trainset, y_testset <br> <br>\n",
    "The <b> train_test_split </b> will need the parameters: <br>\n",
    "X, y, test_size=0.3, and random_state=3. <br> <br>\n",
    "The <b>X</b> and <b>y</b> are the arrays required before the split, the <b>test_size</b> represents the ratio of the testing dataset, and the <b>random_state</b> ensures that we obtain the same splits.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h3>Practice</h3>\n",
    "Print the shape of X_trainset and y_trainset. Ensure that the dimensions match.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the shape of X_trainset:  (140, 5)\n",
      "Print the shape of y_trainset:  (140, 1)\n"
     ]
    }
   ],
   "source": [
    "# your code\n",
    "print('Print the shape of X_trainset: ', X_trainset.shape)\n",
    "print('Print the shape of y_trainset: ', y_trainset.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X training set (140, 5) &  Size of Y training set (140, 1)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "print('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Print the shape of X_testset and y_testset. Ensure that the dimensions match.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the shape of X_testset:  (60, 5)\n",
      "Print the shape of y_testset:  (60, 1)\n"
     ]
    }
   ],
   "source": [
    "# your code\n",
    "print('Print the shape of X_testset: ', X_testset.shape)\n",
    "print('Print the shape of y_testset: ', y_testset.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<hr>\n",
    "\n",
    "<div id=\"modeling\">\n",
    "    <h2>Modeling</h2>\n",
    "    We will first create an instance of the <b>DecisionTreeClassifier</b> called <b>drugTree</b>.<br>\n",
    "    Inside of the classifier, specify <i> criterion=\"entropy\" </i> so we can see the information gain of each node.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=4)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n",
    "drugTree # it shows the default parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Next, we will fit the data with the training feature matrix <b> X_trainset </b> and training  response vector <b> y_trainset </b>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=4)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugTree.fit(X_trainset,y_trainset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<hr>\n",
    "\n",
    "<div id=\"prediction\">\n",
    "    <h2>Prediction</h2>\n",
    "    Let's make some <b>predictions</b> on the testing dataset and store it into a variable called <b>predTree</b>.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "predTree = drugTree.predict(X_testset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "You can print out <b>predTree</b> and <b>y_testset</b> if you want to visually compare the predictions to the actual values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['drugY' 'drugX' 'drugX' 'drugX' 'drugX']\n",
      "[['drugY']\n",
      " ['drugX']\n",
      " ['drugX']\n",
      " ['drugX']\n",
      " ['drugX']]\n"
     ]
    }
   ],
   "source": [
    "print (predTree [0:5])\n",
    "print (y_testset [0:5])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<hr>\n",
    "\n",
    "<div id=\"evaluation\">\n",
    "    <h2>Evaluation</h2>\n",
    "    Next, let's import <b>metrics</b> from sklearn and check the accuracy of our model.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy:  0.9833333333333333\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, predTree))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Accuracy classification score** computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true.\n",
    "\n",
    "In multilabel classification, the function returns the subset accuracy. If the entire set of predicted labels for a sample strictly matches with the true set of labels, then the subset accuracy is 1.0; otherwise it is 0.0.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "<div id=\"visualization\">\n",
    "    <h2>Visualization</h2>\n",
    "\n",
    "Let's visualize the tree\n",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): ...working... done"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 4.10.1\n",
      "  latest version: 22.9.0\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Solving environment: ...working... done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: C:\\Users\\cgata\\anaconda3\n",
      "\n",
      "  added / updated specs:\n",
      "    - pydotplus\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    cairo-1.16.0               |    h63a05c6_1001         5.2 MB  conda-forge\n",
      "    conda-22.9.0               |   py38haa244fe_2         982 KB  conda-forge\n",
      "    expat-2.4.9                |       h6c2663c_0         207 KB\n",
      "    fribidi-1.0.10             |       h8d14728_0          63 KB  conda-forge\n",
      "    getopt-win32-0.1           |       h8ffe710_0          19 KB  conda-forge\n",
      "    glib-2.69.1                |       h5dc1a3c_1         1.6 MB\n",
      "    graphite2-1.3.14           |       hd77b12b_1          91 KB\n",
      "    graphviz-2.50.0            |       hdb8b0d4_0         903 KB\n",
      "    gts-0.7.6                  |       h69fa6b1_0         186 KB  conda-forge\n",
      "    harfbuzz-4.3.0             |       hda2c7e1_0         676 KB\n",
      "    libffi-3.4.2               |       h8ffe710_5          41 KB  conda-forge\n",
      "    libgd-2.3.3                |       ha43c60c_1         314 KB\n",
      "    pango-1.50.7               |       h78c2152_0         279 KB\n",
      "    pcre-8.45                  |       h0e60522_0         518 KB  conda-forge\n",
      "    pixman-0.38.0              |    hfa6e2cd_1003         993 KB  conda-forge\n",
      "    pydotplus-2.0.2            |     pyhaef67bd_5          24 KB  conda-forge\n",
      "    python_abi-3.8             |           2_cp38           4 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:        12.0 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  cairo              conda-forge/win-64::cairo-1.16.0-h63a05c6_1001\n",
      "  expat              pkgs/main/win-64::expat-2.4.9-h6c2663c_0\n",
      "  fribidi            conda-forge/win-64::fribidi-1.0.10-h8d14728_0\n",
      "  getopt-win32       conda-forge/win-64::getopt-win32-0.1-h8ffe710_0\n",
      "  glib               pkgs/main/win-64::glib-2.69.1-h5dc1a3c_1\n",
      "  graphite2          pkgs/main/win-64::graphite2-1.3.14-hd77b12b_1\n",
      "  graphviz           pkgs/main/win-64::graphviz-2.50.0-hdb8b0d4_0\n",
      "  gts                conda-forge/win-64::gts-0.7.6-h69fa6b1_0\n",
      "  harfbuzz           pkgs/main/win-64::harfbuzz-4.3.0-hda2c7e1_0\n",
      "  libffi             conda-forge/win-64::libffi-3.4.2-h8ffe710_5\n",
      "  libgd              pkgs/main/win-64::libgd-2.3.3-ha43c60c_1\n",
      "  pango              pkgs/main/win-64::pango-1.50.7-h78c2152_0\n",
      "  pcre               conda-forge/win-64::pcre-8.45-h0e60522_0\n",
      "  pixman             conda-forge/win-64::pixman-0.38.0-hfa6e2cd_1003\n",
      "  pydotplus          conda-forge/noarch::pydotplus-2.0.2-pyhaef67bd_5\n",
      "  python_abi         conda-forge/win-64::python_abi-3.8-2_cp38\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  conda              pkgs/main::conda-4.10.1-py38haa95532_1 --> conda-forge::conda-22.9.0-py38haa244fe_2\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "\n",
      "graphviz-2.50.0      | 903 KB    |            |   0% \n",
      "graphviz-2.50.0      | 903 KB    | 1          |   2% \n",
      "graphviz-2.50.0      | 903 KB    | #5         |  16% \n",
      "graphviz-2.50.0      | 903 KB    | ##1        |  21% \n",
      "graphviz-2.50.0      | 903 KB    | ###5       |  35% \n",
      "graphviz-2.50.0      | 903 KB    | ####2      |  43% \n",
      "graphviz-2.50.0      | 903 KB    | ######2    |  62% \n",
      "graphviz-2.50.0      | 903 KB    | #######4   |  74% \n",
      "graphviz-2.50.0      | 903 KB    | ########6  |  87% \n",
      "graphviz-2.50.0      | 903 KB    | ########## | 100% \n",
      "graphviz-2.50.0      | 903 KB    | ########## | 100% \n",
      "\n",
      "expat-2.4.9          | 207 KB    |            |   0% \n",
      "expat-2.4.9          | 207 KB    | 7          |   8% \n",
      "expat-2.4.9          | 207 KB    | #5         |  15% \n",
      "expat-2.4.9          | 207 KB    | #######7   |  77% \n",
      "expat-2.4.9          | 207 KB    | ########## | 100% \n",
      "expat-2.4.9          | 207 KB    | ########## | 100% \n",
      "\n",
      "pcre-8.45            | 518 KB    |            |   0% \n",
      "pcre-8.45            | 518 KB    | 3          |   3% \n",
      "pcre-8.45            | 518 KB    | ####       |  40% \n",
      "pcre-8.45            | 518 KB    | #####5     |  56% \n",
      "pcre-8.45            | 518 KB    | #######4   |  74% \n",
      "pcre-8.45            | 518 KB    | ########6  |  86% \n",
      "pcre-8.45            | 518 KB    | ########## | 100% \n",
      "pcre-8.45            | 518 KB    | ########## | 100% \n",
      "\n",
      "pango-1.50.7         | 279 KB    |            |   0% \n",
      "pango-1.50.7         | 279 KB    | 5          |   6% \n",
      "pango-1.50.7         | 279 KB    | ######8    |  69% \n",
      "pango-1.50.7         | 279 KB    | ########## | 100% \n",
      "pango-1.50.7         | 279 KB    | ########## | 100% \n",
      "\n",
      "pydotplus-2.0.2      | 24 KB     |            |   0% \n",
      "pydotplus-2.0.2      | 24 KB     | ######5    |  66% \n",
      "pydotplus-2.0.2      | 24 KB     | ########## | 100% \n",
      "pydotplus-2.0.2      | 24 KB     | ########## | 100% \n",
      "\n",
      "python_abi-3.8       | 4 KB      |            |   0% \n",
      "python_abi-3.8       | 4 KB      | ########## | 100% \n",
      "python_abi-3.8       | 4 KB      | ########## | 100% \n",
      "\n",
      "getopt-win32-0.1     | 19 KB     |            |   0% \n",
      "getopt-win32-0.1     | 19 KB     | ########5  |  86% \n",
      "getopt-win32-0.1     | 19 KB     | ########## | 100% \n",
      "getopt-win32-0.1     | 19 KB     | ########## | 100% \n",
      "\n",
      "graphite2-1.3.14     | 91 KB     |            |   0% \n",
      "graphite2-1.3.14     | 91 KB     | #7         |  18% \n",
      "graphite2-1.3.14     | 91 KB     | ########## | 100% \n",
      "graphite2-1.3.14     | 91 KB     | ########## | 100% \n",
      "\n",
      "fribidi-1.0.10       | 63 KB     |            |   0% \n",
      "fribidi-1.0.10       | 63 KB     | ##5        |  25% \n",
      "fribidi-1.0.10       | 63 KB     | ########## | 100% \n",
      "fribidi-1.0.10       | 63 KB     | ########## | 100% \n",
      "\n",
      "libgd-2.3.3          | 314 KB    |            |   0% \n",
      "libgd-2.3.3          | 314 KB    | 5          |   5% \n",
      "libgd-2.3.3          | 314 KB    | ######6    |  66% \n",
      "libgd-2.3.3          | 314 KB    | ########6  |  87% \n",
      "libgd-2.3.3          | 314 KB    | ########## | 100% \n",
      "\n",
      "libffi-3.4.2         | 41 KB     |            |   0% \n",
      "libffi-3.4.2         | 41 KB     | ###8       |  39% \n",
      "libffi-3.4.2         | 41 KB     | ########## | 100% \n",
      "libffi-3.4.2         | 41 KB     | ########## | 100% \n",
      "\n",
      "pixman-0.38.0        | 993 KB    |            |   0% \n",
      "pixman-0.38.0        | 993 KB    | 1          |   2% \n",
      "pixman-0.38.0        | 993 KB    | 4          |   5% \n",
      "pixman-0.38.0        | 993 KB    | 6          |   6% \n",
      "pixman-0.38.0        | 993 KB    | #2         |  13% \n",
      "pixman-0.38.0        | 993 KB    | #7         |  18% \n",
      "pixman-0.38.0        | 993 KB    | ##         |  21% \n",
      "pixman-0.38.0        | 993 KB    | ##5        |  26% \n",
      "pixman-0.38.0        | 993 KB    | ###        |  31% \n",
      "pixman-0.38.0        | 993 KB    | ###5       |  35% \n",
      "pixman-0.38.0        | 993 KB    | ###8       |  39% \n",
      "pixman-0.38.0        | 993 KB    | ####3      |  43% \n",
      "pixman-0.38.0        | 993 KB    | ####6      |  47% \n",
      "pixman-0.38.0        | 993 KB    | #####3     |  53% \n",
      "pixman-0.38.0        | 993 KB    | #####9     |  60% \n",
      "pixman-0.38.0        | 993 KB    | ######4    |  64% \n",
      "pixman-0.38.0        | 993 KB    | ######9    |  69% \n",
      "pixman-0.38.0        | 993 KB    | #######4   |  74% \n",
      "pixman-0.38.0        | 993 KB    | #######8   |  79% \n",
      "pixman-0.38.0        | 993 KB    | ########3  |  84% \n",
      "pixman-0.38.0        | 993 KB    | ########8  |  89% \n",
      "pixman-0.38.0        | 993 KB    | #########3 |  93% \n",
      "pixman-0.38.0        | 993 KB    | #########8 |  98% \n",
      "pixman-0.38.0        | 993 KB    | ########## | 100% \n",
      "\n",
      "harfbuzz-4.3.0       | 676 KB    |            |   0% \n",
      "harfbuzz-4.3.0       | 676 KB    | 2          |   2% \n",
      "harfbuzz-4.3.0       | 676 KB    | ##1        |  21% \n",
      "harfbuzz-4.3.0       | 676 KB    | ####       |  40% \n",
      "harfbuzz-4.3.0       | 676 KB    | ######1    |  61% \n",
      "harfbuzz-4.3.0       | 676 KB    | ########2  |  83% \n",
      "harfbuzz-4.3.0       | 676 KB    | #########6 |  97% \n",
      "harfbuzz-4.3.0       | 676 KB    | ########## | 100% \n",
      "\n",
      "conda-22.9.0         | 982 KB    |            |   0% \n",
      "conda-22.9.0         | 982 KB    | 1          |   2% \n",
      "conda-22.9.0         | 982 KB    | 8          |   8% \n",
      "conda-22.9.0         | 982 KB    | #4         |  15% \n",
      "conda-22.9.0         | 982 KB    | #9         |  20% \n",
      "conda-22.9.0         | 982 KB    | ##6        |  26% \n",
      "conda-22.9.0         | 982 KB    | ###2       |  33% \n",
      "conda-22.9.0         | 982 KB    | ###9       |  39% \n",
      "conda-22.9.0         | 982 KB    | ####3      |  44% \n",
      "conda-22.9.0         | 982 KB    | ####8      |  49% \n",
      "conda-22.9.0         | 982 KB    | ######3    |  64% \n",
      "conda-22.9.0         | 982 KB    | #######1   |  72% \n",
      "conda-22.9.0         | 982 KB    | #######8   |  78% \n",
      "conda-22.9.0         | 982 KB    | ########9  |  90% \n",
      "conda-22.9.0         | 982 KB    | #########6 |  96% \n",
      "conda-22.9.0         | 982 KB    | ########## | 100% \n",
      "\n",
      "gts-0.7.6            | 186 KB    |            |   0% \n",
      "gts-0.7.6            | 186 KB    | 8          |   9% \n",
      "gts-0.7.6            | 186 KB    | ####3      |  43% \n",
      "gts-0.7.6            | 186 KB    | ########## | 100% \n",
      "gts-0.7.6            | 186 KB    | ########## | 100% \n",
      "\n",
      "cairo-1.16.0         | 5.2 MB    |            |   0% \n",
      "cairo-1.16.0         | 5.2 MB    |            |   0% \n",
      "cairo-1.16.0         | 5.2 MB    |            |   1% \n",
      "cairo-1.16.0         | 5.2 MB    | 3          |   3% \n",
      "cairo-1.16.0         | 5.2 MB    | 5          |   5% \n",
      "cairo-1.16.0         | 5.2 MB    | 8          |   8% \n",
      "cairo-1.16.0         | 5.2 MB    | #1         |  11% \n",
      "cairo-1.16.0         | 5.2 MB    | #3         |  13% \n",
      "cairo-1.16.0         | 5.2 MB    | #6         |  17% \n",
      "cairo-1.16.0         | 5.2 MB    | #8         |  19% \n",
      "cairo-1.16.0         | 5.2 MB    | ##5        |  25% \n",
      "cairo-1.16.0         | 5.2 MB    | ##8        |  28% \n",
      "cairo-1.16.0         | 5.2 MB    | ###        |  31% \n",
      "cairo-1.16.0         | 5.2 MB    | ###3       |  33% \n",
      "cairo-1.16.0         | 5.2 MB    | ###5       |  36% \n",
      "cairo-1.16.0         | 5.2 MB    | ###8       |  38% \n",
      "cairo-1.16.0         | 5.2 MB    | ####       |  41% \n",
      "cairo-1.16.0         | 5.2 MB    | ####2      |  43% \n",
      "cairo-1.16.0         | 5.2 MB    | ####4      |  45% \n",
      "cairo-1.16.0         | 5.2 MB    | ####7      |  47% \n",
      "cairo-1.16.0         | 5.2 MB    | ####9      |  50% \n",
      "cairo-1.16.0         | 5.2 MB    | #####6     |  56% \n",
      "cairo-1.16.0         | 5.2 MB    | #####9     |  60% \n",
      "cairo-1.16.0         | 5.2 MB    | ######4    |  64% \n",
      "cairo-1.16.0         | 5.2 MB    | ######6    |  66% \n",
      "cairo-1.16.0         | 5.2 MB    | ######8    |  68% \n",
      "cairo-1.16.0         | 5.2 MB    | #######    |  70% \n",
      "cairo-1.16.0         | 5.2 MB    | #######3   |  73% \n",
      "cairo-1.16.0         | 5.2 MB    | #######5   |  76% \n",
      "cairo-1.16.0         | 5.2 MB    | #######8   |  78% \n",
      "cairo-1.16.0         | 5.2 MB    | ########   |  81% \n",
      "cairo-1.16.0         | 5.2 MB    | ########2  |  83% \n",
      "cairo-1.16.0         | 5.2 MB    | ########4  |  85% \n",
      "cairo-1.16.0         | 5.2 MB    | ########6  |  87% \n",
      "cairo-1.16.0         | 5.2 MB    | ########9  |  89% \n",
      "cairo-1.16.0         | 5.2 MB    | #########1 |  91% \n",
      "cairo-1.16.0         | 5.2 MB    | #########2 |  93% \n",
      "cairo-1.16.0         | 5.2 MB    | #########6 |  96% \n",
      "cairo-1.16.0         | 5.2 MB    | #########8 |  98% \n",
      "cairo-1.16.0         | 5.2 MB    | ########## | 100% \n",
      "\n",
      "glib-2.69.1          | 1.6 MB    |            |   0% \n",
      "glib-2.69.1          | 1.6 MB    |            |   1% \n",
      "glib-2.69.1          | 1.6 MB    | 9          |  10% \n",
      "glib-2.69.1          | 1.6 MB    | #8         |  19% \n",
      "glib-2.69.1          | 1.6 MB    | ##7        |  28% \n",
      "glib-2.69.1          | 1.6 MB    | ###8       |  39% \n",
      "glib-2.69.1          | 1.6 MB    | ####8      |  49% \n",
      "glib-2.69.1          | 1.6 MB    | #####6     |  56% \n",
      "glib-2.69.1          | 1.6 MB    | ######4    |  64% \n",
      "glib-2.69.1          | 1.6 MB    | #######1   |  71% \n",
      "glib-2.69.1          | 1.6 MB    | #######8   |  78% \n",
      "glib-2.69.1          | 1.6 MB    | #########5 |  95% \n",
      "glib-2.69.1          | 1.6 MB    | ########## | 100% \n",
      "Preparing transaction: ...working... done\n",
      "Verifying transaction: ...working... done\n",
      "Executing transaction: ...working... done\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: C:\\Users\\cgata\\anaconda3\n",
      "\n",
      "  added / updated specs:\n",
      "    - python-graphviz\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    python-graphviz-0.20.1     |     pyh22cad53_0          35 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:          35 KB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  python-graphviz    conda-forge/noarch::python-graphviz-0.20.1-pyh22cad53_0 None\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "\n",
      "python-graphviz-0.20 | 35 KB     |            |   0% \n",
      "python-graphviz-0.20 | 35 KB     | ####5      |  45% \n",
      "python-graphviz-0.20 | 35 KB     | ########## | 100% \n",
      "python-graphviz-0.20 | 35 KB     | ########## | 100% \n",
      "Preparing transaction: ...working... done\n",
      "Verifying transaction: ...working... done\n",
      "Executing transaction: ...working... done\n",
      "Retrieving notices: ...working... done\n"
     ]
    }
   ],
   "source": [
    "# Notice: You might need to uncomment and install the pydotplus and graphviz libraries if you have not installed these before\n",
    "!conda install -c conda-forge pydotplus -y\n",
    "!conda install -c conda-forge python-graphviz -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#update conda to latest version: 22.9.0 from current version: 4.10.1\n",
    "!conda update -n base -c defaults conda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(drugTree)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.8.8"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}