{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

\n", " \n", " \"Skills\n", " \n", "

\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": {}, "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": [ "

Table of contents

\n", "\n", "
\n", "
    \n", "
  1. About the dataset
    2. \n", "
  2. Downloading the Data
    3. \n", "
  3. Pre-processing
    4. \n", "
  4. Setting up the Decision Tree
    5. \n", "
  5. Modeling
    6. \n", "
  6. Prediction
    7. \n", "
  7. Evaluation
    8. \n", "
  8. Visualization
    9. \n", "
\n", "
\n", "
\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the Following Libraries:\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "if you uisng you own version comment out\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "# Surpress warnings:\n", "def warn(*args, **kwargs):\n", " pass\n", "import warnings\n", "warnings.warn = warn" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [], "source": [ "import sys\n", "import numpy as np \n", "import pandas as pd\n", "from sklearn.tree import DecisionTreeClassifier\n", "import sklearn.tree as tree" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

About the dataset

\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", "
\n", "
\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", "
\n", "
\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", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

Downloading the Data

\n", " To download the data, we will use pandas library to read itdirectly into a dataframe from IBM Object Storage.\n", "
\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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AgeSexBPCholesterolNa_to_KDrug
023FHIGHHIGH25.355drugY
147MLOWHIGH13.093drugC
247MLOWHIGH10.114drugC
328FNORMALHIGH7.798drugX
461FLOWHIGH18.043drugY
522FNORMALHIGH8.607drugX
649FNORMALHIGH16.275drugY
741MLOWHIGH11.037drugC
860MNORMALHIGH15.171drugY
943MLOWNORMAL19.368drugY
1047FLOWHIGH11.767drugC
1134FHIGHNORMAL19.199drugY
1243MLOWHIGH15.376drugY
\n", "
" ], "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\n", "5 22 F NORMAL HIGH 8.607 drugX\n", "6 49 F NORMAL HIGH 16.275 drugY\n", "7 41 M LOW HIGH 11.037 drugC\n", "8 60 M NORMAL HIGH 15.171 drugY\n", "9 43 M LOW NORMAL 19.368 drugY\n", "10 47 F LOW HIGH 11.767 drugC\n", "11 34 F HIGH NORMAL 19.199 drugY\n", "12 43 M LOW HIGH 15.376 drugY" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/drug200.csv', delimiter=\",\")\n", "my_data.head(13)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

Practice

\n", " What is the size of data? \n", "
\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(200, 6)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_data.shape\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Click here for the solution\n", "\n", "```python\n", "my_data.shape\n", "\n", "```\n", "\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

Pre-processing

\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using my_data as the Drug.csv data read by pandas, declare the following variables:
\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remove the column containing the target name since it doesn't contain numeric values.\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [] }, "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": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values\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 the **LabelEncoder() method**\n", "to convert the categorical variable into dummy/indicator variables.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "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": 6, "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": 7, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0 drugY\n", "1 drugC\n", "2 drugC\n", "3 drugX\n", "4 drugY\n", "Name: Drug, dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = my_data[\"Drug\"]\n", "y[0:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "
\n", "

Setting up the Decision Tree

\n", " We will be using train/test split on our decision tree. Let's import train_test_split from sklearn.cross_validation.\n", "
\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [] }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now train_test_split will return 4 different parameters. We will name them:
\n", "X_trainset, X_testset, y_trainset, y_testset

\n", "The train_test_split will need the parameters:
\n", "X, y, test_size=0.3, and random_state=3.

\n", "The X and y are the arrays required before the split, the test_size represents the ratio of the testing dataset, and the random_state ensures that we obtain the same splits.\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [] }, "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": { "tags": [] }, "source": [ "

Practice

\n", "Print the shape of X_trainset and y_trainset. Ensure that the dimensions match.\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of X training set (140, 5) & Size of Y training set (140,)\n" ] } ], "source": [ "print('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Click here for the solution\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", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the shape of X_testset and y_testset. Ensure that the dimensions match.\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of X test set (60, 5) & Size of y test set (60,)\n" ] } ], "source": [ "print('Shape of X test set {}'.format(X_testset.shape),'&','Size of y test set {}'.format(y_testset.shape))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Click here for the solution\n", "\n", "```python\n", "print('Shape of X test set {}'.format(X_testset.shape),'&','Size of y test set {}'.format(y_testset.shape))\n", "\n", "```\n", "\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "
\n", "

Modeling

\n", " We will first create an instance of the DecisionTreeClassifier called drugTree.
\n", " Inside of the classifier, specify criterion=\"entropy\" so we can see the information gain of each node.\n", "
\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "DecisionTreeClassifier(criterion='entropy', max_depth=4)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n", "drugTree # it shows the default parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we will fit the data with the training feature matrix X_trainset and training response vector y_trainset \n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "DecisionTreeClassifier(criterion='entropy', max_depth=4)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "drugTree.fit(X_trainset,y_trainset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "
\n", "

Prediction

\n", " Let's make some predictions on the testing dataset and store it into a variable called predTree.\n", "
\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "tags": [] }, "outputs": [], "source": [ "predTree = drugTree.predict(X_testset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can print out predTree and y_testset if you want to visually compare the predictions to the actual values.\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['drugY' 'drugX' 'drugX' 'drugX' 'drugX']\n", "40 drugY\n", "51 drugX\n", "139 drugX\n", "197 drugX\n", "170 drugX\n", "Name: Drug, dtype: object\n" ] } ], "source": [ "print (predTree [0:5])\n", "print (y_testset [0:5])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "
\n", "

Evaluation

\n", " Next, let's import metrics from sklearn and check the accuracy of our model.\n", "
\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "tags": [] }, "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": {}, "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": [ "
\n", "\n", "
\n", "

Visualization

\n", "\n", "Let's visualize the tree\n", "\n", "
\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "tags": [] }, "outputs": [], "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\n", "\n", "#After executing the code below, a file named 'tree.png' would be generated which contains the decision tree image." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "tags": [] }, "outputs": [], "source": [ "from sklearn.tree import export_graphviz\n", "export_graphviz(drugTree, out_file='tree.dot', filled=True, feature_names=['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K'])\n", "!dot -Tpng tree.dot -o tree.png\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Thank you for completing this lab!\n", "\n", "## Author\n", "\n", "Saeed Aghabozorgi\n", "\n", "### Other Contributors\n", "\n", "Joseph Santarcangelo\n", "\n", "Richard Ye\n", "\n", "##

© IBM Corporation 2020. All rights reserved.

\n", " \n", "\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python", "language": "python", "name": "conda-env-python-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "prev_pub_hash": "1228bf81fd1be0f6e7dda62256f4ffcb19b064217fc51f2e012abde9b84c2b0d" }, "nbformat": 4, "nbformat_minor": 4 }