5.Decision-Trees-Example.ipynb

Kernel: Python 3

Modelling using Decision Tree

Import the Following Libraries:

• numpy (as np)
• pandas
• DecisionTreeClassifier from sklearn.tree

import numpy as np 
import pandas as pd
from sklearn.tree import DecisionTreeClassifier 
from sklearn import tree 

X=[[0,0],[1,1]]
Y=[0,1]
d=tree.DecisionTreeClassifier()
d=d.fit(X,Y)

d.predict([[3,2]])

array([1])

d.predict_proba([[2,2]])

array([[0., 1.]])

from sklearn.datasets import load_iris 
from sklearn import tree
X,y=load_iris(return_X_y=True)
clf=tree.DecisionTreeClassifier()
clf=clf.fit(X,y)

tree.plot_tree(clf)

[Text(167.4, 199.32, 'X[3] <= 0.8\ngini = 0.667\nsamples = 150\nvalue = [50, 50, 50]'),
 Text(141.64615384615385, 163.07999999999998, 'gini = 0.0\nsamples = 50\nvalue = [50, 0, 0]'),
 Text(193.15384615384616, 163.07999999999998, 'X[3] <= 1.75\ngini = 0.5\nsamples = 100\nvalue = [0, 50, 50]'),
 Text(103.01538461538462, 126.83999999999999, 'X[2] <= 4.95\ngini = 0.168\nsamples = 54\nvalue = [0, 49, 5]'),
 Text(51.50769230769231, 90.6, 'X[3] <= 1.65\ngini = 0.041\nsamples = 48\nvalue = [0, 47, 1]'),
 Text(25.753846153846155, 54.359999999999985, 'gini = 0.0\nsamples = 47\nvalue = [0, 47, 0]'),
 Text(77.26153846153846, 54.359999999999985, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]'),
 Text(154.52307692307693, 90.6, 'X[3] <= 1.55\ngini = 0.444\nsamples = 6\nvalue = [0, 2, 4]'),
 Text(128.76923076923077, 54.359999999999985, 'gini = 0.0\nsamples = 3\nvalue = [0, 0, 3]'),
 Text(180.27692307692308, 54.359999999999985, 'X[2] <= 5.45\ngini = 0.444\nsamples = 3\nvalue = [0, 2, 1]'),
 Text(154.52307692307693, 18.119999999999976, 'gini = 0.0\nsamples = 2\nvalue = [0, 2, 0]'),
 Text(206.03076923076924, 18.119999999999976, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]'),
 Text(283.2923076923077, 126.83999999999999, 'X[2] <= 4.85\ngini = 0.043\nsamples = 46\nvalue = [0, 1, 45]'),
 Text(257.53846153846155, 90.6, 'X[0] <= 5.95\ngini = 0.444\nsamples = 3\nvalue = [0, 1, 2]'),
 Text(231.7846153846154, 54.359999999999985, 'gini = 0.0\nsamples = 1\nvalue = [0, 1, 0]'),
 Text(283.2923076923077, 54.359999999999985, 'gini = 0.0\nsamples = 2\nvalue = [0, 0, 2]'),
 Text(309.04615384615386, 90.6, 'gini = 0.0\nsamples = 43\nvalue = [0, 0, 43]')]

About the dataset

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.

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 feature sets of this dataset are Age, Sex, Blood Pressure, and Cholesterol of patients, and the target is the drug that each patient responded to.

It is a sample of binary classifier, and you can use the training part of the dataset to build a decision tree, and then use it to predict the class of a unknown patient, or to prescribe it to a new patient.

Read data using pandas dataframe

my_data = pd.read_csv("drug200.csv", delimiter=",")
my_data[0:5]

What is the size of data?

# write your code here

my_data.shape

Pre-processing

Using my_data as the Drug.csv data read by pandas, declare the following variables:

• X as the Feature Matrix (data of my_data)
• y as the response vector (target)

Remove the column containing the target name since it doesn't contain numeric values.

X = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values
X[0:5]

As you may figure out, some features in this dataset are categorical such as Sex or BP. Unfortunately, Sklearn Decision Trees do not handle categorical variables. But still we can convert these features to numerical values. pandas.get_dummies() Convert categorical variable into dummy/indicator variables.

from sklearn import preprocessing
le_sex = preprocessing.LabelEncoder()
le_sex.fit(['F','M'])
X[:,1] = le_sex.transform(X[:,1]) 


le_BP = preprocessing.LabelEncoder()
le_BP.fit([ 'LOW', 'NORMAL', 'HIGH'])
X[:,2] = le_BP.transform(X[:,2])


le_Chol = preprocessing.LabelEncoder()
le_Chol.fit([ 'NORMAL', 'HIGH'])
X[:,3] = le_Chol.transform(X[:,3]) 

X[0:5]

Now we can fill the target variable.

y = my_data["Drug"]
y[0:5]

---

Setting up the Decision Tree

We will be using train/test split on our decision tree. Let's import train_test_split from sklearn.cross_validation.

from sklearn.model_selection import train_test_split

Now train_test_split will return 4 different parameters. We will name them:
X_trainset, X_testset, y_trainset, y_testset

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

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.

X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)

Print the shape of X_trainset and y_trainset. Ensure that the dimensions match

# your code
print("X_trainset.shape =",X_trainset.shape)
print("y_train.shape =",y_trainset.shape)

Print the shape of X_testset and y_testset. Ensure that the dimensions match

# your code

print("X_testset.shape =",X_testset.shape)
print("y_testset.shape =",y_testset.shape)

---

Modeling

We will first create an instance of the DecisionTreeClassifier called drugTree.
Inside of the classifier, specify criterion="entropy" so we can see the information gain of each node.

drugTree = DecisionTreeClassifier(criterion="entropy", max_depth = 4)
drugTree # it shows the default parameters

Next, we will fit the data with the training feature matrix X_trainset and training response vector y_trainset

drugTree.fit(X_trainset,y_trainset)

---

Prediction

Let's make some predictions on the testing dataset and store it into a variable called predTree.

predTree = drugTree.predict(X_testset)

You can print out predTree and y_testset if you want to visually compare the prediction to the actual values.

print (predTree [0:10])
print (y_testset [0:10])

---

Evaluation

Next, let's import metrics from sklearn and check the accuracy of our model.

from sklearn.metrics import multilabel_confusion_matrix
multilabel_confusion_matrix(y_testset,predTree,
                          labels=[0 ,1,2,3,4])

from sklearn import metrics
import matplotlib.pyplot as plt
print("DecisionTrees's Accuracy: ", metrics.accuracy_score(y_testset, predTree))

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.

In multilabel classification, the function returns the subset accuracy. If the entire set of predicted labels for a sample strictly match with the true set of labels, then the subset accuracy is 1.0; otherwise it is 0.0.

from sklearn.datasets import load_iris
from sklearn import tree 
X,y = load_iris(return_X_y=True)
clf=tree.DecisionTreeClassifier()
clf=clf.fit(X,y)

tree.plot_tree(clf)

[Text(167.4, 199.32, 'X[3] <= 0.8\ngini = 0.667\nsamples = 150\nvalue = [50, 50, 50]'),
 Text(141.64615384615385, 163.07999999999998, 'gini = 0.0\nsamples = 50\nvalue = [50, 0, 0]'),
 Text(193.15384615384616, 163.07999999999998, 'X[3] <= 1.75\ngini = 0.5\nsamples = 100\nvalue = [0, 50, 50]'),
 Text(103.01538461538462, 126.83999999999999, 'X[2] <= 4.95\ngini = 0.168\nsamples = 54\nvalue = [0, 49, 5]'),
 Text(51.50769230769231, 90.6, 'X[3] <= 1.65\ngini = 0.041\nsamples = 48\nvalue = [0, 47, 1]'),
 Text(25.753846153846155, 54.359999999999985, 'gini = 0.0\nsamples = 47\nvalue = [0, 47, 0]'),
 Text(77.26153846153846, 54.359999999999985, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]'),
 Text(154.52307692307693, 90.6, 'X[3] <= 1.55\ngini = 0.444\nsamples = 6\nvalue = [0, 2, 4]'),
 Text(128.76923076923077, 54.359999999999985, 'gini = 0.0\nsamples = 3\nvalue = [0, 0, 3]'),
 Text(180.27692307692308, 54.359999999999985, 'X[2] <= 5.45\ngini = 0.444\nsamples = 3\nvalue = [0, 2, 1]'),
 Text(154.52307692307693, 18.119999999999976, 'gini = 0.0\nsamples = 2\nvalue = [0, 2, 0]'),
 Text(206.03076923076924, 18.119999999999976, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]'),
 Text(283.2923076923077, 126.83999999999999, 'X[2] <= 4.85\ngini = 0.043\nsamples = 46\nvalue = [0, 1, 45]'),
 Text(257.53846153846155, 90.6, 'X[0] <= 5.95\ngini = 0.444\nsamples = 3\nvalue = [0, 1, 2]'),
 Text(231.7846153846154, 54.359999999999985, 'gini = 0.0\nsamples = 1\nvalue = [0, 1, 0]'),
 Text(283.2923076923077, 54.359999999999985, 'gini = 0.0\nsamples = 2\nvalue = [0, 0, 2]'),
 Text(309.04615384615386, 90.6, 'gini = 0.0\nsamples = 43\nvalue = [0, 0, 43]')]

### Regression
from sklearn import tree
X=[[0,0],[2,2]]
y=[0.5,2.5]
clf=tree.DecisionTreeRegressor()
clf=clf.fit(X,y)
##Predict
clf.predict([[3,1]])

---

Visualization

Lets visualize the tree

# Notice: You might need to uncomment and install the pydotplus and graphviz libraries if you have not installed these before
# !conda install -c conda-forge pydotplus -y
# !conda install -c conda-forge python-graphviz -y

from sklearn.externals.six import StringIO
import pydotplus
import matplotlib.image as mpimg
from sklearn import tree
%matplotlib inline 

dot_data = StringIO()
filename = "drugtree.png"
featureNames = my_data.columns[0:5]
targetNames = my_data["Drug"].unique().tolist()
out=tree.export_graphviz(drugTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_trainset), filled=True,  special_characters=True,rotate=False)  
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  
graph.write_png(filename)
img = mpimg.imread(filename)
plt.figure(figsize=(100, 200))
plt.imshow(img,interpolation='nearest')