SYildiz
1/13/2019 - 8:10 AM
Decision Trees - Python
Decision Trees
In this lab exercise, you will learn a popular machine learning algorithm, Decision Tree. You will use this classification algorithm to build a model from historical data of patients, and their response to different medications. Then you use the trained decision tree to predict the class of a unknown patient, or to find a proper drug for a new patient.
Table of contents
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
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.
Downloading the Data
To download the data, we will use !wget to download it from IBM Object Storage.!wget -O drug200.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/drug200.csv
--2019-01-13 07:39:14-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/drug200.csv
Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193
Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 6027 (5.9K) [text/csv]
Saving to: ‘drug200.csv’
drug200.csv 100%[=====================>] 5.89K --.-KB/s in 0s
2019-01-13 07:39:14 (68.6 MB/s) - ‘drug200.csv’ saved [6027/6027]
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
now, read data using pandas dataframe:
my_data = pd.read_csv("drug200.csv", delimiter=",")
my_data[0:5]
| Age | Sex | BP | Cholesterol | Na_to_K | Drug | |
|---|---|---|---|---|---|---|
| 0 | 23 | F | HIGH | HIGH | 25.355 | drugY |
| 1 | 47 | M | LOW | HIGH | 13.093 | drugC |
| 2 | 47 | M | LOW | HIGH | 10.114 | drugC |
| 3 | 28 | F | NORMAL | HIGH | 7.798 | drugX |
| 4 | 61 | F | LOW | HIGH | 18.043 | drugY |
Practice
What is the size of data?# write your code here
my_data.size
1200
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]
array([[23, 'F', 'HIGH', 'HIGH', 25.355],
[47, 'M', 'LOW', 'HIGH', 13.093],
[47, 'M', 'LOW', 'HIGH', 10.113999999999999],
[28, 'F', 'NORMAL', 'HIGH', 7.797999999999999],
[61, 'F', 'LOW', 'HIGH', 18.043]], dtype=object)
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]
array([[23, 0, 0, 0, 25.355],
[47, 1, 1, 0, 13.093],
[47, 1, 1, 0, 10.113999999999999],
[28, 0, 2, 0, 7.797999999999999],
[61, 0, 1, 0, 18.043]], dtype=object)
Now we can fill the target variable.
y = my_data["Drug"]
y[0:5]
0 drugY
1 drugC
2 drugC
3 drugX
4 drugY
Name: Drug, dtype: object
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)
Practice
Print the shape of X_trainset and y_trainset. Ensure that the dimensions match# your code
print(X_trainset.shape)
print(y_trainset.shape)
(140, 5)
(140,)
Print the shape of X_testset and y_testset. Ensure that the dimensions match
# your code
print(X_testset.shape)
print(y_testset.shape)
(60, 5)
(60,)
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
DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False, random_state=None,
splitter='best')
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)
DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False, random_state=None,
splitter='best')
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 [0:5])
print (y_testset [0:5])
['drugY' 'drugX' 'drugX' 'drugX' 'drugX']
40 drugY
51 drugX
139 drugX
197 drugX
170 drugX
Name: Drug, dtype: object
Evaluation
Next, let's import metrics from sklearn and check the accuracy of our model.from sklearn import metrics
import matplotlib.pyplot as plt
print("DecisionTrees's Accuracy: ", metrics.f1_score(y_testset, predTree, average='weighted'))
DecisionTrees's Accuracy: 0.9833152664859981
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.
Practice
Can you calculate the accuracy score without sklearn ?
# your code here
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
Solving environment: done
## Package Plan ##
environment location: /home/jupyterlab/conda
added / updated specs:
- pydotplus
The following packages will be downloaded:
package | build
---------------------------|-----------------
pydotplus-2.0.2 | py_2 23 KB conda-forge
openssl-1.0.2p | h470a237_2 3.1 MB conda-forge
------------------------------------------------------------
Total: 3.1 MB
The following packages will be UPDATED:
openssl: 1.0.2p-h470a237_1 conda-forge --> 1.0.2p-h470a237_2 conda-forge
pydotplus: 2.0.2-py36_1 anaconda --> 2.0.2-py_2 conda-forge
Downloading and Extracting Packages
pydotplus-2.0.2 | 23 KB | ##################################### | 100%
openssl-1.0.2p | 3.1 MB | ##################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
Solving environment: done
## Package Plan ##
environment location: /home/jupyterlab/conda
added / updated specs:
- python-graphviz
The following packages will be downloaded:
package | build
---------------------------|-----------------
python-graphviz-0.8.4 | py36_1002 27 KB conda-forge
The following NEW packages will be INSTALLED:
python-graphviz: 0.8.4-py36_1002 conda-forge
Downloading and Extracting Packages
python-graphviz-0.8. | 27 KB | ##################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
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')