decisionTreeViz.py

from sklearn.datasets import load_iris
from sklearn import tree
import numpy as np
from graphviz import Source

iris = load_iris()
print("Feature Names - ", iris.feature_names,"\n")
print(iris.target)
#Print the row 0,50 and 100 i.e. 1 example for each type
print("\nSetosa flower 1 - ",iris.data[0])
print("Versicolor flower 1 - ",iris.data[50])
print("Virginica flower 1 - ",iris.data[100],"\n")

test_indices = [0,1,50,51,100,101]

#training data
train_target = np.delete(iris.target, test_indices)
train_data = np.delete(iris.data, test_indices, axis=0)

#testing data
test_target = iris.target[test_indices]
test_data = iris.data[test_indices]

#Build The Classifier
dtClassifier = tree.DecisionTreeClassifier()
#Train The Classifier
dtClassifier.fit(train_data, train_target)

#Print the actual labels of each test point
print("\n********** Actual **************")
for p in range(len(test_indices)):
	print("Test Row ",test_indices[p], " belongs to the class ",test_target[p] )

predicted_target = (dtClassifier.predict(test_data))

#Print the predicted labels of each test point
print("\n********** Predicted **************")
for p in range(len(test_indices)):
	print("Test Row ",test_indices[p], " is predicted to be of the class ", predicted_target[p] )

#Visualize The Decision Tree
graph = Source(tree.export_graphviz(dtClassifier, out_file=None, 
                     feature_names=iris.feature_names,  
                     class_names=iris.target_names,  
                     filled=True, rounded=True,  node_ids= True,
                     special_characters=True))
graph.format = 'png'
graph.render('dtree_render',view=True)