def classify_dataset_test():
#create dataset
filename = "Dataset/iris.data"
dataset = DT.Dataset(filename, _delimiter=',')
Tree = DT.DecisionTree(dataset)
#load exemples
exemple1 = np.array([5.4, 3.9, 1.3, 0.4]).astype('S15')
exemple2 = np.array([6.3, 2.5, 4.9, 1.5]).astype('S15')
exemple3 = np.array([
6.5,
3.0,
5.5,
1.8,
]).astype('S15')
#classify exemples
class1 = Tree.classify(exemple1)
class2 = Tree.classify(exemple2)
class3 = Tree.classify(exemple3)
#verify classification
eq_(class1, b'Iris-setosa')
eq_(class2, b'Iris-versicolor')
eq_(class3, b'Iris-virginica')
def read_dataset_test():
"""construct numpy arrays from dataset"""
filename = "/home/jorge/Documents/2-Programming/AI/DecisionTree/Dataset/players.txt"
data = np.loadtxt(filename, dtype="S4", delimiter="\t")
dataset = DT.Dataset(filename, _delimiter="\t")
#TODO make sure that the arguments are valid so that the dataset can be correcly loaded
eq_(3, dataset.NbAttr)
eq_(np.array(['No', 'Yes']), dataset.classes)
def decisionTreeLearning_test():
""""Function to test the general decision tree learning function."""
#create dataset
filename = "/home/jorge/Documents/2-Programming/AI/DecisionTree/Dataset/restaurant.txt"
dataset = DT.Dataset(filename, _delimiter='\t')
Tree = DT.DecisionTree(dataset)
#show first branch of decisition tree shown in page 702
# print(Tree.root)
# print(Tree.leaf)
#return tree for classify function next
return Tree
def gain_test():
""""Function to test given dataset and their gain. The dataset to test from
is the one shown in the book in page 700. Only Pat, Type and WillWait columns"""
#create dataset
filename = "/home/jorge/Documents/2-Programming/AI/DecisionTree/Dataset/restaurant.txt"
dataset = DT.Dataset(filename, _delimiter='\t')
Tree = DT.DecisionTree(dataset)
#test entropy function. Because the probs are equal for each class, then 1.0
eq_(1.0, Tree.entropy(Tree.dataset[:, -1]))
#test two attribues, calculations got from book page 704
eq_(0.541, Tree.gain(4))
eq_(0.0, Tree.gain(1))
print(Tree.getImportance())
def main(argv):
exemple = ""
#load dataset
try:
filename = argv[0]
dataset = DT.Dataset(filename, _delimiter=',')
except getopt.GetoptError:
print('dt.py -i <dataset dir>')
sys.exit(2)
#create and train decision tree
Tree = DT.DecisionTree(dataset)
#load exemple
try:
exemple = np.array(argv[1].split(",")).astype('S15')
except:
print("error loading exemple")
# classify
eclass = Tree.classify(exemple)
print(eclass)