Example of creating Dataset object

Firstly, import Dataset:

from dataset import Dataset

Secondly, get some data:

data = [
	[5.1, 3.5, 1.4, 0.2, "Iris-setosa"],
	[5.0, 3.2, 1.2, 0.2, "Iris-setosa"],
	[6.4, 3.2, 4.5, 1.5, "Iris-versicolor"],
	[6.7, 3.1, 4.4, 1.4, "Iris-versicolor"],
	[6.7, 3.0, 5.2, 2.3, "Iris-virginica"]]

Thirdly, set columns (attributes) names:

column_names = [
	"Sepal length", "Sepal width",
	"Petal length", "Petal width",
	"Class"]

Now we can create Dataset object. Arguments:

• data - just list of list
• target index - index of target attribute, attribute that contains classes values
• column or attributes names - list of attributes names
• name - Dataset name

iris = Dataset(data, 4, column_names, "Iris")

Also you can just get iris dataset by calling method

get_iris().

You can specify path to dataset file by passing this path as argument, for example:

get_iris("data\\iris.data")

Default value of path

resources\data\iris\iris.data.

iris = get_iris()

Preprocessing dataset

Threshold - process of converting continius values to discrete values. There are two methods:

• median - thresholding by median value of column
• gain - thresholding by using value with maximum gain

As first argument takes column index, second - name of method

iris.threshold(i, "gain")

Shuffle dataset:

iris = iris.shuffle()

Split dataset on "train" and "test", as argument passing ratio. Train dataset gets 80% of original dataset, test - other:

train, test = iris.split_by_ration(0.8)

Creating tree

For this purpose import tree.py

from tree import make_tree
from tree import Root

To create decision tree use make_tree method. This method takes one argument - dataset. Return tree object.

tree = make_tree(train)

Classification

For classify the instance use method classify. May return None if can't classify this instance.

instance_to_classify = [4.8, 3.1, 1.6, 0.2]
predicted_class = tree.classify(instance_to_classify)