Data Preprocessing with Orange Tool

This blog is about data preprocessing using the Orange tool. In this blog, I will be discussing how you can use the Orange library in python and perform various data preprocessing tasks like Discretization, Randomization, and Normalization on data with help of various Orange functions.

In the Orange tool canvas, take the Python script from the left panel and double click on it.

Discretization

Data discretization refers to a method of converting a huge number of data values into smaller ones so that the evaluation and management of data become easy. In other words, data discretization is a method of converting attributes values of continuous data into a finite set of intervals with minimum data loss. In this example I have taken the built in dataset provided by Orange namely iris which classifies the flowers based on their characteristics. For performing discretization Discretize function is used.
import Orange

#Python Script for Discreetization
iris = Orange.data.Table("iris.tab")
disc = Orange.preprocess.Discretize()
disc.method = Orange.preprocess.discretize.EqualFreq(n=3)
d_iris = disc(iris)
print("Original dataset:\n")
for e in iris[:3]:
print(e)
print("Discretized dataset:")
for e in d_iris[:3]:
print(e)

Continuization

Given a data table, return a new table in which the discretize attributes are replaced with continuous or removed.

• binary variables are transformed into 0.0/1.0 or -1.0/1.0 indicator variables, depending upon the argument zero_based.
• multinomial variables are treated according to the argument multinomial_treatment.
• discrete attribute with only one possible value are removed.

Continuize_Indicators

The variable is replaced by indicator variables, each corresponding to one value of the original variable. For each value of the original attribute, only the corresponding new attribute will have a value of one, and others will be zero. This is the default behavior.

For example, as shown in the below code snippet, dataset “titanic” has featured “status” with values “crew”, “first”, “second” and “third”, in that order. Its value for the 10th row is “first”. Continuization replaces the variable with variables “status=crew”, “status=first”, “status=second” and “status=third”.

#python script for Continuization
import Orange
titanic = Orange.data.Table("titanic")
continuizer = Orange.preprocess.Continuize()
titanic1 = continuizer(titanic)
print("Before Continuization : ",titanic.domain)
print("After Continuization : ",titanic1.domain)
#Data of row 15 in the before and after continuization
print("15th row data before : ",titanic[15])
print("15th row data after : ",titanic1[15])

Normalization

Normalization is used to scale the data of an attribute so that it falls in a smaller range, such as -1.0 to 1.0 or 0.0 to 1.0. Normalization is generally required when we are dealing with attributes on a different scale, otherwise, it may lead to a dilution in effectiveness of an important equally important attribute(on lower scale) because of other attribute having values on larger scale. We use the Normalize function to perform normalization.

#python script for Normalization
from Orange.data import Table
from Orange.preprocess import Normalize
data = Table("iris")
normalizer = Normalize(norm_type=Normalize.NormalizeBySpan)
normalized_data = normalizer(data)
print("Before Normalization : ",iris[2])
print("After Normalization : ",normalized_data[2])

Randomization

With randomization, given a data table, preprocessor returns a new table in which the data is shuffled. Randomize function is used from the Orange library to perform randomization.

#python script for Randomize
from Orange.data import Table
from Orange.preprocess import Randomize
data = Table("iris")
randomizer = Randomize(Randomize.RandomizeClasses)
randomized_data = randomizer(data)
print("Before randomization : ",iris[2])
print("After Randomization : ",randomized_data[2])

So this is all for this blog, we use various preprocessing functions in Orange library for data preprocessing.

Thank You!!