Data Preprocessing (preprocess)

Preprocessing module contains data processing utilities like data discretization, continuization, imputation and transformation.


Imputation replaces missing values with new values (or omits such features).

from import Table
from Orange.preprocess import Impute

data = Table("")
imputer = Impute()

impute_heart = imputer(data)

There are several imputation methods one can use.

from import Table
from Orange.preprocess import Impute, Average

data = Table("")
imputer = Impute(method=Average())
impute_heart = imputer(data)


Discretization replaces continuous features with the corresponding categorical features:

import Orange

iris ="")
disc = Orange.preprocess.Discretize()
disc.method = Orange.preprocess.discretize.EqualFreq(n=3)
d_iris = disc(iris)

print("Original dataset:")
for e in iris[:3]:

print("Discretized dataset:")
for e in d_iris[:3]:

The variable in the new data table indicate the bins to which the original values belong.

Original dataset:
[5.1, 3.5, 1.4, 0.2 | Iris-setosa]
[4.9, 3.0, 1.4, 0.2 | Iris-setosa]
[4.7, 3.2, 1.3, 0.2 | Iris-setosa]
Discretized dataset:
[<5.5, >=3.2, <2.5, <0.8 | Iris-setosa]
[<5.5, [2.8, 3.2), <2.5, <0.8 | Iris-setosa]
[<5.5, >=3.2, <2.5, <0.8 | Iris-setosa]

Default discretization method (four bins with approximatelly equal number of data instances) can be replaced with other methods.

iris ="")
disc = Orange.preprocess.Discretize()
disc.method = Orange.preprocess.discretize.EqualFreq(n=2)

Discretization Algorithms

To add a new discretization, derive it from Discretization.


class Orange.preprocess.Continuize

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;
import Orange
titanic ="titanic")
continuizer = Orange.preprocess.Continuize()
titanic1 = continuizer(titanic)

The class has a number of attributes that can be set either in constructor or, later, as attributes.


Determines the value used as the “low” value of the variable. When binary variables are transformed into continuous or when multivalued variable is transformed into multiple variables, the transformed variable can either have values 0.0 and 1.0 (default, zero_based=True) or -1.0 and 1.0 (zero_based=False).


Defines the treatment of multinomial variables.


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 behaviour.

Note that these variables are not independent, so they cannot be used (directly) in, for instance, linear or logistic regression.

For example, dataset “titanic” has feature “status” with values “crew”, “first”, “second” and “third”, in that order. Its value for the 15th row is “first”. Continuization replaces the variable with variables “status=crew”, “status=first”, “status=second” and “status=third”. After

continuizer = Orange.preprocess.Continuize()
titanic1 = continuizer(titanic)

we have

>>> titanic.domain
[status, age, sex | survived]
>>> titanic1.domain
[status=crew, status=first, status=second, status=third,
 age=adult, age=child, sex=female, sex=male | survived]

For the 15th row, the variable “status=first” has value 1 and the values of the other three variables are 0:

>>> print(titanic[15])
[first, adult, male | yes]
>>> print(titanic1[15])
[0.000, 1.000, 0.000, 0.000, 1.000, 0.000, 0.000, 1.000 | yes]

Similar to the above, except that it creates indicators for all values except the first one, according to the order in the variable’s values attribute. If all indicators in the transformed data instance are 0, the original instance had the first value of the corresponding variable.

Continuizing the variable “status” with this setting gives variables “status=first”, “status=second” and “status=third”. If all of them were 0, the status of the original data instance was “crew”.

>>> continuizer.multinomial_treatment = continuizer.FirstAsBase
>>> continuizer(titanic).domain
[status=first, status=second, status=third, age=child, sex=male | survived]

Like above, except that the most frequent value is used as the base. If there are multiple most frequent values, the one with the lowest index in values is used. The frequency of values is extracted from data, so this option does not work if only the domain is given.

Continuizing the Titanic data in this way differs from the above by the attributes sex: instead of “sex=male” it constructs “sex=female” since there were more females than males on Titanic.

>>> continuizer.multinomial_treatment = continuizer.FrequentAsBase
>>> continuizer(titanic).domain
[status=first, status=second, status=third, age=child, sex=female | survived]

Discrete variables are removed.

>>> continuizer.multinomial_treatment = continuizer.Remove
>>> continuizer(titanic).domain
[ | survived]

Discrete variables with more than two values are removed. Binary variables are treated the same as in FirstAsBase.

>>> continuizer.multinomial_treatment = continuizer.RemoveMultinomial
>>> continuizer(titanic).domain
[age=child, sex=male | survived]
Raise an error if there are any multinomial variables in the data.

Multinomial variables are treated as ordinal and replaced by continuous variables with indices within values, e.g. 0, 1, 2, 3…

>>> continuizer.multinomial_treatment = continuizer.AsOrdinal
>>> titanic1 = continuizer(titanic)
>>> titanic[700]
[third, adult, male | no]
>>> titanic1[700]
[3.000, 0.000, 1.000 | no]

As above, except that the resulting continuous value will be from range 0 to 1, e.g. 0, 0.333, 0.667, 1 for a four-valued variable:

>>> continuizer.multinomial_treatment = continuizer.AsNormalizedOrdinal
>>> titanic1 = continuizer(titanic)
>>> titanic1[700]
[1.000, 0.000, 1.000 | no]
>>> titanic1[15]
[0.333, 0.000, 1.000 | yes]

If True the class is replaced by continuous attributes or normalized as well. Multiclass problems are thus transformed to multitarget ones. (Default: False)

class Orange.preprocess.DomainContinuizer

Construct a domain in which discrete attributes are replaced by continuous.

domain_continuizer = Orange.preprocess.DomainContinuizer()
domain1 = domain_continuizer(titanic)

Orange.preprocess.Continuize calls DomainContinuizer to construct the domain.

Domain continuizers can be given either a dataset or a domain, and return a new domain. When given only the domain, use the most frequent value as the base value.

By default, the class does not change continuous and class attributes, discrete attributes are replaced with N attributes (Indicators) with values 0 and 1.




Feature selection

Feature scoring

Feature scoring is an assessment of the usefulness of features for prediction of the dependant (class) variable. Orange provides classes that compute the common feature scores for classification and regression.

The code below computes the information gain of feature “tear_rate” in the Lenses dataset:

>>> data ="lenses")
>>> Orange.preprocess.score.InfoGain(data, "tear_rate")

An alternative way of invoking the scorers is to construct the scoring object and calculate the scores for all the features at once, like in the following example:

>>> gain = Orange.preprocess.score.InfoGain()
>>> scores = gain(data)
>>> for attr, score in zip(data.domain.attributes, scores):
...     print('%.3f' % score,
0.039 age
0.040 prescription
0.377 astigmatic
0.549 tear_rate

Feature scoring methods work on different feature types (continuous or discrete) and different types of target variables (i.e. in classification or regression problems). Refer to method’s feature_type and class_type attributes for intended type or employ preprocessing methods (e.g. discretization) for conversion between data types.

Additionally, you can use the score_data() method of some learners (Orange.classification.LinearRegressionLearner, Orange.regression.LogisticRegressionLearner, Orange.classification.RandomForestLearner, and Orange.regression.RandomForestRegressionLearner) to obtain the feature scores as calculated by these learners. For example:

>>> learner = Orange.classification.LogisticRegressionLearner()
>>> learner.score_data(data)

Feature selection

We can use feature selection to limit the analysis to only the most relevant or informative features in the dataset.

Feature selection with a scoring method that works on continuous features will retain all discrete features and vice versa.

The code below constructs a new dataset consisting of two best features according to the ANOVA method:

>>> data ="wine")
>>> anova = Orange.preprocess.score.ANOVA()
>>> selector = Orange.preprocess.SelectBestFeatures(method=anova, k=2)
>>> data2 = selector(data)
>>> data2.domain
[Flavanoids, Proline | Wine]