Overview classes

matchup.structure.occurrence Module

Occurrence module is composed only by the Occurrence class.

Classes

Occurrence(doc, term)	Occurrence is an encapsulation of:
Term(word, position)

matchup.structure.query Module

The query module is responsible for encapsulating everything related to the process of creating a user query.

Classes

Model	IR Models base class.
NoSuchAnswerException	Exception when no such answer (input) are given by user during a search method.
Orchestrator(vocabulary)
Query(*, vocabulary)	Represents the Query of the IR service.
Sanitizer(*, stopwords_path, stemming)	Responsible to clean and process the text representation.
Solution(results)	The solution class has the function of properly storing and displaying the responses of the queries
Term(word, position)

matchup.structure.solution Module

Module that represents the search solution using some IR model in addition to some operations for presentation

Classes

Result(document, score)
Solution(results)	The solution class has the function of properly storing and displaying the responses of the queries

matchup.structure.vocabulary Module

Describes the data structure of IR models design.

Classes

IDF()	Abstract base class who represents IDF param.
InvertedIndex()	Simple structure based in a inverted file.
Occurrence(doc, term)	Occurrence is an encapsulation of:
Sanitizer(*, stopwords_path, stemming)	Responsible to clean and process the text representation.
TF(**kwargs)	Abstract base class who represents TF param.
Vocabulary(save, **kwargs)	Crucial data structure that represents and storage all text processing.

matchup.structure.weighting.tf Module

Module that represents one weighting param for IR models: Term Frequency (TF).

Classes

ABC	Helper class that provides a standard way to create an ABC using inheritance.
Binary(**kwargs)
DoubleNormalization(**kwargs)
DoubleNormalizationK(**kwargs)
LogNormalization(**kwargs)
TF(**kwargs)	Abstract base class who represents TF param.
TFFactory	Factory for TF based on String values.
TermFrequency(**kwargs)
defaultdict	defaultdict(default_factory[, …]) –> dict with default factory

matchup.structure.weighting.idf Module

Module that represents one weighting param for IR models: Inverse Document Frequency (IDF).

Classes

ABC	Helper class that provides a standard way to create an ABC using inheritance.
IDF()	Abstract base class who represents IDF param.
IDFFactory	Factory for IDF based on String values.
InverseFrequency()
InverseFrequencyMax()
InverseFrequencySmooth()
ProbabilisticInverseFrequency()
Unary()
defaultdict	defaultdict(default_factory[, …]) –> dict with default factory

matchup.presentation.sanitizer Module

Module responsible to configure the text representation.

Classes

Line(content, number)
Sanitizer(*, stopwords_path, stemming)	Responsible to clean and process the text representation.
Term(word, position)

matchup.models.orchestrator Module

The brain of IR algorithms. This module are responsible to execute one model and return the resulted scored document list.

Classes

InverseFrequency()
LogNormalization(**kwargs)
Model	IR Models base class.
ModelMissingParameters
NoSuchInputException
Orchestrator(vocabulary)
Result(document, score)
Term(word, position)
Vector()

matchup.models.model Module

First abstraction that represents IR models

Model, IterModel

Classes

IterModel()	Describe one variation of Model classes : IterModel classes have some features for help his works Pointers and occurrences are implemented here.
Model	IR Models base class.
ModelExecutionError
NoSuchModelException
Occurrence(doc, term)	Occurrence is an encapsulation of:
Result(document, score)
Term(word, position)
Vocabulary(save, **kwargs)	Crucial data structure that represents and storage all text processing.
defaultdict	defaultdict(default_factory[, …]) –> dict with default factory

matchup.models.algorithms.boolean Module

Classic IR model. Boolean model are implemented here, based in Interface Model

Classes

Boolean()
Model	IR Models base class.
Result(document, score)
Term(word, position)
Vocabulary(save, **kwargs)	Crucial data structure that represents and storage all text processing.
defaultdict	defaultdict(default_factory[, …]) –> dict with default factory

matchup.models.algorithms.belief_network Module

Belief Network module. Defines a IR modern model network-based. Classic probabilistic and vector space concepts are important here.

Classes

BeliefNetwork()
IterModel()	Describe one variation of Model classes : IterModel classes have some features for help his works Pointers and occurrences are implemented here.
Result(document, score)
Term(word, position)
Vocabulary(save, **kwargs)	Crucial data structure that represents and storage all text processing.
defaultdict	defaultdict(default_factory[, …]) –> dict with default factory

matchup.models.algorithms.extended_boolean Module

IR modern model. Combination with Boolean and Vector Space concepts.

Classes

ExtendedBoolean(p)
IterModel()	Describe one variation of Model classes : IterModel classes have some features for help his works Pointers and occurrences are implemented here.
Term(word, position)
Vocabulary(save, **kwargs)	Crucial data structure that represents and storage all text processing.
defaultdict	defaultdict(default_factory[, …]) –> dict with default factory

matchup.models.algorithms.generalized_vector Module

IR modern model. Vector space concepts considering term co-relations

Classes

Correlation(keyword, cir)
GeneralizedVector()	Class that implements the ‘run’ method of Generalized Vector IR model.
IterModel()	Describe one variation of Model classes : IterModel classes have some features for help his works Pointers and occurrences are implemented here.
Minterm(correlations, id)
Result(document, score)
Term(word, position)
Vocabulary(save, **kwargs)	Crucial data structure that represents and storage all text processing.
defaultdict	defaultdict(default_factory[, …]) –> dict with default factory

matchup.models.algorithms.probabilistic Module

Classic IR model. Probabilistic model are implemented here, based in Interface Model

Classes

IterModel()	Describe one variation of Model classes : IterModel classes have some features for help his works Pointers and occurrences are implemented here.
Probabilistic()
Result(document, score)
Term(word, position)
Vocabulary(save, **kwargs)	Crucial data structure that represents and storage all text processing.
defaultdict	defaultdict(default_factory[, …]) –> dict with default factory

matchup.models.algorithms.vector_space Module

Classic IR model. Vector model are implemented here, based in Interface Model

Classes

IterModel()	Describe one variation of Model classes : IterModel classes have some features for help his works Pointers and occurrences are implemented here.
Result(document, score)
Term(word, position)
Vector()
Vocabulary(save, **kwargs)	Crucial data structure that represents and storage all text processing.
defaultdict	defaultdict(default_factory[, …]) –> dict with default factory