Package org.terrier.matching.models

Class DFRWeightingModel

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, Model
    public class DFRWeightingModel
extends WeightingModel
    This class implements a modular Divergence from Randomness weighting model. Components in the model are specified individually, with many implementations provided: org.terrier.matching.models.basicmodel; org.terrier.matching.models.aftereffect; and org.terrier.matching.models.normalisation. A class name for each component should be specific in parenthesis after DFRWeightingModel. Moreover, besides the basic model, the other two components can be either specified or disabled.

    Examples:

    • DFRWeightingModel(P,L,2) is equivalent to PL2
    • DFRWeightingModel(P,L) removes the term frequency normalisastion component from PL2
    Author:
    Ben He
    See Also:
    Serialized Form

    • Field Detail

      • logger

        protected static final org.slf4j.Logger logger

      • basicModel

        protected BasicModel basicModel
        The applied basic model for randomness.

      • afterEffect

        protected AfterEffect afterEffect
        The applied model for after effect (aka. first normalisation).

      • normalisation

        protected Normalisation normalisation
        The applied frequency normalisation method.

      • NORMALISATION_PREFIX

        protected final java.lang.String NORMALISATION_PREFIX
        The prefix of the package of the frequency normalisation methods.
        See Also:
        Constant Field Values

      • BASICMODEL_PREFIX

        protected final java.lang.String BASICMODEL_PREFIX
        The prefix of the package of the basic models for randomness.
        See Also:
        Constant Field Values

      • AFTEREFFECT_PREFIX

        protected final java.lang.String AFTEREFFECT_PREFIX
        The prefix of the package of the first normalisation methods by after effect.
        See Also:
        Constant Field Values

      • parameter

        protected double parameter
        The parameter of the frequency normalisation component.

      • ENABLE_NORMALISATION

        protected boolean ENABLE_NORMALISATION
        A boolean that indicates if the frequency normalisation is enabled.

      • ENABLE_AFTEREFFECT

        protected boolean ENABLE_AFTEREFFECT
        A boolean that indicates if the first normalisation by after effect is enabled.

    • Constructor Detail

      • DFRWeightingModel

        public DFRWeightingModel​(java.lang.String[] components)
        The default constructor. Takes an array of strings to define the Basic Model, the After Effect component and the Normalisation component. If the array is less than 3 items in length, then empty strings will be passed instead of the After Effect and/or Normalisation components.
        Parameters:
        components - Corresponds to the names of the 3 DFR weighting models component names, as passed to initialise().

    • Method Detail

      • initialise

        protected void initialise​(java.lang.String basicModelName,
                          java.lang.String afterEffectName,
                          java.lang.String normalisationName)
        Initialise the components in the DFR model. For each component, if a package is not specified, then a prefix will be applied. These are BASICMODEL_PREFIX, AFTEREFFECT_PREFIX and NORMALISATION_PREFIX respectively. Note that NORMALISATION_PREFIX includes a partial class name.
        Parameters:
        basicModelName - The name of the applied basic model for randomness. This component must be specified and can NOT be an empty string.
        afterEffectName - The name of the applied first normalisation by after effect. An empty string to disable this component.
        normalisationName - The name of the applied frequency normalisation component. An empty string to disable this component.

      • initialise

        protected void initialise​(java.lang.String basicModelName,
                          java.lang.String afterEffectName,
                          java.lang.String normalisationName,
                          double _parameter)
        Initialise the components in the DFR model. For each component, if a package is not specified, then a prefix will be applied. These are BASICMODEL_PREFIX, AFTEREFFECT_PREFIX and NORMALISATION_PREFIX respectively. Note that NORMALISATION_PREFIX includes a partial class name.
        Parameters:
        basicModelName - The name of the applied basic model for randomness. This component must be specified and can NOT be an empty string.
        afterEffectName - The name of the applied first normalisation by after effect. An empty string to disable this component.
        normalisationName - The name of the applied frequency normalisation component. An empty string to disable this component.
        _parameter - The applied parameter value of the frequency normalisation.

      • setParameter

        public void setParameter​(double value)
        Set the frequency normalisation parameter.
        Specified by:
        setParameter in interface Model
        Overrides:
        setParameter in class WeightingModel
        Parameters:
        value - The given parameter value.

      • getParameter

        public double getParameter()
        Return the parameter set by setParameter()
        Specified by:
        getParameter in interface Model
        Overrides:
        getParameter in class WeightingModel
        Returns:
        parameter double value

      • getInfo

        public final java.lang.String getInfo()
        Returns the name of the model.
        Specified by:
        getInfo in interface Model
        Specified by:
        getInfo in class WeightingModel
        Returns:
        The name of the model.

      • score

        public final double score​(double tf,
                          double docLength)
        Compute a weight for a term in a document.
        Specified by:
        score in class WeightingModel
        Parameters:
        tf - The term frequency in the document
        docLength - the document's length
        Returns:
        the score assigned to a document with the given tf and docLength, and other preset parameters