All Classes

Class	Description
AdapterModel	A scoring model whose initialization is completed via its AdapterModel.init(SolrResourceLoader) method.
CSVFeatureLogger	A feature logger that logs in csv format.
DefaultWrapperModel	A scoring model that fetches the wrapped model from SolrResourceLoader.
DocInfo
Feature	A recipe for computing a feature.
FeatureException
FeatureLogger	FeatureLogger can be registered in a model and provide a strategy for logging the feature values.
FeatureLogger.FeatureFormat
FeatureStore
FieldLengthFeature	This feature returns the length of a field (in terms) for the current document.
FieldValueFeature	This feature returns the value of a field in the current document.
IdentityNormalizer	A Normalizer that normalizes a feature value to itself.
Interleaving	Interleaving considers two ranking models: modelA and modelB. For a given query, each model returns its ranked list of documents La = (a1,a2,...) and Lb = (b1, b2, ...). An Interleaving algorithm creates a unique ranked list I = (i1, i2, ...). This list is created by interleaving elements from the two lists la and lb as described by the implementation algorithm. Each element Ij is labelled TeamA if it is selected from La and TeamB if it is selected from Lb.
InterleavingResult
LegacyFieldValueFeature	Deprecated.
LinearModel	A scoring model that computes scores using a dot product.
LTRFeatureLoggerTransformerFactory	This transformer will take care to generate and append in the response the features declared in the feature store of the current reranking model, or a specified feature store.
LTRInterleavingQuery	A learning to rank Query with Interleaving, will incapsulate two models, and delegate to it the rescoring of the documents.
LTRInterleavingRescorer	Implements the rescoring logic.
LTRInterleavingScoringQuery
LTRInterleavingTransformerFactory
LTRQParserPlugin	Plug into solr a rerank model.
LTRQuery	A learning to rank Query, will incapsulate a learning to rank model, and delegate to it the rescoring of the documents.
LTRRescorer	Implements the rescoring logic.
LTRScoringModel	A scoring model computes scores that can be used to rerank documents.
LTRScoringQuery	The ranking query that is run, reranking results using the LTRScoringModel algorithm
LTRScoringQuery.FeatureInfo
LTRThreadModule	The LTRThreadModule is optionally used by the LTRQParserPlugin and LTRFeatureLoggerTransformerFactory classes to parallelize the creation of Feature.FeatureWeight objects.
ManagedFeatureStore	Managed resource for a storing a feature.
ManagedModelStore	Menaged resource for storing a model
MinMaxNormalizer	A Normalizer to scale a feature value using a (min,max) range.
ModelException
ModelStore	Contains the model and features declared.
MultipleAdditiveTreesModel	A scoring model that computes scores based on the summation of multiple weighted trees.
NeuralNetworkModel	A scoring model that computes document scores using a neural network.
NeuralNetworkModel.Activation
NeuralNetworkModel.Layer
Normalizer	A normalizer normalizes the value of a feature.
NormalizerException
OriginalRankingLTRScoringQuery
OriginalScoreFeature	This feature returns the original score that the document had before performing the reranking.
SolrFeature	This feature allows you to reuse any Solr query as a feature.
SolrQueryRequestContextUtils
StandardNormalizer	A Normalizer to scale a feature value around an average-and-standard-deviation distribution.
TeamDraftInterleaving	Interleaving was introduced the first time by Joachims in [1, 2]. Team Draft Interleaving is among the most successful and used interleaving approaches[3]. Team Draft Interleaving implements a method similar to the way in which captains select their players in team-matches. Team Draft Interleaving produces a fair distribution of ranking models’ elements in the final interleaved list. "Team draft interleaving" has also proved to overcome an issue of the "Balanced interleaving" approach, in determining the winning model[4].
ValueFeature	This feature allows to return a constant given value for the current document.
WrapperModel	A scoring model that wraps the other model.