Class |
Description |
AdapterModel |
|
CSVFeatureLogger |
A feature logger that logs in csv format.
|
DefaultWrapperModel |
|
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 |
|
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.
|