elasticsearch_dsl::search::queries::specialized

Struct RankFeatureQuery

pub struct RankFeatureQuery { /* private fields */ }

Expand description

Boosts the relevance score of documents based on the numeric value of a rank_feature or rank_features field.

To create a rank feature query:

Query::rank_feature("test");

To apply mathematical functions:

Query::rank_feature("test").saturation();
Query::rank_feature("test").saturation().pivot(2.2);
Query::rank_feature("test").logarithm(3.0);
Query::rank_feature("test").sigmoid(1.0, 2.0);
Query::rank_feature("test").linear();

https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-rank-feature-query.html

Implementations§

Source §

The saturation function gives a score equal to S / (S + pivot), where S is the value of the rank feature field and pivot is a configurable pivot value so that the result will be less than 0.5 if S is less than pivot and greater than 0.5 otherwise. Scores are always (0,1).

If the rank feature has a negative score impact then the function will be computed as pivot / (S + pivot), which decreases when S increases.

If a pivot value is not provided, Elasticsearch computes a default value equal to the approximate geometric mean of all rank feature values in the index. We recommend using this default value if you haven’t had the opportunity to train a good pivot value.

Source

pub fn logarithm(self, scaling_factor: f64) -> RankFeatureLogarithmQuery

The log function gives a score equal to log(scaling_factor + S), where S is the value of the rank feature field and scaling_factor is a configurable scaling factor. Scores are unbounded.

This function only supports rank features that have a positive score impact.

Source

pub fn sigmoid(self, pivot: f64, exponent: f64) -> RankFeatureSigmoidQuery

The sigmoid function is an extension of saturation which adds a configurable exponent. Scores are computed as S^exp^ / (S^exp^ + pivot^exp^). Like for the saturation function, pivot is the value of S that gives a score of 0.5 and scores are (0,1).

The exponent must be positive and is typically in [0.5, 1]. A good value should be computed via training. If you don’t have the opportunity to do so, we recommend you use the saturation function instead.

Source

pub fn linear(self) -> RankFeatureLinearQuery

The linear function is the simplest function, and gives a score equal to the indexed value of S, where S is the value of the rank feature field. If a rank feature field is indexed with "positive_score_impact": true, its indexed value is equal to S and rounded to preserve only 9 significant bits for the precision. If a rank feature field is indexed with "positive_score_impact": false, its indexed value is equal to 1/S and rounded to preserve only 9 significant bits for the precision.

Source

pub fn boost<T>(self, boost: T) -> Self
where T: AsPrimitive<f32>,

Floating point number used to decrease or increase the relevance scores of a query. Defaults to 1.0.

You can use the boost parameter to adjust relevance scores for searches containing two or more queries.

Boost values are relative to the default value of 1.0. A boost value between 0 and 1.0 decreases the relevance score. A value greater than 1.0 increases the relevance score.

Source

pub fn name<S>(self, name: S) -> Self
where S: ToString,

You can use named queries to track which queries matched returned documents. If named queries are used, the response includes a matched_queries property for each hit.

https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-bool-query.html#named-queries