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use crate::search::*;
use crate::util::*;
use serde::Serialize;
/// 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:
/// ```
/// # use elasticsearch_dsl::queries::*;
/// # use elasticsearch_dsl::queries::params::*;
/// # let query =
/// Query::rank_feature("test");
/// ```
/// To apply mathematical functions:
/// ```
/// # use elasticsearch_dsl::queries::*;
/// # use elasticsearch_dsl::queries::params::*;
/// # let query =
/// Query::rank_feature("test").saturation();
/// # let query =
/// Query::rank_feature("test").saturation().pivot(2.2);
/// # let query =
/// Query::rank_feature("test").logarithm(3.0);
/// # let query =
/// Query::rank_feature("test").sigmoid(1.0, 2.0);
/// # let query =
/// Query::rank_feature("test").linear();
/// ```
/// <https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-rank-feature-query.html>
#[derive(Debug, Clone, PartialEq, Serialize)]
pub struct RankFeatureQuery {
#[serde(rename = "rank_feature")]
inner: Inner,
}
/// 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:
/// ```
/// # use elasticsearch_dsl::queries::*;
/// # use elasticsearch_dsl::queries::params::*;
/// # let query =
/// Query::rank_feature("test");
/// ```
/// To apply mathematical functions:
/// ```
/// # use elasticsearch_dsl::queries::*;
/// # use elasticsearch_dsl::queries::params::*;
/// # let query =
/// Query::rank_feature("test").saturation();
/// # let query =
/// Query::rank_feature("test").saturation().pivot(2.2);
/// # let query =
/// Query::rank_feature("test").logarithm(3.0);
/// # let query =
/// Query::rank_feature("test").sigmoid(1.0, 2.0);
/// # let query =
/// Query::rank_feature("test").linear();
/// ```
/// <https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-rank-feature-query.html>
#[derive(Debug, Clone, PartialEq, Serialize)]
pub struct RankFeatureSaturationQuery {
#[serde(rename = "rank_feature")]
inner: InnerSaturation,
}
/// 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:
/// ```
/// # use elasticsearch_dsl::queries::*;
/// # use elasticsearch_dsl::queries::params::*;
/// # let query =
/// Query::rank_feature("test");
/// ```
/// To apply mathematical functions:
/// ```
/// # use elasticsearch_dsl::queries::*;
/// # use elasticsearch_dsl::queries::params::*;
/// # let query =
/// Query::rank_feature("test").saturation();
/// # let query =
/// Query::rank_feature("test").saturation().pivot(2.2);
/// # let query =
/// Query::rank_feature("test").logarithm(3.0);
/// # let query =
/// Query::rank_feature("test").sigmoid(1.0, 2.0);
/// # let query =
/// Query::rank_feature("test").linear();
/// ```
/// <https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-rank-feature-query.html>
#[derive(Debug, Clone, PartialEq, Serialize)]
pub struct RankFeatureLogarithmQuery {
#[serde(rename = "rank_feature")]
inner: InnerLogarithm,
}
/// 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:
/// ```
/// # use elasticsearch_dsl::queries::*;
/// # use elasticsearch_dsl::queries::params::*;
/// # let query =
/// Query::rank_feature("test");
/// ```
/// To apply mathematical functions:
/// ```
/// # use elasticsearch_dsl::queries::*;
/// # use elasticsearch_dsl::queries::params::*;
/// # let query =
/// Query::rank_feature("test").saturation();
/// # let query =
/// Query::rank_feature("test").saturation().pivot(2.2);
/// # let query =
/// Query::rank_feature("test").logarithm(3.0);
/// # let query =
/// Query::rank_feature("test").sigmoid(1.0, 2.0);
/// # let query =
/// Query::rank_feature("test").linear();
/// ```
/// <https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-rank-feature-query.html>
#[derive(Debug, Clone, PartialEq, Serialize)]
pub struct RankFeatureSigmoidQuery {
#[serde(rename = "rank_feature")]
inner: InnerSigmoid,
}
/// 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:
/// ```
/// # use elasticsearch_dsl::queries::*;
/// # use elasticsearch_dsl::queries::params::*;
/// # let query =
/// Query::rank_feature("test");
/// ```
/// To apply mathematical functions:
/// ```
/// # use elasticsearch_dsl::queries::*;
/// # use elasticsearch_dsl::queries::params::*;
/// # let query =
/// Query::rank_feature("test").saturation();
/// # let query =
/// Query::rank_feature("test").saturation().pivot(2.2);
/// # let query =
/// Query::rank_feature("test").logarithm(3.0);
/// # let query =
/// Query::rank_feature("test").sigmoid(1.0, 2.0);
/// # let query =
/// Query::rank_feature("test").linear();
/// ```
/// <https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-rank-feature-query.html>
#[derive(Debug, Clone, PartialEq, Serialize)]
pub struct RankFeatureLinearQuery {
#[serde(rename = "rank_feature")]
inner: InnerLinear,
}
#[derive(Debug, Clone, PartialEq, Serialize)]
struct Saturation {
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
pivot: Option<f64>,
}
#[derive(Debug, Clone, PartialEq, Serialize)]
struct Logarithm {
scaling_factor: f64,
}
#[derive(Debug, Clone, PartialEq, Serialize)]
struct Sigmoid {
pivot: f64,
exponent: f64,
}
#[derive(Debug, Clone, PartialEq, Serialize)]
struct Linear {}
#[derive(Debug, Clone, PartialEq, Serialize)]
struct Inner {
field: String,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
boost: Option<Boost>,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
_name: Option<String>,
}
#[derive(Debug, Clone, PartialEq, Serialize)]
struct InnerSaturation {
field: String,
saturation: Saturation,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
boost: Option<Boost>,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
_name: Option<String>,
}
#[derive(Debug, Clone, PartialEq, Serialize)]
struct InnerLogarithm {
field: String,
log: Logarithm,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
boost: Option<Boost>,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
_name: Option<String>,
}
#[derive(Debug, Clone, PartialEq, Serialize)]
struct InnerSigmoid {
field: String,
sigmoid: Sigmoid,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
boost: Option<Boost>,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
_name: Option<String>,
}
#[derive(Debug, Clone, PartialEq, Serialize)]
struct InnerLinear {
field: String,
linear: Linear,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
boost: Option<Boost>,
#[serde(skip_serializing_if = "ShouldSkip::should_skip")]
_name: Option<String>,
}
impl Query {
/// Creates an instance of [`RankFeatureQuery`]
///
/// - `field` - `rank_feature` or `rank_features` field used to boost relevance scores
pub fn rank_feature(field: impl Into<String>) -> RankFeatureQuery {
RankFeatureQuery {
inner: Inner {
field: field.into(),
boost: None,
_name: None,
},
}
}
}
impl RankFeatureQuery {
/// 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.
pub fn saturation(self) -> RankFeatureSaturationQuery {
RankFeatureSaturationQuery {
inner: InnerSaturation {
field: self.inner.field,
boost: self.inner.boost,
_name: self.inner._name,
saturation: Saturation { pivot: None },
},
}
}
/// 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.
pub fn logarithm(self, scaling_factor: f64) -> RankFeatureLogarithmQuery {
RankFeatureLogarithmQuery {
inner: InnerLogarithm {
field: self.inner.field,
boost: self.inner.boost,
_name: self.inner._name,
log: Logarithm { scaling_factor },
},
}
}
/// 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.
pub fn sigmoid(self, pivot: f64, exponent: f64) -> RankFeatureSigmoidQuery {
RankFeatureSigmoidQuery {
inner: InnerSigmoid {
field: self.inner.field,
boost: self.inner.boost,
_name: self.inner._name,
sigmoid: Sigmoid { pivot, exponent },
},
}
}
/// 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.
pub fn linear(self) -> RankFeatureLinearQuery {
RankFeatureLinearQuery {
inner: InnerLinear {
field: self.inner.field,
boost: self.inner.boost,
_name: self.inner._name,
linear: Linear {},
},
}
}
add_boost_and_name!();
}
impl RankFeatureSaturationQuery {
/// Sets pivot value
pub fn pivot(mut self, pivot: impl Into<Option<f64>>) -> Self {
self.inner.saturation.pivot = pivot.into();
self
}
add_boost_and_name!();
}
impl RankFeatureLogarithmQuery {
add_boost_and_name!();
}
impl RankFeatureSigmoidQuery {
add_boost_and_name!();
}
impl RankFeatureLinearQuery {
add_boost_and_name!();
}
impl ShouldSkip for RankFeatureQuery {}
impl ShouldSkip for RankFeatureSaturationQuery {}
impl ShouldSkip for RankFeatureLogarithmQuery {}
impl ShouldSkip for RankFeatureSigmoidQuery {}
impl ShouldSkip for RankFeatureLinearQuery {}
#[cfg(test)]
mod tests {
use super::*;
test_serialization! {
with_required_fields(
Query::rank_feature("test"),
json!({
"rank_feature": {
"field": "test",
}
})
);
with_all_fields(
Query::rank_feature("test").boost(2).name("query"),
json!({
"rank_feature": {
"field": "test",
"boost": 2,
"_name": "query",
}
})
);
with_saturation(
Query::rank_feature("test").saturation().boost(2).name("query"),
json!({
"rank_feature": {
"field": "test",
"boost": 2,
"_name": "query",
"saturation": {},
}
})
);
with_saturation_and_pivot_value(
Query::rank_feature("test").saturation().pivot(2.2).boost(2).name("query"),
json!({
"rank_feature": {
"field": "test",
"boost": 2,
"_name": "query",
"saturation": {
"pivot": 2.2,
},
}
})
);
with_logarithm(
Query::rank_feature("test").logarithm(2.2).boost(2).name("query"),
json!({
"rank_feature": {
"field": "test",
"boost": 2,
"_name": "query",
"log": {
"scaling_factor": 2.2
},
}
})
);
with_sigmoid(
Query::rank_feature("test").sigmoid(2.2, 3.3).boost(2).name("query"),
json!({
"rank_feature": {
"field": "test",
"boost": 2,
"_name": "query",
"sigmoid": {
"pivot": 2.2,
"exponent": 3.3,
},
}
})
);
with_linear(
Query::rank_feature("test").linear().boost(2).name("query"),
json!({
"rank_feature": {
"field": "test",
"boost": 2,
"_name": "query",
"linear": {},
}
})
);
}
}