elasticsearch/docs/reference/query-dsl/rank-feature-query.asciidoc

[[query-dsl-rank-feature-query]]
=== Rank feature query
++++
<titleabbrev>Rank feature</titleabbrev>
++++

Boosts the <<relevance-scores,relevance score>> of documents based on the
numeric value of a <<rank-feature,`rank_feature`>> or
<<rank-features,`rank_features`>> field.

The `rank_feature` query is typically used in the `should` clause of a
<<query-dsl-bool-query,`bool`>> query so its relevance scores are added to other
scores from the `bool` query.

Unlike the <<query-dsl-function-score-query,`function_score`>> query or other
ways to change <<relevance-scores,relevance scores>>, the
`rank_feature` query efficiently skips non-competitive hits when the
<<search-uri-request,`track_total_hits`>> parameter is **not** `true`. This can
dramatically improve query speed.

[[rank-feature-query-functions]]
==== Rank feature functions

To calculate relevance scores based on rank feature fields, the `rank_feature`
query supports the following mathematical functions:

* <<rank-feature-query-saturation,Saturation>>
* <<rank-feature-query-logarithm,Logarithm>>
* <<rank-feature-query-sigmoid,Sigmoid>>
* <<rank-feature-query-linear,Linear>>

If you don't know where to start, we recommend using the `saturation` function.
If no function is provided, the `rank_feature` query uses the `saturation`
function by default.

[[rank-feature-query-ex-request]]
==== Example request

[[rank-feature-query-index-setup]]
===== Index setup

To use the `rank_feature` query, your index must include a
<<rank-feature,`rank_feature`>> or <<rank-features,`rank_features`>> field
mapping. To see how you can set up an index for the `rank_feature` query, try
the following example.

Create a `test` index with the following field mappings:

- `pagerank`, a <<rank-feature,`rank_feature`>> field which measures the
importance of a website
- `url_length`, a <<rank-feature,`rank_feature`>> field which contains the
length of the website's URL. For this example, a long URL correlates negatively
to relevance, indicated by a `positive_score_impact` value of `false`.
- `topics`, a <<rank-features,`rank_features`>> field which contains a list of
topics and a measure of how well each document is connected to this topic

[source,console]
----
PUT /test
{
  "mappings": {
    "properties": {
      "pagerank": {
        "type": "rank_feature"
      },
      "url_length": {
        "type": "rank_feature",
        "positive_score_impact": false
      },
      "topics": {
        "type": "rank_features"
      }
    }
  }
}
----
// TESTSETUP


Index several documents to the `test` index.

[source,console]
----
PUT /test/_doc/1?refresh
{
  "url": "https://en.wikipedia.org/wiki/2016_Summer_Olympics",
  "content": "Rio 2016",
  "pagerank": 50.3,
  "url_length": 42,
  "topics": {
    "sports": 50,
    "brazil": 30
  }
}

PUT /test/_doc/2?refresh
{
  "url": "https://en.wikipedia.org/wiki/2016_Brazilian_Grand_Prix",
  "content": "Formula One motor race held on 13 November 2016",
  "pagerank": 50.3,
  "url_length": 47,
  "topics": {
    "sports": 35,
    "formula one": 65,
    "brazil": 20
  }
}

PUT /test/_doc/3?refresh
{
  "url": "https://en.wikipedia.org/wiki/Deadpool_(film)",
  "content": "Deadpool is a 2016 American superhero film",
  "pagerank": 50.3,
  "url_length": 37,
  "topics": {
    "movies": 60,
    "super hero": 65
  }
}
----

[[rank-feature-query-ex-query]]
===== Example query

The following query searches for `2016` and boosts relevance scores based on
`pagerank`, `url_length`, and the `sports` topic.

[source,console]
----
GET /test/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "content": "2016"
          }
        }
      ],
      "should": [
        {
          "rank_feature": {
            "field": "pagerank"
          }
        },
        {
          "rank_feature": {
            "field": "url_length",
            "boost": 0.1
          }
        },
        {
          "rank_feature": {
            "field": "topics.sports",
            "boost": 0.4
          }
        }
      ]
    }
  }
}
----


[[rank-feature-top-level-params]]
==== Top-level parameters for `rank_feature`

`field`::
(Required, string) <<rank-feature,`rank_feature`>> or
<<rank-features,`rank_features`>> field used to boost
<<relevance-scores,relevance scores>>.

`boost`::
+
--
(Optional, float) Floating point number used to decrease or increase
<<relevance-scores,relevance scores>>. Defaults to `1.0`.

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.
--

`saturation`::
+
--
(Optional, <<rank-feature-query-saturation,function object>>) Saturation
function used to boost <<relevance-scores,relevance scores>> based on the
value of the rank feature `field`. If no function is provided, the `rank_feature`
query defaults to the `saturation` function. See
<<rank-feature-query-saturation,Saturation>> for more information.

Only one function `saturation`, `log`, `sigmoid` or `linear` can be provided.
--

`log`::
+
--
(Optional, <<rank-feature-query-logarithm,function object>>) Logarithmic
function used to boost <<relevance-scores,relevance scores>> based on the
value of the rank feature `field`. See
<<rank-feature-query-logarithm,Logarithm>> for more information.

Only one function `saturation`, `log`, `sigmoid` or `linear` can be provided.
--

`sigmoid`::
+
--
(Optional, <<rank-feature-query-sigmoid,function object>>) Sigmoid function used
to boost <<relevance-scores,relevance scores>> based on the value of the
rank feature `field`. See <<rank-feature-query-sigmoid,Sigmoid>> for more
information.

Only one function `saturation`, `log`, `sigmoid` or `linear` can be provided.
--

`linear`::
+
--
(Optional, <<rank-feature-query-linear,function object>>) Linear function used
to boost <<relevance-scores,relevance scores>> based on the value of the
rank feature `field`. See <<rank-feature-query-linear,Linear>> for more
information.

Only one function `saturation`, `log`, `sigmoid` or `linear` can be provided.
--


[[rank-feature-query-notes]]
==== Notes

[[rank-feature-query-saturation]]
===== Saturation
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.

[source,console]
--------------------------------------------------
GET /test/_search
{
  "query": {
    "rank_feature": {
      "field": "pagerank",
      "saturation": {
        "pivot": 8
      }
    }
  }
}
--------------------------------------------------

If a `pivot` value is not provided, {es} 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,console]
--------------------------------------------------
GET /test/_search
{
  "query": {
    "rank_feature": {
      "field": "pagerank",
      "saturation": {}
    }
  }
}
--------------------------------------------------

[[rank-feature-query-logarithm]]
===== Logarithm
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,console]
--------------------------------------------------
GET /test/_search
{
  "query": {
    "rank_feature": {
      "field": "pagerank",
      "log": {
        "scaling_factor": 4
      }
    }
  }
}
--------------------------------------------------

[[rank-feature-query-sigmoid]]
===== Sigmoid
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,console]
--------------------------------------------------
GET /test/_search
{
  "query": {
    "rank_feature": {
      "field": "pagerank",
      "sigmoid": {
        "pivot": 7,
        "exponent": 0.6
      }
    }
  }
}
--------------------------------------------------
[[rank-feature-query-linear]]
===== Linear
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,console]
--------------------------------------------------
GET /test/_search
{
  "query": {
    "rank_feature": {
      "field": "pagerank",
      "linear": {}
    }
  }
}
--------------------------------------------------