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Plugin discovery documentation contained information about installing Elasticsearch 2.0 and installing an oracle JDK, both of which is no longer valid. While noticing that the instructions used cleartext HTTP to install packages, this commit replaces HTTPs links instead of HTTP where possible. In addition a few community links have been removed, as they do not seem to exist anymore. Co-authored-by: Alexander Reelsen <alexander@reelsen.net>
314 lines
8.2 KiB
Plaintext
314 lines
8.2 KiB
Plaintext
[[query-dsl-rank-feature-query]]
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=== Rank feature query
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++++
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<titleabbrev>Rank feature</titleabbrev>
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++++
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Boosts the <<relevance-scores,relevance score>> of documents based on the
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numeric value of a <<rank-feature,`rank_feature`>> or
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<<rank-features,`rank_features`>> field.
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The `rank_feature` query is typically used in the `should` clause of a
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<<query-dsl-bool-query,`bool`>> query so its relevance scores are added to other
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scores from the `bool` query.
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Unlike the <<query-dsl-function-score-query,`function_score`>> query or other
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ways to change <<relevance-scores,relevance scores>>, the
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`rank_feature` query efficiently skips non-competitive hits when the
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<<search-uri-request,`track_total_hits`>> parameter is **not** `true`. This can
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dramatically improve query speed.
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[[rank-feature-query-functions]]
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==== Rank feature functions
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To calculate relevance scores based on rank feature fields, the `rank_feature`
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query supports the following mathematical functions:
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* <<rank-feature-query-saturation,Saturation>>
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* <<rank-feature-query-logarithm,Logarithm>>
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* <<rank-feature-query-sigmoid,Sigmoid>>
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If you don't know where to start, we recommend using the `saturation` function.
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If no function is provided, the `rank_feature` query uses the `saturation`
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function by default.
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[[rank-feature-query-ex-request]]
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==== Example request
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[[rank-feature-query-index-setup]]
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===== Index setup
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To use the `rank_feature` query, your index must include a
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<<rank-feature,`rank_feature`>> or <<rank-features,`rank_features`>> field
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mapping. To see how you can set up an index for the `rank_feature` query, try
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the following example.
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Create a `test` index with the following field mappings:
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- `pagerank`, a <<rank-feature,`rank_feature`>> field which measures the
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importance of a website
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- `url_length`, a <<rank-feature,`rank_feature`>> field which contains the
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length of the website's URL. For this example, a long URL correlates negatively
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to relevance, indicated by a `positive_score_impact` value of `false`.
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- `topics`, a <<rank-features,`rank_features`>> field which contains a list of
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topics and a measure of how well each document is connected to this topic
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[source,console]
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----
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PUT /test
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{
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"mappings": {
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"properties": {
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"pagerank": {
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"type": "rank_feature"
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},
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"url_length": {
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"type": "rank_feature",
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"positive_score_impact": false
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},
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"topics": {
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"type": "rank_features"
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}
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}
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}
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}
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----
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// TESTSETUP
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Index several documents to the `test` index.
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[source,console]
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----
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PUT /test/_doc/1?refresh
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{
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"url": "https://en.wikipedia.org/wiki/2016_Summer_Olympics",
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"content": "Rio 2016",
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"pagerank": 50.3,
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"url_length": 42,
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"topics": {
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"sports": 50,
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"brazil": 30
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}
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}
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PUT /test/_doc/2?refresh
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{
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"url": "https://en.wikipedia.org/wiki/2016_Brazilian_Grand_Prix",
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"content": "Formula One motor race held on 13 November 2016",
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"pagerank": 50.3,
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"url_length": 47,
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"topics": {
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"sports": 35,
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"formula one": 65,
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"brazil": 20
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}
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}
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PUT /test/_doc/3?refresh
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{
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"url": "https://en.wikipedia.org/wiki/Deadpool_(film)",
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"content": "Deadpool is a 2016 American superhero film",
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"pagerank": 50.3,
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"url_length": 37,
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"topics": {
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"movies": 60,
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"super hero": 65
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}
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}
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----
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[[rank-feature-query-ex-query]]
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===== Example query
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The following query searches for `2016` and boosts relevance scores based on
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`pagerank`, `url_length`, and the `sports` topic.
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[source,console]
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----
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GET /test/_search
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{
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"query": {
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"bool": {
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"must": [
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{
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"match": {
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"content": "2016"
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}
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}
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],
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"should": [
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{
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"rank_feature": {
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"field": "pagerank"
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}
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},
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{
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"rank_feature": {
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"field": "url_length",
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"boost": 0.1
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}
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},
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{
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"rank_feature": {
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"field": "topics.sports",
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"boost": 0.4
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}
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}
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]
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}
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}
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}
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----
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[[rank-feature-top-level-params]]
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==== Top-level parameters for `rank_feature`
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`field`::
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(Required, string) <<rank-feature,`rank_feature`>> or
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<<rank-features,`rank_features`>> field used to boost
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<<relevance-scores,relevance scores>>.
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`boost`::
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+
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--
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(Optional, float) Floating point number used to decrease or increase
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<<relevance-scores,relevance scores>>. Defaults to `1.0`.
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Boost values are relative to the default value of `1.0`. A boost value between
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`0` and `1.0` decreases the relevance score. A value greater than `1.0`
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increases the relevance score.
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--
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`saturation`::
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+
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--
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(Optional, <<rank-feature-query-saturation,function object>>) Saturation
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function used to boost <<relevance-scores,relevance scores>> based on the
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value of the rank feature `field`. If no function is provided, the `rank_feature`
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query defaults to the `saturation` function. See
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<<rank-feature-query-saturation,Saturation>> for more information.
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Only one function `saturation`, `log`, or `sigmoid` can be provided.
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--
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`log`::
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+
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--
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(Optional, <<rank-feature-query-logarithm,function object>>) Logarithmic
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function used to boost <<relevance-scores,relevance scores>> based on the
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value of the rank feature `field`. See
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<<rank-feature-query-logarithm,Logarithm>> for more information.
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Only one function `saturation`, `log`, or `sigmoid` can be provided.
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--
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`sigmoid`::
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+
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--
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(Optional, <<rank-feature-query-sigmoid,function object>>) Sigmoid function used
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to boost <<relevance-scores,relevance scores>> based on the value of the
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rank feature `field`. See <<rank-feature-query-sigmoid,Sigmoid>> for more
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information.
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Only one function `saturation`, `log`, or `sigmoid` can be provided.
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--
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[[rank-feature-query-notes]]
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==== Notes
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[[rank-feature-query-saturation]]
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===== Saturation
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The `saturation` function gives a score equal to `S / (S + pivot)`, where `S` is
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the value of the rank feature field and `pivot` is a configurable pivot value so
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that the result will be less than `0.5` if `S` is less than pivot and greater
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than `0.5` otherwise. Scores are always `(0,1)`.
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If the rank feature has a negative score impact then the function will be
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computed as `pivot / (S + pivot)`, which decreases when `S` increases.
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[source,console]
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--------------------------------------------------
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GET /test/_search
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{
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"query": {
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"rank_feature": {
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"field": "pagerank",
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"saturation": {
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"pivot": 8
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}
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}
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}
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}
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--------------------------------------------------
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If a `pivot` value is not provided, {es} computes a default value equal to the
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approximate geometric mean of all rank feature values in the index. We recommend
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using this default value if you haven't had the opportunity to train a good
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pivot value.
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[source,console]
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--------------------------------------------------
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GET /test/_search
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{
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"query": {
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"rank_feature": {
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"field": "pagerank",
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"saturation": {}
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}
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}
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}
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--------------------------------------------------
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[[rank-feature-query-logarithm]]
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===== Logarithm
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The `log` function gives a score equal to `log(scaling_factor + S)`, where `S`
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is the value of the rank feature field and `scaling_factor` is a configurable
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scaling factor. Scores are unbounded.
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This function only supports rank features that have a positive score impact.
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[source,console]
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--------------------------------------------------
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GET /test/_search
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{
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"query": {
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"rank_feature": {
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"field": "pagerank",
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"log": {
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"scaling_factor": 4
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}
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}
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}
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}
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--------------------------------------------------
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[[rank-feature-query-sigmoid]]
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===== Sigmoid
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The `sigmoid` function is an extension of `saturation` which adds a configurable
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exponent. Scores are computed as `S^exp^ / (S^exp^ + pivot^exp^)`. Like for the
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`saturation` function, `pivot` is the value of `S` that gives a score of `0.5`
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and scores are `(0,1)`.
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The `exponent` must be positive and is typically in `[0.5, 1]`. A
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good value should be computed via training. If you don't have the opportunity to
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do so, we recommend you use the `saturation` function instead.
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[source,console]
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--------------------------------------------------
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GET /test/_search
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{
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"query": {
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"rank_feature": {
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"field": "pagerank",
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"sigmoid": {
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"pivot": 7,
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"exponent": 0.6
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}
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}
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}
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}
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--------------------------------------------------
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