本地英文版地址: ../en/search-rank-eval.html
Ranking Evaluation APIedit
Allows you to evaluate the quality of ranked search results over a set of typical search queries.
Descriptionedit
The ranking evaluation API allows you to evaluate the quality of ranked search
results over a set of typical search queries. Given this set of queries and a
list of manually rated documents, the _rank_eval endpoint calculates and
returns typical information retrieval metrics like mean reciprocal rank,
precision or discounted cumulative gain.
Search quality evaluation starts with looking at the users of your search application, and the things that they are searching for. Users have a specific information need; for example, they are looking for gift in a web shop or want to book a flight for their next holiday. They usually enter some search terms into a search box or some other web form. All of this information, together with meta information about the user (for example the browser, location, earlier preferences and so on) then gets translated into a query to the underlying search system.
The challenge for search engineers is to tweak this translation process from user entries to a concrete query, in such a way that the search results contain the most relevant information with respect to the user’s information need. This can only be done if the search result quality is evaluated constantly across a representative test suite of typical user queries, so that improvements in the rankings for one particular query don’t negatively affect the ranking for other types of queries.
In order to get started with search quality evaluation, you need three basic things:
- A collection of documents you want to evaluate your query performance against, usually one or more indices.
- A collection of typical search requests that users enter into your system.
- A set of document ratings that represent the documents' relevance with respect to a search request.
It is important to note that one set of document ratings is needed per test query, and that the relevance judgements are based on the information need of the user that entered the query.
The ranking evaluation API provides a convenient way to use this information in a ranking evaluation request to calculate different search evaluation metrics. This gives you a first estimation of your overall search quality, as well as a measurement to optimize against when fine-tuning various aspect of the query generation in your application.
Path parametersedit
-
<index> - (Required, string) Comma-separated list or wildcard expression of index names used to limit the request.
Query parametersedit
-
allow_no_indices -
(Optional, boolean) If
true, the request does not return an error if a wildcard expression or_allvalue retrieves only missing or closed indices.This parameter also applies to index aliases that point to a missing or closed index.
Defaults to
true. -
expand_wildcards -
(Optional, string) Controls what kind of indices that wildcard expressions can expand to. Multiple values are accepted when separated by a comma, as in
open,hidden. Valid values are:-
all - Expand to open and closed indices, including hidden indices.
-
open - Expand only to open indices.
-
closed - Expand only to closed indices.
-
hidden -
Expansion of wildcards will include hidden indices.
Must be combined with
open,closed, or both. -
none - Wildcard expressions are not accepted.
Defaults to
open. -
-
ignore_unavailable -
(Optional, boolean) If
true, missing or closed indices are not included in the response. Defaults tofalse.
Examplesedit
In its most basic form, a request to the _rank_eval endpoint has two sections:
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a set of typical search requests, together with their provided ratings |
|
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definition of the evaluation metric to calculate |
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a specific metric and its parameters |
The request section contains several search requests typical to your application, along with the document ratings for each particular search request.
GET /my_index/_rank_eval
{
"requests": [
{
"id": "amsterdam_query",
"request": {
"query": { "match": { "text": "amsterdam" }}
},
"ratings": [
{ "_index": "my_index", "_id": "doc1", "rating": 0 },
{ "_index": "my_index", "_id": "doc2", "rating": 3},
{ "_index": "my_index", "_id": "doc3", "rating": 1 }
]
},
{
"id": "berlin_query",
"request": {
"query": { "match": { "text": "berlin" }}
},
"ratings": [
{ "_index": "my_index", "_id": "doc1", "rating": 1 }
]
}
]
}
|
the search request’s id, used to group result details later |
|
|
the query that is being evaluated |
|
|
a list of document ratings, each entry containing the document’s |
A document rating can be any integer value that expresses the relevance of the
document on a user-defined scale. For some of the metrics, just giving a binary
rating (for example 0 for irrelevant and 1 for relevant) will be sufficient,
while other metrics can use a more fine-grained scale.
Template-based ranking evaluationedit
As an alternative to having to provide a single query per test request, it is
possible to specify query templates in the evaluation request and later refer to
them. This way, queries with a similar structure that differ only in their
parameters don’t have to be repeated all the time in the requests section.
In typical search systems, where user inputs usually get filled into a small
set of query templates, this helps make the evaluation request more succinct.
GET /my_index/_rank_eval
{
[...]
"templates": [
{
"id": "match_one_field_query",
"template": {
"inline": {
"query": {
"match": { "{{field}}": { "query": "{{query_string}}" }}
}
}
}
}
],
"requests": [
{
"id": "amsterdam_query"
"ratings": [ ... ],
"template_id": "match_one_field_query",
"params": {
"query_string": "amsterdam",
"field": "text"
}
},
[...]
}
|
the template id |
|
|
the template definition to use |
|
|
a reference to a previously defined template |
|
|
the parameters to use to fill the template |
It is also possible to use templates already stored in the cluster state by referencing their id in the templates section.
Available evaluation metricsedit
The metric section determines which of the available evaluation metrics
will be used. The following metrics are supported:
Precision at K (P@k)edit
This metric measures the proportion of relevant results in the top k search results. It’s a form of the well-known Precision metric that only looks at the top k documents. It is the fraction of relevant documents in those first k results. A precision at 10 (P@10) value of 0.6 then means 6 out of the 10 top hits are relevant with respect to the user’s information need.
P@k works well as a simple evaluation metric that has the benefit of being easy to understand and explain. Documents in the collection need to be rated as either relevant or irrelevant with respect to the current query. P@k is a set-based metric and does not take into account the position of the relevant documents within the top k results, so a ranking of ten results that contains one relevant result in position 10 is equally as good as a ranking of ten results that contains one relevant result in position 1.
GET /twitter/_rank_eval
{
"requests": [
{
"id": "JFK query",
"request": { "query": { "match_all": {}}},
"ratings": []
}],
"metric": {
"precision": {
"k" : 20,
"relevant_rating_threshold": 1,
"ignore_unlabeled": false
}
}
}
The precision metric takes the following optional parameters
| Parameter | Description |
|---|---|
|
sets the maximum number of documents retrieved per query. This value will act in place of the usual |
|
sets the rating threshold above which documents are considered to be
"relevant". Defaults to |
|
controls how unlabeled documents in the search results are counted. If set to true, unlabeled documents are ignored and neither count as relevant or irrelevant. Set to false (the default), they are treated as irrelevant. |
Recall at K (R@k)edit
This metric measures the total number of relevant results in the top k search results. It’s a form of the well-known Recall metric. It is the fraction of relevant documents in those first k results relative to all possible relevant results. A recall at 10 (R@10) value of 0.5 then means 4 out of 8 relevant documents, with respect to the user’s information need, were retrieved in the 10 top hits.
R@k works well as a simple evaluation metric that has the benefit of being easy to understand and explain. Documents in the collection need to be rated as either relevant or irrelevant with respect to the current query. R@k is a set-based metric and does not take into account the position of the relevant documents within the top k results, so a ranking of ten results that contains one relevant result in position 10 is equally as good as a ranking of ten results that contains one relevant result in position 1.
GET /twitter/_rank_eval
{
"requests": [
{
"id": "JFK query",
"request": { "query": { "match_all": {}}},
"ratings": []
}],
"metric": {
"recall": {
"k" : 20,
"relevant_rating_threshold": 1
}
}
}
The recall metric takes the following optional parameters
| Parameter | Description |
|---|---|
|
sets the maximum number of documents retrieved per query. This value will act in place of the usual |
|
sets the rating threshold above which documents are considered to be
"relevant". Defaults to |
Mean reciprocal rankedit
For every query in the test suite, this metric calculates the reciprocal of the rank of the first relevant document. For example, finding the first relevant result in position 3 means the reciprocal rank is 1/3. The reciprocal rank for each query is averaged across all queries in the test suite to give the mean reciprocal rank.
GET /twitter/_rank_eval
{
"requests": [
{
"id": "JFK query",
"request": { "query": { "match_all": {}}},
"ratings": []
}],
"metric": {
"mean_reciprocal_rank": {
"k" : 20,
"relevant_rating_threshold" : 1
}
}
}
The mean_reciprocal_rank metric takes the following optional parameters
| Parameter | Description |
|---|---|
|
sets the maximum number of documents retrieved per query. This value will act in place of the usual |
|
Sets the rating threshold above which documents are considered to be
"relevant". Defaults to |
Discounted cumulative gain (DCG)edit
In contrast to the two metrics above, discounted cumulative gain takes both the rank and the rating of the search results into account.
The assumption is that highly relevant documents are more useful for the user when appearing at the top of the result list. Therefore, the DCG formula reduces the contribution that high ratings for documents on lower search ranks have on the overall DCG metric.
GET /twitter/_rank_eval
{
"requests": [
{
"id": "JFK query",
"request": { "query": { "match_all": {}}},
"ratings": []
}],
"metric": {
"dcg": {
"k" : 20,
"normalize": false
}
}
}
The dcg metric takes the following optional parameters:
| Parameter | Description |
|---|---|
|
sets the maximum number of documents retrieved per query. This value will act in place of the usual |
|
If set to |
Expected Reciprocal Rank (ERR)edit
Expected Reciprocal Rank (ERR) is an extension of the classical reciprocal rank for the graded relevance case (Olivier Chapelle, Donald Metzler, Ya Zhang, and Pierre Grinspan. 2009. Expected reciprocal rank for graded relevance.)
It is based on the assumption of a cascade model of search, in which a user scans through ranked search results in order and stops at the first document that satisfies the information need. For this reason, it is a good metric for question answering and navigation queries, but less so for survey-oriented information needs where the user is interested in finding many relevant documents in the top k results.
The metric models the expectation of the reciprocal of the position at which a user stops reading through the result list. This means that a relevant document in a top ranking position will have a large contribution to the overall score. However, the same document will contribute much less to the score if it appears in a lower rank; even more so if there are some relevant (but maybe less relevant) documents preceding it. In this way, the ERR metric discounts documents that are shown after very relevant documents. This introduces a notion of dependency in the ordering of relevant documents that e.g. Precision or DCG don’t account for.
GET /twitter/_rank_eval
{
"requests": [
{
"id": "JFK query",
"request": { "query": { "match_all": {}}},
"ratings": []
}],
"metric": {
"expected_reciprocal_rank": {
"maximum_relevance" : 3,
"k" : 20
}
}
}
The expected_reciprocal_rank metric takes the following parameters:
| Parameter | Description |
|---|---|
|
Mandatory parameter. The highest relevance grade used in the user-supplied relevance judgments. |
|
sets the maximum number of documents retrieved per query. This value will act in place of the usual |
Response formatedit
The response of the _rank_eval endpoint contains the overall calculated result
for the defined quality metric, a details section with a breakdown of results
for each query in the test suite and an optional failures section that shows
potential errors of individual queries. The response has the following format:
{
"rank_eval": {
"metric_score": 0.4,
"details": {
"my_query_id1": {
"metric_score": 0.6,
"unrated_docs": [
{
"_index": "my_index",
"_id": "1960795"
}, [...]
],
"hits": [
{
"hit": {
"_index": "my_index",
"_type": "page",
"_id": "1528558",
"_score": 7.0556192
},
"rating": 1
}, [...]
],
"metric_details": {
"precision" : {
"relevant_docs_retrieved": 6,
"docs_retrieved": 10
}
}
},
"my_query_id2" : { [...] }
},
"failures": { [...] }
}
}
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the overall evaluation quality calculated by the defined metric |
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