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[ML] rename frequent_items to frequent_item_sets and make it GA (#93421)

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rename frequent_items to frequent_item_sets and remove the experimental batch
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Hendrik Muhs 2023-02-02 09:25:00 +01:00 • committed by GitHub
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10
docs/changelog/93421.yaml Normal file
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@ -0,0 +1,10 @@
pr: 93421
summary: Make `frequent_item_sets` aggregation GA
area: Machine Learning
type: feature
issues: []
highlight:
title: Make `frequent_item_sets` aggregation GA
body: The `frequent_item_sets` aggregation has been moved from
technical preview to general availability.
notable: true

2
docs/reference/aggregations/bucket.asciidoc
View file

@ -36,7 +36,7 @@ include::bucket/filter-aggregation.asciidoc[]
include::bucket/filters-aggregation.asciidoc[] include::bucket/filters-aggregation.asciidoc[]
include::bucket/frequent-items-aggregation.asciidoc[] include::bucket/frequent-item-sets-aggregation.asciidoc[]
include::bucket/geodistance-aggregation.asciidoc[] include::bucket/geodistance-aggregation.asciidoc[]

166
docs/reference/aggregations/bucket/frequent-items-aggregation.asciidoc → docs/reference/aggregations/bucket/frequent-item-sets-aggregation.asciidoc
View file

@ -1,40 +1,38 @@
[[search-aggregations-bucket-frequent-items-aggregation]] [[search-aggregations-bucket-frequent-item-sets-aggregation]]
=== Frequent items aggregation === Frequent item sets aggregation
++++ ++++
<titleabbrev>Frequent items</titleabbrev> <titleabbrev>Frequent item sets</titleabbrev>
++++ ++++
experimental::[] A bucket aggregation which finds frequent item sets. It is a form of association
rules mining that identifies items that often occur together. Items that are
A bucket aggregation which finds frequent item sets. It is a form of association frequently purchased together or log events that tend to co-occur are examples
rules mining that identifies items that often occur together. Items that are of frequent item sets. Finding frequent item sets helps to discover
frequently purchased together or log events that tend to co-occur are examples
of frequent item sets. Finding frequent item sets helps to discover
relationships between different data points (items). relationships between different data points (items).
The aggregation reports closed item sets. A frequent item set is called closed The aggregation reports closed item sets. A frequent item set is called closed
if no superset exists with the same ratio of documents (also known as its if no superset exists with the same ratio of documents (also known as its
<<frequent-items-minimum-support,support value>>). For example, we have the two <<frequent-item-sets-minimum-support,support value>>). For example, we have the two
following candidates for a frequent item set, which have the same support value: following candidates for a frequent item set, which have the same support value:
1. `apple, orange, banana` 1. `apple, orange, banana`
2. `apple, orange, banana, tomato`. 2. `apple, orange, banana, tomato`.
Only the second item set (`apple, orange, banana, tomato`) is returned, and the Only the second item set (`apple, orange, banana, tomato`) is returned, and the
first set – which is a subset of the second one – is skipped. Both item sets first set – which is a subset of the second one – is skipped. Both item sets
might be returned if their support values are different. might be returned if their support values are different.
The runtime of the aggregation depends on the data and the provided parameters. The runtime of the aggregation depends on the data and the provided parameters.
It might take a significant time for the aggregation to complete. For this It might take a significant time for the aggregation to complete. For this
reason, it is recommended to use <<async-search,async search>> to run your reason, it is recommended to use <<async-search,async search>> to run your
requests asynchronously. requests asynchronously.
==== Syntax ==== Syntax
A `frequent_items` aggregation looks like this in isolation: A `frequent_item_sets` aggregation looks like this in isolation:
[source,js] [source,js]
-------------------------------------------------- --------------------------------------------------
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 3, "minimum_set_size": 3,
"fields": [ "fields": [
{"field": "my_field_1"}, {"field": "my_field_1"},
@ -44,74 +42,74 @@ A `frequent_items` aggregation looks like this in isolation:
-------------------------------------------------- --------------------------------------------------
// NOTCONSOLE // NOTCONSOLE
.`frequent_items` Parameters .`frequent_item_sets` Parameters
|=== |===
|Parameter Name |Description |Required |Default Value |Parameter Name |Description |Required |Default Value
|`fields` |(array) Fields to analyze. | Required | |`fields` |(array) Fields to analyze. | Required |
|`minimum_set_size` | (integer) The <<frequent-items-minimum-set-size,minimum size>> of one item set. | Optional | `1` |`minimum_set_size` | (integer) The <<frequent-item-sets-minimum-set-size,minimum size>> of one item set. | Optional | `1`
|`minimum_support` | (integer) The <<frequent-items-minimum-support,minimum support>> of one item set. | Optional | `0.1` |`minimum_support` | (integer) The <<frequent-item-sets-minimum-support,minimum support>> of one item set. | Optional | `0.1`
|`size` | (integer) The number of top item sets to return. | Optional | `10` |`size` | (integer) The number of top item sets to return. | Optional | `10`
|`filter` | (object) Query that filters documents from the analysis | Optional | `match_all` |`filter` | (object) Query that filters documents from the analysis | Optional | `match_all`
|=== |===
[discrete] [discrete]
[[frequent-items-fields]] [[frequent-item-sets-fields]]
==== Fields ==== Fields
Supported field types for the analyzed fields are keyword, numeric, ip, date, Supported field types for the analyzed fields are keyword, numeric, ip, date,
and arrays of these types. You can also add runtime fields to your analyzed and arrays of these types. You can also add runtime fields to your analyzed
fields. fields.
If the combined cardinality of the analyzed fields are high, the aggregation If the combined cardinality of the analyzed fields are high, the aggregation
might require a significant amount of system resources. might require a significant amount of system resources.
You can filter the values for each field by using the `include` and `exclude` You can filter the values for each field by using the `include` and `exclude`
parameters. The parameters can be regular expression strings or arrays of parameters. The parameters can be regular expression strings or arrays of
strings of exact terms. The filtered values are removed from the analysis and strings of exact terms. The filtered values are removed from the analysis and
therefore reduce the runtime. If both `include` and `exclude` are defined, therefore reduce the runtime. If both `include` and `exclude` are defined,
`exclude` takes precedence; it means `include` is evaluated first and then `exclude` takes precedence; it means `include` is evaluated first and then
`exclude`. `exclude`.
[discrete] [discrete]
[[frequent-items-minimum-set-size]] [[frequent-item-sets-minimum-set-size]]
==== Minimum set size ==== Minimum set size
The minimum set size is the minimum number of items the set needs to contain. A The minimum set size is the minimum number of items the set needs to contain. A
value of 1 returns the frequency of single items. Only item sets that contain at value of 1 returns the frequency of single items. Only item sets that contain at
least the number of `minimum_set_size` items are returned. For example, the item least the number of `minimum_set_size` items are returned. For example, the item
set `orange, banana, apple` is returned only if the minimum set size is 3 or set `orange, banana, apple` is returned only if the minimum set size is 3 or
lower. lower.
[discrete] [discrete]
[[frequent-items-minimum-support]] [[frequent-item-sets-minimum-support]]
==== Minimum support ==== Minimum support
The minimum support value is the ratio of documents that an item set must exist The minimum support value is the ratio of documents that an item set must exist
in to be considered "frequent". In particular, it is a normalized value between in to be considered "frequent". In particular, it is a normalized value between
0 and 1. It is calculated by dividing the number of documents containing the 0 and 1. It is calculated by dividing the number of documents containing the
item set by the total number of documents. item set by the total number of documents.
For example, if a given item set is contained by five documents and the total For example, if a given item set is contained by five documents and the total
number of documents is 20, then the support of the item set is 5/20 = 0.25. number of documents is 20, then the support of the item set is 5/20 = 0.25.
Therefore, this set is returned only if the minimum support is 0.25 or lower. Therefore, this set is returned only if the minimum support is 0.25 or lower.
As a higher minimum support prunes more items, the calculation is less resource As a higher minimum support prunes more items, the calculation is less resource
intensive. The `minimum_support` parameter has an effect on the required memory intensive. The `minimum_support` parameter has an effect on the required memory
and the runtime of the aggregation. and the runtime of the aggregation.
[discrete] [discrete]
[[frequent-items-size]] [[frequent-item-sets-size]]
==== Size ==== Size
This parameter defines the maximum number of item sets to return. The result This parameter defines the maximum number of item sets to return. The result
contains top-k item sets; the item sets with the highest support values. This contains top-k item sets; the item sets with the highest support values. This
parameter has a significant effect on the required memory and the runtime of the parameter has a significant effect on the required memory and the runtime of the
aggregation. aggregation.
[discrete] [discrete]
[[frequent-items-filter]] [[frequent-item-sets-filter]]
==== Filter ==== Filter
A query to filter documents to use as part of the analysis. Documents that A query to filter documents to use as part of the analysis. Documents that
@ -123,7 +121,7 @@ Use a top-level query to filter the data set.
[discrete] [discrete]
[[frequent-items-example]] [[frequent-item-sets-example]]
==== Examples ==== Examples
In the following examples, we use the e-commerce {kib} sample data set. In the following examples, we use the e-commerce {kib} sample data set.
@ -132,14 +130,14 @@ In the following examples, we use the e-commerce {kib} sample data set.
[discrete] [discrete]
==== Aggregation with two analyzed fields and an `exclude` parameter ==== Aggregation with two analyzed fields and an `exclude` parameter
In the first example, the goal is to find out based on transaction data (1.) In the first example, the goal is to find out based on transaction data (1.)
from what product categories the customers purchase products frequently together from what product categories the customers purchase products frequently together
and (2.) from which cities they make those purchases. We want to exclude results and (2.) from which cities they make those purchases. We want to exclude results
where location information is not available (where the city name is `other`). where location information is not available (where the city name is `other`).
Finally, we are interested in sets with three or more items, and want to see the Finally, we are interested in sets with three or more items, and want to see the
first three frequent item sets with the highest support. first three frequent item sets with the highest support.
Note that we use the <<async-search,async search>> endpoint in this first Note that we use the <<async-search,async search>> endpoint in this first
example. example.
[source,console] [source,console]
@ -149,7 +147,7 @@ POST /kibana_sample_data_ecommerce/_async_search
"size":0, "size":0,
"aggs":{ "aggs":{
"my_agg":{ "my_agg":{
"frequent_items":{ "frequent_item_sets":{
"minimum_set_size":3, "minimum_set_size":3,
"fields":[ "fields":[
{ {
@ -168,7 +166,7 @@ POST /kibana_sample_data_ecommerce/_async_search
------------------------------------------------- -------------------------------------------------
// TEST[skip:setup kibana sample data] // TEST[skip:setup kibana sample data]
The response of the API call above contains an identifier (`id`) of the async The response of the API call above contains an identifier (`id`) of the async
search request. You can use the identifier to retrieve the search results: search request. You can use the identifier to retrieve the search results:
[source,console] [source,console]
@ -225,27 +223,27 @@ The API returns a response similar to the following one:
"support" : 0.026310160427807486 "support" : 0.026310160427807486
} }
], ],
(...) (...)
} }
} }
------------------------------------------------- -------------------------------------------------
// TEST[skip:setup kibana sample data] // TEST[skip:setup kibana sample data]
<1> The array of returned item sets. <1> The array of returned item sets.
<2> The `key` object contains one item set. In this case, it consists of two <2> The `key` object contains one item set. In this case, it consists of two
values of the `category.keyword` field and one value of the `geoip.city_name`. values of the `category.keyword` field and one value of the `geoip.city_name`.
<3> The number of documents that contain the item set. <3> The number of documents that contain the item set.
<4> The support value of the item set. It is calculated by dividing the number <4> The support value of the item set. It is calculated by dividing the number
of documents containing the item set by the total number of documents. of documents containing the item set by the total number of documents.
The response shows that the categories customers purchase from most frequently The response shows that the categories customers purchase from most frequently
together are `Women's Clothing` and `Women's Shoes` and customers from New York together are `Women's Clothing` and `Women's Shoes` and customers from New York
tend to buy items from these categories frequently together. In other words, tend to buy items from these categories frequently together. In other words,
customers who buy products labelled `Women's Clothing` more likely buy products customers who buy products labelled `Women's Clothing` more likely buy products
also from the `Women's Shoes` category and customers from New York most likely also from the `Women's Shoes` category and customers from New York most likely
buy products from these categories together. The item set with the second buy products from these categories together. The item set with the second
highest support is `Women's Clothing` and `Women's Accessories` with customers highest support is `Women's Clothing` and `Women's Accessories` with customers
mostly from New York. Finally, the item set with the third highest support is mostly from New York. Finally, the item set with the third highest support is
`Men's Clothing` and `Men's Shoes` with customers mostly from Cairo. `Men's Clothing` and `Men's Shoes` with customers mostly from Cairo.
@ -262,7 +260,7 @@ POST /kibana_sample_data_ecommerce/_async_search
"size": 0, "size": 0,
"aggs": { "aggs": {
"my_agg": { "my_agg": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 3, "minimum_set_size": 3,
"fields": [ "fields": [
{ "field": "category.keyword" }, { "field": "category.keyword" },
@ -282,20 +280,20 @@ POST /kibana_sample_data_ecommerce/_async_search
// TEST[skip:setup kibana sample data] // TEST[skip:setup kibana sample data]
The result will only show item sets that created from documents matching the The result will only show item sets that created from documents matching the
filter, namely purchases in Europe. Using `filter`, the calculated `support` filter, namely purchases in Europe. Using `filter`, the calculated `support`
still takes all purchases into acount. That's different than specifying a query still takes all purchases into acount. That's different than specifying a query
at the top-level, in which case `support` gets calculated only from purchases in at the top-level, in which case `support` gets calculated only from purchases in
Europe. Europe.
[discrete] [discrete]
==== Analyzing numeric values by using a runtime field ==== Analyzing numeric values by using a runtime field
The frequent items aggregation enables you to bucket numeric values by using The frequent items aggregation enables you to bucket numeric values by using
<<runtime,runtime fields>>. The next example demonstrates how to use a script to <<runtime,runtime fields>>. The next example demonstrates how to use a script to
add a runtime field to your documents called `price_range`, which is add a runtime field to your documents called `price_range`, which is
calculated from the taxful total price of the individual transactions. The calculated from the taxful total price of the individual transactions. The
runtime field then can be used in the frequent items aggregation as a field to runtime field then can be used in the frequent items aggregation as a field to
analyze. analyze.
@ -318,7 +316,7 @@ GET kibana_sample_data_ecommerce/_search
"size": 0, "size": 0,
"aggs": { "aggs": {
"my_agg": { "my_agg": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 4, "minimum_set_size": 4,
"fields": [ "fields": [
{ {
@ -402,6 +400,6 @@ The API returns a response similar to the following one:
------------------------------------------------- -------------------------------------------------
// TEST[skip:setup kibana sample data] // TEST[skip:setup kibana sample data]
The response shows the categories that customers purchase from most frequently The response shows the categories that customers purchase from most frequently
together, the location of the customers who tend to buy items from these together, the location of the customers who tend to buy items from these
categories, and the most frequent price ranges of these purchases. categories, and the most frequent price ranges of these purchases.

2
x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/MachineLearning.java
View file

@ -1573,7 +1573,7 @@ public class MachineLearning extends Plugin
).addResultReader(InternalCategorizationAggregation::new) ).addResultReader(InternalCategorizationAggregation::new)
.setAggregatorRegistrar(s -> s.registerUsage(CategorizeTextAggregationBuilder.NAME)), .setAggregatorRegistrar(s -> s.registerUsage(CategorizeTextAggregationBuilder.NAME)),
new AggregationSpec( new AggregationSpec(
FrequentItemSetsAggregationBuilder.NAME, new ParseField(FrequentItemSetsAggregationBuilder.NAME, FrequentItemSetsAggregationBuilder.DEPRECATED_NAME),
FrequentItemSetsAggregationBuilder::new, FrequentItemSetsAggregationBuilder::new,
checkAggLicense(FrequentItemSetsAggregationBuilder.PARSER, FREQUENT_ITEM_SETS_AGG_FEATURE) checkAggLicense(FrequentItemSetsAggregationBuilder.PARSER, FREQUENT_ITEM_SETS_AGG_FEATURE)
).addResultReader(FrequentItemSetsAggregatorFactory.getResultReader()) ).addResultReader(FrequentItemSetsAggregatorFactory.getResultReader())

1
x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/aggs/frequentitemsets/EclatMapReducer.java
View file

@ -120,6 +120,7 @@ public final class EclatMapReducer extends AbstractItemSetMapReducer<
private static final Logger logger = LogManager.getLogger(EclatMapReducer.class); private static final Logger logger = LogManager.getLogger(EclatMapReducer.class);
private static final int VERSION = 1; private static final int VERSION = 1;
// named writable for this implementation
public static final String NAME = "frequent_items-eclat-" + VERSION; public static final String NAME = "frequent_items-eclat-" + VERSION;
// cache for marking transactions visited, memory usage: ((BITSET_CACHE_TRAVERSAL_DEPTH -2) * BITSET_CACHE_NUMBER_OF_TRANSACTIONS) / 8 // cache for marking transactions visited, memory usage: ((BITSET_CACHE_TRAVERSAL_DEPTH -2) * BITSET_CACHE_NUMBER_OF_TRANSACTIONS) / 8

5
x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/aggs/frequentitemsets/FrequentItemSetsAggregationBuilder.java
View file

@ -37,7 +37,10 @@ import static org.elasticsearch.common.Strings.format;
public final class FrequentItemSetsAggregationBuilder extends AbstractAggregationBuilder<FrequentItemSetsAggregationBuilder> { public final class FrequentItemSetsAggregationBuilder extends AbstractAggregationBuilder<FrequentItemSetsAggregationBuilder> {
public static final String NAME = "frequent_items"; public static final String NAME = "frequent_item_sets";
// name used between 8.4 - 8.6, kept for backwards compatibility until 9.0
public static final String DEPRECATED_NAME = "frequent_items";
public static final double DEFAULT_MINIMUM_SUPPORT = 0.01; public static final double DEFAULT_MINIMUM_SUPPORT = 0.01;
public static final int DEFAULT_MINIMUM_SET_SIZE = 1; public static final int DEFAULT_MINIMUM_SET_SIZE = 1;

4
x-pack/plugin/ml/src/test/java/org/elasticsearch/xpack/ml/aggs/frequentitemsets/FrequentItemSetsAggregationBuilderTests.java
View file

@ -184,7 +184,7 @@ public class FrequentItemSetsAggregationBuilderTests extends AbstractXContentSer
randomFrom(EXECUTION_HINT_ALLOWED_MODES) randomFrom(EXECUTION_HINT_ALLOWED_MODES)
).subAggregation(AggregationBuilders.avg("fieldA"))); ).subAggregation(AggregationBuilders.avg("fieldA")));
assertEquals("Aggregator [fi] of type [frequent_items] cannot accept sub-aggregations", e.getMessage()); assertEquals("Aggregator [fi] of type [frequent_item_sets] cannot accept sub-aggregations", e.getMessage());
e = expectThrows( e = expectThrows(
IllegalArgumentException.class, IllegalArgumentException.class,
@ -202,7 +202,7 @@ public class FrequentItemSetsAggregationBuilderTests extends AbstractXContentSer
).subAggregations(new AggregatorFactories.Builder().addAggregator(AggregationBuilders.avg("fieldA"))) ).subAggregations(new AggregatorFactories.Builder().addAggregator(AggregationBuilders.avg("fieldA")))
); );
assertEquals("Aggregator [fi] of type [frequent_items] cannot accept sub-aggregations", e.getMessage()); assertEquals("Aggregator [fi] of type [frequent_item_sets] cannot accept sub-aggregations", e.getMessage());
e = expectThrows( e = expectThrows(
IllegalArgumentException.class, IllegalArgumentException.class,

92
x-pack/plugin/src/yamlRestTest/resources/rest-api-spec/test/ml/frequent_items_agg.yml → x-pack/plugin/src/yamlRestTest/resources/rest-api-spec/test/ml/frequent_item_sets_agg.yml
View file

@ -93,7 +93,7 @@ setup:
--- ---
"Test frequent items array fields": "Test frequent item sets array fields":
- do: - do:
search: search:
@ -103,7 +103,7 @@ setup:
"size": 0, "size": 0,
"aggs": { "aggs": {
"fi": { "fi": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 3, "minimum_set_size": 3,
"minimum_support": 0.3, "minimum_support": 0.3,
"fields": [ "fields": [
@ -123,7 +123,7 @@ setup:
- match: { aggregations.fi.buckets.1.key.error_message: ["engine overheated"] } - match: { aggregations.fi.buckets.1.key.error_message: ["engine overheated"] }
--- ---
"Test frequent items date format": "Test frequent item sets date format":
- do: - do:
search: search:
@ -141,7 +141,7 @@ setup:
"size": 0, "size": 0,
"aggs": { "aggs": {
"fi": { "fi": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 3, "minimum_set_size": 3,
"minimum_support": 0.3, "minimum_support": 0.3,
"fields": [ "fields": [
@ -159,7 +159,7 @@ setup:
--- ---
"Test frequent items date format 2": "Test frequent item sets date format 2":
- do: - do:
search: search:
@ -177,7 +177,7 @@ setup:
"size": 0, "size": 0,
"aggs": { "aggs": {
"fi": { "fi": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 2, "minimum_set_size": 2,
"minimum_support": 0.3, "minimum_support": 0.3,
"fields": [ "fields": [
@ -195,7 +195,7 @@ setup:
- match: { aggregations.fi.buckets.0.key.error_message: ["engine overheated"] } - match: { aggregations.fi.buckets.0.key.error_message: ["engine overheated"] }
--- ---
"Test frequent items array fields profile": "Test frequent item sets array fields profile":
- do: - do:
search: search:
@ -206,7 +206,7 @@ setup:
"size": 0, "size": 0,
"aggs": { "aggs": {
"fi": { "fi": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 3, "minimum_set_size": 3,
"minimum_support": 0.2, "minimum_support": 0.2,
"fields": [ "fields": [
@ -229,7 +229,7 @@ setup:
- match: { aggregations.fi.profile.unique_items_after_prune: 11 } - match: { aggregations.fi.profile.unique_items_after_prune: 11 }
--- ---
"Test frequent items flattened fields": "Test frequent item sets flattened fields":
- do: - do:
search: search:
@ -239,7 +239,7 @@ setup:
"size": 0, "size": 0,
"aggs": { "aggs": {
"fi": { "fi": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 3, "minimum_set_size": 3,
"minimum_support": 0.3, "minimum_support": 0.3,
"fields": [ "fields": [
@ -259,7 +259,7 @@ setup:
- match: { aggregations.fi.buckets.1.key.data\.error_message: ["engine overheated"] } - match: { aggregations.fi.buckets.1.key.data\.error_message: ["engine overheated"] }
--- ---
"Test frequent items as subagg": "Test frequent item sets as subagg":
- do: - do:
search: search:
@ -276,7 +276,7 @@ setup:
}, },
"aggs": { "aggs": {
"fi": { "fi": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 3, "minimum_set_size": 3,
"minimum_support": 0.3, "minimum_support": 0.3,
"fields": [ "fields": [
@ -298,7 +298,7 @@ setup:
- match: { aggregations.filter_error.fi.buckets.0.key.error_message: ["compressor low pressure"] } - match: { aggregations.filter_error.fi.buckets.0.key.error_message: ["compressor low pressure"] }
--- ---
"Test frequent items as multi-bucket subagg": "Test frequent item sets as multi-bucket subagg":
- do: - do:
search: search:
@ -314,7 +314,7 @@ setup:
}, },
"aggs": { "aggs": {
"fi": { "fi": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 3, "minimum_set_size": 3,
"minimum_support": 0.3, "minimum_support": 0.3,
"fields": [ "fields": [
@ -335,7 +335,7 @@ setup:
- match: { aggregations.weekly.buckets.2.fi.buckets.0.doc_count: 1 } - match: { aggregations.weekly.buckets.2.fi.buckets.0.doc_count: 1 }
--- ---
"Test frequent items filter": "Test frequent item sets filter":
- do: - do:
search: search:
@ -345,7 +345,7 @@ setup:
"size": 0, "size": 0,
"aggs": { "aggs": {
"fi": { "fi": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 3, "minimum_set_size": 3,
"minimum_support": 0.3, "minimum_support": 0.3,
"fields": [ "fields": [
@ -369,7 +369,7 @@ setup:
- match: { aggregations.fi.buckets.0.key.error_message: ["compressor low pressure"] } - match: { aggregations.fi.buckets.0.key.error_message: ["compressor low pressure"] }
--- ---
"Test frequent items exclude": "Test frequent item sets exclude":
- do: - do:
search: search:
@ -379,7 +379,7 @@ setup:
"size": 0, "size": 0,
"aggs": { "aggs": {
"fi": { "fi": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 3, "minimum_set_size": 3,
"minimum_support": 0.3, "minimum_support": 0.3,
"fields": [ "fields": [
@ -401,7 +401,7 @@ setup:
- match: { aggregations.fi.buckets.1.support: 0.3 } - match: { aggregations.fi.buckets.1.support: 0.3 }
--- ---
"Test frequent items include": "Test frequent item sets include":
- do: - do:
search: search:
@ -411,7 +411,7 @@ setup:
"size": 0, "size": 0,
"aggs": { "aggs": {
"fi": { "fi": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 3, "minimum_set_size": 3,
"minimum_support": 0.3, "minimum_support": 0.3,
"fields": [ "fields": [
@ -431,9 +431,9 @@ setup:
- match: { aggregations.fi.buckets.0.key.error_message: ["engine overheated"] } - match: { aggregations.fi.buckets.0.key.error_message: ["engine overheated"] }
--- ---
"Test frequent items unsupported types": "Test frequent item sets unsupported types":
- do: - do:
catch: /Field \[geo_point\] of type \[geo_point\] is not supported for aggregation \[frequent_items\]/ catch: /Field \[geo_point\] of type \[geo_point\] is not supported for aggregation \[frequent_item_sets\]/
search: search:
index: store index: store
body: > body: >
@ -441,7 +441,7 @@ setup:
"size": 0, "size": 0,
"aggs": { "aggs": {
"fi": { "fi": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 3, "minimum_set_size": 3,
"minimum_support": 0.3, "minimum_support": 0.3,
"fields": [ "fields": [
@ -454,7 +454,7 @@ setup:
} }
} }
- do: - do:
catch: /Field \[histogram\] of type \[histogram\] is not supported for aggregation \[frequent_items\]/ catch: /Field \[histogram\] of type \[histogram\] is not supported for aggregation \[frequent_item_sets\]/
search: search:
index: store index: store
body: > body: >
@ -462,7 +462,7 @@ setup:
"size": 0, "size": 0,
"aggs": { "aggs": {
"fi": { "fi": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 3, "minimum_set_size": 3,
"minimum_support": 0.3, "minimum_support": 0.3,
"fields": [ "fields": [
@ -476,9 +476,9 @@ setup:
} }
--- ---
"Test frequent items unsupported subaggs": "Test frequent item sets unsupported subaggs":
- do: - do:
catch: /Aggregator \[fi\] of type \[frequent_items\] cannot accept sub-aggregations/ catch: /Aggregator \[fi\] of type \[frequent_item_sets\] cannot accept sub-aggregations/
search: search:
index: store index: store
body: > body: >
@ -486,7 +486,7 @@ setup:
"size": 0, "size": 0,
"aggs": { "aggs": {
"fi": { "fi": {
"frequent_items": { "frequent_item_sets": {
"minimum_set_size": 3, "minimum_set_size": 3,
"minimum_support": 0.3, "minimum_support": 0.3,
"fields": [ "fields": [
@ -504,3 +504,39 @@ setup:
} }
} }
} }
---
"Test deprecated frequent items":
- skip:
features:
- "allowed_warnings"
- do:
allowed_warnings:
- 'Deprecated field [frequent_items] used, expected [frequent_item_sets] instead'
search:
index: store
body: >
{
"size": 0,
"aggs": {
"fi": {
"frequent_items": {
"minimum_set_size": 3,
"minimum_support": 0.3,
"fields": [
{"field": "features"},
{"field": "error_message"}
]
}
}
}
}
- length: { aggregations.fi.buckets: 4 }
- match: { aggregations.fi.buckets.0.doc_count: 5 }
- match: { aggregations.fi.buckets.0.support: 0.5 }
- match: { aggregations.fi.buckets.0.key.error_message: ["compressor low pressure"] }
- match: { aggregations.fi.buckets.1.doc_count: 4 }
- match: { aggregations.fi.buckets.1.support: 0.4 }
- match: { aggregations.fi.buckets.1.key.error_message: ["engine overheated"] }