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[ML] rename frequent_items to frequent_item_sets and make it GA (#93421)
rename frequent_items to frequent_item_sets and remove the experimental batch
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Hendrik Muhs
GitHub
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8 changed files with 165 additions and 117 deletions
10
docs/changelog/93421.yaml
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docs/changelog/93421.yaml
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@ -0,0 +1,10 @@
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pr: 93421
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summary: Make `frequent_item_sets` aggregation GA
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area: Machine Learning
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type: feature
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issues: []
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highlight:
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title: Make `frequent_item_sets` aggregation GA
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body: The `frequent_item_sets` aggregation has been moved from
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technical preview to general availability.
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notable: true
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@ -36,7 +36,7 @@ include::bucket/filter-aggregation.asciidoc[]
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include::bucket/filters-aggregation.asciidoc[]
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include::bucket/frequent-items-aggregation.asciidoc[]
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include::bucket/frequent-item-sets-aggregation.asciidoc[]
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include::bucket/geodistance-aggregation.asciidoc[]
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@ -1,40 +1,38 @@
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[[search-aggregations-bucket-frequent-items-aggregation]]
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=== Frequent items aggregation
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[[search-aggregations-bucket-frequent-item-sets-aggregation]]
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=== Frequent item sets aggregation
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++++
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<titleabbrev>Frequent items</titleabbrev>
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<titleabbrev>Frequent item sets</titleabbrev>
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++++
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experimental::[]
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A bucket aggregation which finds frequent item sets. It is a form of association
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rules mining that identifies items that often occur together. Items that are
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frequently purchased together or log events that tend to co-occur are examples
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of frequent item sets. Finding frequent item sets helps to discover
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A bucket aggregation which finds frequent item sets. It is a form of association
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rules mining that identifies items that often occur together. Items that are
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frequently purchased together or log events that tend to co-occur are examples
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of frequent item sets. Finding frequent item sets helps to discover
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relationships between different data points (items).
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The aggregation reports closed item sets. A frequent item set is called closed
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if no superset exists with the same ratio of documents (also known as its
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<<frequent-items-minimum-support,support value>>). For example, we have the two
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The aggregation reports closed item sets. A frequent item set is called closed
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if no superset exists with the same ratio of documents (also known as its
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<<frequent-item-sets-minimum-support,support value>>). For example, we have the two
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following candidates for a frequent item set, which have the same support value:
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1. `apple, orange, banana`
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2. `apple, orange, banana, tomato`.
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Only the second item set (`apple, orange, banana, tomato`) is returned, and the
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first set – which is a subset of the second one – is skipped. Both item sets
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Only the second item set (`apple, orange, banana, tomato`) is returned, and the
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first set – which is a subset of the second one – is skipped. Both item sets
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might be returned if their support values are different.
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The runtime of the aggregation depends on the data and the provided parameters.
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It might take a significant time for the aggregation to complete. For this
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reason, it is recommended to use <<async-search,async search>> to run your
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The runtime of the aggregation depends on the data and the provided parameters.
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It might take a significant time for the aggregation to complete. For this
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reason, it is recommended to use <<async-search,async search>> to run your
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requests asynchronously.
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==== Syntax
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A `frequent_items` aggregation looks like this in isolation:
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A `frequent_item_sets` aggregation looks like this in isolation:
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[source,js]
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--------------------------------------------------
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"frequent_items": {
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"frequent_item_sets": {
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"minimum_set_size": 3,
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"fields": [
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{"field": "my_field_1"},
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@ -44,74 +42,74 @@ A `frequent_items` aggregation looks like this in isolation:
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--------------------------------------------------
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// NOTCONSOLE
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.`frequent_items` Parameters
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.`frequent_item_sets` Parameters
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|===
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|Parameter Name |Description |Required |Default Value
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|`fields` |(array) Fields to analyze. | Required |
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|`minimum_set_size` | (integer) The <<frequent-items-minimum-set-size,minimum size>> of one item set. | Optional | `1`
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|`minimum_support` | (integer) The <<frequent-items-minimum-support,minimum support>> of one item set. | Optional | `0.1`
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|`minimum_set_size` | (integer) The <<frequent-item-sets-minimum-set-size,minimum size>> of one item set. | Optional | `1`
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|`minimum_support` | (integer) The <<frequent-item-sets-minimum-support,minimum support>> of one item set. | Optional | `0.1`
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|`size` | (integer) The number of top item sets to return. | Optional | `10`
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|`filter` | (object) Query that filters documents from the analysis | Optional | `match_all`
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|===
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[discrete]
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[[frequent-items-fields]]
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[[frequent-item-sets-fields]]
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==== Fields
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Supported field types for the analyzed fields are keyword, numeric, ip, date,
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and arrays of these types. You can also add runtime fields to your analyzed
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Supported field types for the analyzed fields are keyword, numeric, ip, date,
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and arrays of these types. You can also add runtime fields to your analyzed
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fields.
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If the combined cardinality of the analyzed fields are high, the aggregation
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If the combined cardinality of the analyzed fields are high, the aggregation
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might require a significant amount of system resources.
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You can filter the values for each field by using the `include` and `exclude`
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parameters. The parameters can be regular expression strings or arrays of
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strings of exact terms. The filtered values are removed from the analysis and
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therefore reduce the runtime. If both `include` and `exclude` are defined,
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`exclude` takes precedence; it means `include` is evaluated first and then
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You can filter the values for each field by using the `include` and `exclude`
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parameters. The parameters can be regular expression strings or arrays of
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strings of exact terms. The filtered values are removed from the analysis and
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therefore reduce the runtime. If both `include` and `exclude` are defined,
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`exclude` takes precedence; it means `include` is evaluated first and then
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`exclude`.
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[discrete]
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[[frequent-items-minimum-set-size]]
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[[frequent-item-sets-minimum-set-size]]
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==== Minimum set size
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The minimum set size is the minimum number of items the set needs to contain. A
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value of 1 returns the frequency of single items. Only item sets that contain at
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least the number of `minimum_set_size` items are returned. For example, the item
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set `orange, banana, apple` is returned only if the minimum set size is 3 or
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The minimum set size is the minimum number of items the set needs to contain. A
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value of 1 returns the frequency of single items. Only item sets that contain at
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least the number of `minimum_set_size` items are returned. For example, the item
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set `orange, banana, apple` is returned only if the minimum set size is 3 or
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lower.
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[discrete]
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[[frequent-items-minimum-support]]
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[[frequent-item-sets-minimum-support]]
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==== Minimum support
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The minimum support value is the ratio of documents that an item set must exist
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in to be considered "frequent". In particular, it is a normalized value between
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0 and 1. It is calculated by dividing the number of documents containing the
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The minimum support value is the ratio of documents that an item set must exist
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in to be considered "frequent". In particular, it is a normalized value between
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0 and 1. It is calculated by dividing the number of documents containing the
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item set by the total number of documents.
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For example, if a given item set is contained by five documents and the total
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number of documents is 20, then the support of the item set is 5/20 = 0.25.
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Therefore, this set is returned only if the minimum support is 0.25 or lower.
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As a higher minimum support prunes more items, the calculation is less resource
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intensive. The `minimum_support` parameter has an effect on the required memory
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For example, if a given item set is contained by five documents and the total
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number of documents is 20, then the support of the item set is 5/20 = 0.25.
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Therefore, this set is returned only if the minimum support is 0.25 or lower.
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As a higher minimum support prunes more items, the calculation is less resource
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intensive. The `minimum_support` parameter has an effect on the required memory
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and the runtime of the aggregation.
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[discrete]
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[[frequent-items-size]]
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[[frequent-item-sets-size]]
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==== Size
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This parameter defines the maximum number of item sets to return. The result
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contains top-k item sets; the item sets with the highest support values. This
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parameter has a significant effect on the required memory and the runtime of the
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This parameter defines the maximum number of item sets to return. The result
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contains top-k item sets; the item sets with the highest support values. This
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parameter has a significant effect on the required memory and the runtime of the
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aggregation.
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[discrete]
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[[frequent-items-filter]]
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[[frequent-item-sets-filter]]
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==== Filter
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A query to filter documents to use as part of the analysis. Documents that
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@ -123,7 +121,7 @@ Use a top-level query to filter the data set.
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[discrete]
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[[frequent-items-example]]
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[[frequent-item-sets-example]]
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==== Examples
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In the following examples, we use the e-commerce {kib} sample data set.
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@ -132,14 +130,14 @@ In the following examples, we use the e-commerce {kib} sample data set.
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[discrete]
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==== Aggregation with two analyzed fields and an `exclude` parameter
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In the first example, the goal is to find out based on transaction data (1.)
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from what product categories the customers purchase products frequently together
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and (2.) from which cities they make those purchases. We want to exclude results
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where location information is not available (where the city name is `other`).
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Finally, we are interested in sets with three or more items, and want to see the
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In the first example, the goal is to find out based on transaction data (1.)
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from what product categories the customers purchase products frequently together
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and (2.) from which cities they make those purchases. We want to exclude results
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where location information is not available (where the city name is `other`).
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Finally, we are interested in sets with three or more items, and want to see the
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first three frequent item sets with the highest support.
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Note that we use the <<async-search,async search>> endpoint in this first
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Note that we use the <<async-search,async search>> endpoint in this first
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example.
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[source,console]
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@ -149,7 +147,7 @@ POST /kibana_sample_data_ecommerce/_async_search
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"size":0,
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"aggs":{
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"my_agg":{
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"frequent_items":{
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"frequent_item_sets":{
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"minimum_set_size":3,
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"fields":[
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{
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@ -168,7 +166,7 @@ POST /kibana_sample_data_ecommerce/_async_search
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-------------------------------------------------
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// TEST[skip:setup kibana sample data]
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The response of the API call above contains an identifier (`id`) of the async
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The response of the API call above contains an identifier (`id`) of the async
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search request. You can use the identifier to retrieve the search results:
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[source,console]
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@ -225,27 +223,27 @@ The API returns a response similar to the following one:
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"support" : 0.026310160427807486
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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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// TEST[skip:setup kibana sample data]
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<1> The array of returned item sets.
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<2> The `key` object contains one item set. In this case, it consists of two
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<2> The `key` object contains one item set. In this case, it consists of two
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values of the `category.keyword` field and one value of the `geoip.city_name`.
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<3> The number of documents that contain the item set.
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<4> The support value of the item set. It is calculated by dividing the number
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of documents containing the item set by the total number of documents.
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<3> The number of documents that contain the item set.
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<4> The support value of the item set. It is calculated by dividing the number
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of documents containing the item set by the total number of documents.
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The response shows that the categories customers purchase from most frequently
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together are `Women's Clothing` and `Women's Shoes` and customers from New York
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tend to buy items from these categories frequently together. In other words,
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customers who buy products labelled `Women's Clothing` more likely buy products
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also from the `Women's Shoes` category and customers from New York most likely
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buy products from these categories together. The item set with the second
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highest support is `Women's Clothing` and `Women's Accessories` with customers
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mostly from New York. Finally, the item set with the third highest support is
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The response shows that the categories customers purchase from most frequently
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together are `Women's Clothing` and `Women's Shoes` and customers from New York
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tend to buy items from these categories frequently together. In other words,
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customers who buy products labelled `Women's Clothing` more likely buy products
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also from the `Women's Shoes` category and customers from New York most likely
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buy products from these categories together. The item set with the second
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highest support is `Women's Clothing` and `Women's Accessories` with customers
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mostly from New York. Finally, the item set with the third highest support is
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`Men's Clothing` and `Men's Shoes` with customers mostly from Cairo.
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@ -262,7 +260,7 @@ POST /kibana_sample_data_ecommerce/_async_search
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"size": 0,
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"aggs": {
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"my_agg": {
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"frequent_items": {
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"frequent_item_sets": {
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"minimum_set_size": 3,
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"fields": [
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{ "field": "category.keyword" },
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@ -282,20 +280,20 @@ POST /kibana_sample_data_ecommerce/_async_search
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// TEST[skip:setup kibana sample data]
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The result will only show item sets that created from documents matching the
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filter, namely purchases in Europe. Using `filter`, the calculated `support`
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still takes all purchases into acount. That's different than specifying a query
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at the top-level, in which case `support` gets calculated only from purchases in
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filter, namely purchases in Europe. Using `filter`, the calculated `support`
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still takes all purchases into acount. That's different than specifying a query
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at the top-level, in which case `support` gets calculated only from purchases in
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Europe.
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[discrete]
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==== Analyzing numeric values by using a runtime field
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The frequent items aggregation enables you to bucket numeric values by using
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<<runtime,runtime fields>>. The next example demonstrates how to use a script to
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add a runtime field to your documents called `price_range`, which is
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calculated from the taxful total price of the individual transactions. The
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runtime field then can be used in the frequent items aggregation as a field to
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The frequent items aggregation enables you to bucket numeric values by using
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<<runtime,runtime fields>>. The next example demonstrates how to use a script to
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add a runtime field to your documents called `price_range`, which is
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calculated from the taxful total price of the individual transactions. The
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runtime field then can be used in the frequent items aggregation as a field to
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analyze.
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@ -318,7 +316,7 @@ GET kibana_sample_data_ecommerce/_search
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"size": 0,
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"aggs": {
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"my_agg": {
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"frequent_items": {
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"frequent_item_sets": {
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"minimum_set_size": 4,
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"fields": [
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{
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@ -402,6 +400,6 @@ The API returns a response similar to the following one:
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-------------------------------------------------
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// TEST[skip:setup kibana sample data]
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The response shows the categories that customers purchase from most frequently
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together, the location of the customers who tend to buy items from these
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The response shows the categories that customers purchase from most frequently
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together, the location of the customers who tend to buy items from these
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categories, and the most frequent price ranges of these purchases.
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@ -1573,7 +1573,7 @@ public class MachineLearning extends Plugin
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).addResultReader(InternalCategorizationAggregation::new)
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.setAggregatorRegistrar(s -> s.registerUsage(CategorizeTextAggregationBuilder.NAME)),
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new AggregationSpec(
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FrequentItemSetsAggregationBuilder.NAME,
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new ParseField(FrequentItemSetsAggregationBuilder.NAME, FrequentItemSetsAggregationBuilder.DEPRECATED_NAME),
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FrequentItemSetsAggregationBuilder::new,
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checkAggLicense(FrequentItemSetsAggregationBuilder.PARSER, FREQUENT_ITEM_SETS_AGG_FEATURE)
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).addResultReader(FrequentItemSetsAggregatorFactory.getResultReader())
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@ -120,6 +120,7 @@ public final class EclatMapReducer extends AbstractItemSetMapReducer<
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private static final Logger logger = LogManager.getLogger(EclatMapReducer.class);
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private static final int VERSION = 1;
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// named writable for this implementation
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public static final String NAME = "frequent_items-eclat-" + VERSION;
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// cache for marking transactions visited, memory usage: ((BITSET_CACHE_TRAVERSAL_DEPTH -2) * BITSET_CACHE_NUMBER_OF_TRANSACTIONS) / 8
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@ -37,7 +37,10 @@ import static org.elasticsearch.common.Strings.format;
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public final class FrequentItemSetsAggregationBuilder extends AbstractAggregationBuilder<FrequentItemSetsAggregationBuilder> {
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public static final String NAME = "frequent_items";
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public static final String NAME = "frequent_item_sets";
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// name used between 8.4 - 8.6, kept for backwards compatibility until 9.0
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public static final String DEPRECATED_NAME = "frequent_items";
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public static final double DEFAULT_MINIMUM_SUPPORT = 0.01;
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public static final int DEFAULT_MINIMUM_SET_SIZE = 1;
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|
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@ -184,7 +184,7 @@ public class FrequentItemSetsAggregationBuilderTests extends AbstractXContentSer
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randomFrom(EXECUTION_HINT_ALLOWED_MODES)
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).subAggregation(AggregationBuilders.avg("fieldA")));
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assertEquals("Aggregator [fi] of type [frequent_items] cannot accept sub-aggregations", e.getMessage());
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assertEquals("Aggregator [fi] of type [frequent_item_sets] cannot accept sub-aggregations", e.getMessage());
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e = expectThrows(
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IllegalArgumentException.class,
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@ -202,7 +202,7 @@ public class FrequentItemSetsAggregationBuilderTests extends AbstractXContentSer
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).subAggregations(new AggregatorFactories.Builder().addAggregator(AggregationBuilders.avg("fieldA")))
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);
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assertEquals("Aggregator [fi] of type [frequent_items] cannot accept sub-aggregations", e.getMessage());
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assertEquals("Aggregator [fi] of type [frequent_item_sets] cannot accept sub-aggregations", e.getMessage());
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e = expectThrows(
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IllegalArgumentException.class,
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|
|
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|
|
@ -93,7 +93,7 @@ setup:
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|
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---
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"Test frequent items array fields":
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"Test frequent item sets array fields":
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- do:
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search:
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|
|
@ -103,7 +103,7 @@ setup:
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"size": 0,
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"aggs": {
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"fi": {
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"frequent_items": {
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"frequent_item_sets": {
|
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"minimum_set_size": 3,
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"minimum_support": 0.3,
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"fields": [
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|
|
@ -123,7 +123,7 @@ setup:
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- match: { aggregations.fi.buckets.1.key.error_message: ["engine overheated"] }
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|
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---
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"Test frequent items date format":
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"Test frequent item sets date format":
|
||||
|
||||
- do:
|
||||
search:
|
||||
|
|
@ -141,7 +141,7 @@ setup:
|
|||
"size": 0,
|
||||
"aggs": {
|
||||
"fi": {
|
||||
"frequent_items": {
|
||||
"frequent_item_sets": {
|
||||
"minimum_set_size": 3,
|
||||
"minimum_support": 0.3,
|
||||
"fields": [
|
||||
|
|
@ -159,7 +159,7 @@ setup:
|
|||
|
||||
|
||||
---
|
||||
"Test frequent items date format 2":
|
||||
"Test frequent item sets date format 2":
|
||||
|
||||
- do:
|
||||
search:
|
||||
|
|
@ -177,7 +177,7 @@ setup:
|
|||
"size": 0,
|
||||
"aggs": {
|
||||
"fi": {
|
||||
"frequent_items": {
|
||||
"frequent_item_sets": {
|
||||
"minimum_set_size": 2,
|
||||
"minimum_support": 0.3,
|
||||
"fields": [
|
||||
|
|
@ -195,7 +195,7 @@ setup:
|
|||
- match: { aggregations.fi.buckets.0.key.error_message: ["engine overheated"] }
|
||||
|
||||
---
|
||||
"Test frequent items array fields profile":
|
||||
"Test frequent item sets array fields profile":
|
||||
|
||||
- do:
|
||||
search:
|
||||
|
|
@ -206,7 +206,7 @@ setup:
|
|||
"size": 0,
|
||||
"aggs": {
|
||||
"fi": {
|
||||
"frequent_items": {
|
||||
"frequent_item_sets": {
|
||||
"minimum_set_size": 3,
|
||||
"minimum_support": 0.2,
|
||||
"fields": [
|
||||
|
|
@ -229,7 +229,7 @@ setup:
|
|||
- match: { aggregations.fi.profile.unique_items_after_prune: 11 }
|
||||
|
||||
---
|
||||
"Test frequent items flattened fields":
|
||||
"Test frequent item sets flattened fields":
|
||||
|
||||
- do:
|
||||
search:
|
||||
|
|
@ -239,7 +239,7 @@ setup:
|
|||
"size": 0,
|
||||
"aggs": {
|
||||
"fi": {
|
||||
"frequent_items": {
|
||||
"frequent_item_sets": {
|
||||
"minimum_set_size": 3,
|
||||
"minimum_support": 0.3,
|
||||
"fields": [
|
||||
|
|
@ -259,7 +259,7 @@ setup:
|
|||
- match: { aggregations.fi.buckets.1.key.data\.error_message: ["engine overheated"] }
|
||||
|
||||
---
|
||||
"Test frequent items as subagg":
|
||||
"Test frequent item sets as subagg":
|
||||
|
||||
- do:
|
||||
search:
|
||||
|
|
@ -276,7 +276,7 @@ setup:
|
|||
},
|
||||
"aggs": {
|
||||
"fi": {
|
||||
"frequent_items": {
|
||||
"frequent_item_sets": {
|
||||
"minimum_set_size": 3,
|
||||
"minimum_support": 0.3,
|
||||
"fields": [
|
||||
|
|
@ -298,7 +298,7 @@ setup:
|
|||
- 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:
|
||||
search:
|
||||
|
|
@ -314,7 +314,7 @@ setup:
|
|||
},
|
||||
"aggs": {
|
||||
"fi": {
|
||||
"frequent_items": {
|
||||
"frequent_item_sets": {
|
||||
"minimum_set_size": 3,
|
||||
"minimum_support": 0.3,
|
||||
"fields": [
|
||||
|
|
@ -335,7 +335,7 @@ setup:
|
|||
- match: { aggregations.weekly.buckets.2.fi.buckets.0.doc_count: 1 }
|
||||
|
||||
---
|
||||
"Test frequent items filter":
|
||||
"Test frequent item sets filter":
|
||||
|
||||
- do:
|
||||
search:
|
||||
|
|
@ -345,7 +345,7 @@ setup:
|
|||
"size": 0,
|
||||
"aggs": {
|
||||
"fi": {
|
||||
"frequent_items": {
|
||||
"frequent_item_sets": {
|
||||
"minimum_set_size": 3,
|
||||
"minimum_support": 0.3,
|
||||
"fields": [
|
||||
|
|
@ -369,7 +369,7 @@ setup:
|
|||
- match: { aggregations.fi.buckets.0.key.error_message: ["compressor low pressure"] }
|
||||
|
||||
---
|
||||
"Test frequent items exclude":
|
||||
"Test frequent item sets exclude":
|
||||
|
||||
- do:
|
||||
search:
|
||||
|
|
@ -379,7 +379,7 @@ setup:
|
|||
"size": 0,
|
||||
"aggs": {
|
||||
"fi": {
|
||||
"frequent_items": {
|
||||
"frequent_item_sets": {
|
||||
"minimum_set_size": 3,
|
||||
"minimum_support": 0.3,
|
||||
"fields": [
|
||||
|
|
@ -401,7 +401,7 @@ setup:
|
|||
- match: { aggregations.fi.buckets.1.support: 0.3 }
|
||||
|
||||
---
|
||||
"Test frequent items include":
|
||||
"Test frequent item sets include":
|
||||
|
||||
- do:
|
||||
search:
|
||||
|
|
@ -411,7 +411,7 @@ setup:
|
|||
"size": 0,
|
||||
"aggs": {
|
||||
"fi": {
|
||||
"frequent_items": {
|
||||
"frequent_item_sets": {
|
||||
"minimum_set_size": 3,
|
||||
"minimum_support": 0.3,
|
||||
"fields": [
|
||||
|
|
@ -431,9 +431,9 @@ setup:
|
|||
- match: { aggregations.fi.buckets.0.key.error_message: ["engine overheated"] }
|
||||
|
||||
---
|
||||
"Test frequent items unsupported types":
|
||||
"Test frequent item sets unsupported types":
|
||||
- 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:
|
||||
index: store
|
||||
body: >
|
||||
|
|
@ -441,7 +441,7 @@ setup:
|
|||
"size": 0,
|
||||
"aggs": {
|
||||
"fi": {
|
||||
"frequent_items": {
|
||||
"frequent_item_sets": {
|
||||
"minimum_set_size": 3,
|
||||
"minimum_support": 0.3,
|
||||
"fields": [
|
||||
|
|
@ -454,7 +454,7 @@ setup:
|
|||
}
|
||||
}
|
||||
- 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:
|
||||
index: store
|
||||
body: >
|
||||
|
|
@ -462,7 +462,7 @@ setup:
|
|||
"size": 0,
|
||||
"aggs": {
|
||||
"fi": {
|
||||
"frequent_items": {
|
||||
"frequent_item_sets": {
|
||||
"minimum_set_size": 3,
|
||||
"minimum_support": 0.3,
|
||||
"fields": [
|
||||
|
|
@ -476,9 +476,9 @@ setup:
|
|||
}
|
||||
|
||||
---
|
||||
"Test frequent items unsupported subaggs":
|
||||
"Test frequent item sets unsupported subaggs":
|
||||
- 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:
|
||||
index: store
|
||||
body: >
|
||||
|
|
@ -486,7 +486,7 @@ setup:
|
|||
"size": 0,
|
||||
"aggs": {
|
||||
"fi": {
|
||||
"frequent_items": {
|
||||
"frequent_item_sets": {
|
||||
"minimum_set_size": 3,
|
||||
"minimum_support": 0.3,
|
||||
"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"] }
|
||||
Loading…
Add table
Add a link
Reference in a new issue