This PR adds detail to the explanation of the soft_limit
memory_status in ML job stats. A consequence that was not
mentioned before is that examples are not added to category
definitions.
Relates elastic/ml-cpp#1590
* [ML] renames */inference* apis to */trained_models* (#63097)
This commit renames all `inference` CRUD APIs to `trained_models`.
This aligns with internal terminology, documentation, and use-cases.
This commit adjusts the following APIs so now they not only support an `_all` case, but wildcard patterned Ids as well.
- `GET _ml/calendars/<calendar_id>/events`
- `GET _ml/calendars/<calendar_id>`
- `GET _ml/anomaly_detectors/<job_id>/model_snapshots/<snapshot_id>`
- `DELETE _ml/anomaly_detectors/<job_id>/_forecast/<forecast_id>`
* [ML] Add new include flag to GET inference/<model_id> API for model training metadata (#61922)
Adds new flag include to the get trained models API
The flag initially has two valid values: definition, total_feature_importance.
Consequently, the old include_model_definition flag is now deprecated.
When total_feature_importance is included, the total_feature_importance field is included in the model metadata object.
Including definition is the same as previously setting include_model_definition=true.
* fixing test
* Update x-pack/plugin/core/src/test/java/org/elasticsearch/xpack/core/ml/action/GetTrainedModelsRequestTests.java
Previously the "mappings" field of the response from the
find_file_structure endpoint was not a drop-in for the
mappings format of the create index endpoint - the
"properties" layer was missing. The reason for omitting
it initially was that the assumption was that the
find_file_structure endpoint would only ever return very
simple mappings without any nested objects. However,
this will not be true in the future, as we will improve
mappings detection for complex JSON objects. As a first
step it makes sense to move the returned mappings closer
to the standard format.
This is a small building block towards fixing #55616
* [ML] adding docs + hlrc for data frame analysis feature_processors (#61149)
Adds HLRC and some docs for the new feature_processors field in Data frame analytics.
Co-authored-by: Przemysław Witek <przemyslaw.witek@elastic.co>
Co-authored-by: Lisa Cawley <lcawley@elastic.co>
Changes:
* Moves `Retrieve selected fields` to its own page and adds a title abbreviation.
* Adds existing script and stored fields content to `Retrieve selected fields`
* Adds a xref for `Retrieve selected fields` to `Search your data`
* Adds related redirects and updates existing xrefs
* [ML] add new `custom` field to trained model processors (#59542)
This commit adds the new configurable field `custom`.
`custom` indicates if the preprocessor was submitted by a user or automatically created by the analytics job.
Eventually, this field will be used in calculating feature importance. When `custom` is true, the feature importance for
the processed fields is calculated. When `false` the current behavior is the same (we calculate the importance for the originating field/feature).
This also adds new required methods to the preprocessor interface. If users are to supply their own preprocessors
in the analytics job configuration, we need to know the input and output field names.
