When exporting and cloning ml configurations in a cluster it can be
frustrating to remove all the fields that were generated by
the plugin. Especially as the number of these fields change
from version to version.
This flag, exclude_generated, allows the GET config APIs to return
configurations with these generated fields removed.
APIs supporting this flag:
- GET _ml/anomaly_detection/<job_id>
- GET _ml/datafeeds/<datafeed_id>
- GET _ml/data_frame/analytics/<analytics_id>
The following fields are not returned in the objects:
- any field that is not user settable (e.g. version, create_time)
- any field that is a calculated default value (e.g. datafeed chunking_config)
- any field that is automatically set via another Elastic stack process (e.g. anomaly job custom_settings.created_by)
relates to #63055
This adds the new `for_export` flag to the following APIs:
- GET _ml/anomaly_detection/<job_id>
- GET _ml/datafeeds/<datafeed_id>
- GET _ml/data_frame/analytics/<analytics_id>
The flag is designed for cloning or exporting configuration objects to later be put into the same cluster or a separate cluster.
The following fields are not returned in the objects:
- any field that is not user settable (e.g. version, create_time)
- any field that is a calculated default value (e.g. datafeed chunking_config)
- any field that would effectively require changing to be of use (e.g. datafeed job_id)
- any field that is automatically set via another Elastic stack process (e.g. anomaly job custom_settings.created_by)
closes https://github.com/elastic/elasticsearch/issues/63055
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.
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
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.
This PR adds the initial Java side changes to enable
use of the per-partition categorization functionality
added in elastic/ml-cpp#1293.
There will be a followup change to complete the work,
as there cannot be any end-to-end integration tests
until elastic/ml-cpp#1293 is merged, and also
elastic/ml-cpp#1293 does not implement some of the
more peripheral functionality, like stop_on_warn and
per-partition stats documents.
The changes so far cover REST APIs, results object
formats, HLRC and docs.
This PR implements the following changes to make ML model snapshot
retention more flexible in advance of adding a UI for the feature in
an upcoming release.
- The default for `model_snapshot_retention_days` for new jobs is now
10 instead of 1
- There is a new job setting, `daily_model_snapshot_retention_after_days`,
that defaults to 1 for new jobs and `model_snapshot_retention_days`
for pre-7.8 jobs
- For days that are older than `model_snapshot_retention_days`, all
model snapshots are deleted as before
- For days that are in between `daily_model_snapshot_retention_after_days`
and `model_snapshot_retention_days` all but the first model snapshot
for that day are deleted
- The `retain` setting of model snapshots is still respected to allow
selected model snapshots to be retained indefinitely
Closes#52150
The failed_category_count statistic records the number of times
categorization wanted to create a new category but couldn't
because the job had reached its model_memory_limit.
Relates elastic/ml-cpp#1130
Data frame analytics dynamically determines the classification field type. This field type then dictates the encoded JSON that is written to Elasticsearch.
Inference needs to know about this field type so that it may provide the EXACT SAME predicted values as analytics.
Here is added a new field `prediction_field_type` which indicates the desired type. Options are: `string` (DEFAULT), `number`, `boolean` (where close_to(1.0) == true, false otherwise).
Analytics provides the default `prediction_field_type` when the model is created from the process.
A new field called `inference_config` is now added to the trained model config object. This new field allows for default inference settings from analytics or some external model builder.
The inference processor can still override whatever is set as the default in the trained model config.
It is possible for ML jobs to open lazily if the "allow_lazy_open"
option in the job config is set to true. Such jobs wait in the
"opening" state until a node has sufficient capacity to run them.
This commit fixes the bug that prevented datafeeds for jobs lazily
waiting assignment from being started. The state of such datafeeds
is "starting", and they can be stopped by the stop datafeed API
while in this state with or without force.
Fixes#53763
Adds a new parameter for classification that enables choosing whether to assign labels to
maximise accuracy or to maximise the minimum class recall.
Fixes#52427.
Adds a new `default_field_map` field to trained model config objects.
This allows the model creator to supply field map if it knows that there should be some map for inference to work directly against the training data.
The use case internally is having analytics jobs supply a field mapping for multi-field fields. This allows us to use the model "out of the box" on data where we trained on `foo.keyword` but the `_source` only references `foo`.
Adds reporting of memory usage for data frame analytics jobs.
This commit introduces a new index pattern `.ml-stats-*` whose
first concrete index will be `.ml-stats-000001`. This index serves
to store instrumentation information for those jobs.
