This commit allows documents seen within the same time bucket to be out of order.
This is already supported within the native process.
Additionally, when recording the "latest" record timestamp, we were assuming that the latest seen document was truly the "latest". This is not really the case if latency is utilized or if documents come out of order within the same bucket.
A `model_alias` allows trained models to be referred by a user defined moniker.
This not only improves the readability and simplicity of numerous API calls, but it allows for simpler deployment and upgrade procedures for trained models.
Previously, if you referenced a model ID directly within an ingest pipeline, when you have a new model that performs better than an earlier referenced model, you have to update the pipeline itself. If this model was used in numerous pipelines, ALL those pipelines would have to be updated.
When using a `model_alias` in an ingest pipeline, only that `model_alias` needs to be updated. Then, the underlying referenced model will change in place for all ingest pipelines automatically.
An additional benefit is that the model referenced is not changed until it is fully loaded into cache, this way throughput is not hampered by changing models.
The PR adds early_stopping_enabled optional data frame analysis configuration parameter. The enhancement was already described in elastic/ml-cpp#1676 and so I mark it here as non-issue.
At present the Java code makes a decision on whether to
use current model memory or model memory limit to calculate
how much memory a job requires to be assigned.
The plan is to move this decision to the C++ code, which will
report it via a new field in the model size stats. An
additional change will be that once we have made the switch
from using model memory limit to using current model memory
we will never switch back, as this causes large fluctuations
up and down in memory requirement which will be much more
noticeable when autoscaling is in use.
Although the only two options at present are model memory
limit and current model memory, the new enum includes a
third possibility, peak model memory. To switch to this
now would be tricky, as there have been two bugs in the
implementation of peak model memory which render its value
unreliable in 7.x. However, in 8.x it might make sense to
switch to using peak model memory instead of current model
memory and it's much easier from a BWC perspective if the
enum contains all the values from the start.
Relates #63163
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
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.
