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`.
Currently the AbstractBulkByScrollRequest accepts slice values of 0 via its
`setSlices` method, denoting the "auto" slicing behaviour that is usable by
settting the "slices=auto" parameter on rest requests. When using the High Level
Rest Client, however, we send the 0 value as an integer, which is then rejected
as invalid by `AbstractBulkByScrollRequest#parseSlices`. Instead of making
parsing of the rest request more lenient, this PR opts for changing the
RequestConverter logic in the client to translate 0 values to "auto" on the rest
requests.
Closes#53044
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.
Backport of #52778 and #52958
* [ML][Inference] Add support for multi-value leaves to the tree model (#52531)
This adds support for multi-value leaves. This is a prerequisite for multi-class boosted tree classification.
This adds a new configurable field called `indices_options`. This allows users to create or update the indices_options used when a datafeed reads from an index.
This is necessary for the following use cases:
- Reading from frozen indices
- Allowing certain indices in multiple index patterns to not exist yet
These index options are available on datafeed creation and update. Users may specify them as URL parameters or within the configuration object.
closes https://github.com/elastic/elasticsearch/issues/48056
This change adds the recall@k metric and refactors precision@k to match
the new metric.
Recall@k is an important metric to use for learning to rank (LTR)
use-cases. Candidate generation or first ranking phase ranking functions
are often optimized for high recall, in order to generate as many
relevant candidates in the top-k as possible for a second phase of
ranking. Adding this metric allows tuning that base query for LTR.
See: https://github.com/elastic/elasticsearch/issues/51676
Backports: https://github.com/elastic/elasticsearch/pull/52577
Add query execution and return actual results returned from
Elasticsearch inside the tests
(cherry picked from commit 3e039282bf991af87604a6d4f8eada19d5e33842)
* Smarter copying of the rest specs and tests (#52114)
This PR addresses the unnecessary copying of the rest specs and allows
for better semantics for which specs and tests are copied. By default
the rest specs will get copied if the project applies
`elasticsearch.standalone-rest-test` or `esplugin` and the project
has rest tests or you configure the custom extension `restResources`.
This PR also removes the need for dozens of places where the x-pack
specs were copied by supporting copying of the x-pack rest specs too.
The plugin/task introduced here can also copy the rest tests to the
local project through a similar configuration.
The new plugin/task allows a user to minimize the surface area of
which rest specs are copied. Per project can be configured to include
only a subset of the specs (or tests). Configuring a project to only
copy the specs when actually needed should help with build cache hit
rates since we can better define what is actually in use.
However, project level optimizations for build cache hit rates are
not included with this PR.
Also, with this PR you can no longer use the includePackaged flag on
integTest task.
The following items are included in this PR:
* new plugin: `elasticsearch.rest-resources`
* new tasks: CopyRestApiTask and CopyRestTestsTask - performs the copy
* new extension 'restResources'
```
restResources {
restApi {
includeCore 'foo' , 'bar' //will include the core specs that start with foo and bar
includeXpack 'baz' //will include x-pack specs that start with baz
}
restTests {
includeCore 'foo', 'bar' //will include the core tests that start with foo and bar
includeXpack 'baz' //will include the x-pack tests that start with baz
}
}
```
This changes the tree validation code to ensure no node in the tree has a
feature index that is beyond the bounds of the feature_names array.
Specifically this handles the situation where the C++ emits a tree containing
a single node and an empty feature_names list. This is valid tree used to
centre the data in the ensemble but the validation code would reject this
as feature_names is empty. This meant a broken workflow as you cannot GET
the model and PUT it back
The `top_metrics` agg is kind of like `top_hits` but it only works on
doc values so it *should* be faster.
At this point it is fairly limited in that it only supports a single,
numeric sort and a single, numeric metric. And it only fetches the "very
topest" document worth of metric. We plan to support returning a
configurable number of top metrics, requesting more than one metric and
more than one sort. And, eventually, non-numeric sorts and metrics. The
trick is doing those things fairly efficiently.
Co-Authored by: Zachary Tong <zach@elastic.co>
This adds a builder and parsed results for the `string_stats`
aggregation directly to the high level rest client. Without this the
HLRC can't access the `string_stats` API without the elastic licensed
`analytics` module.
While I'm in there this adds a few of our usual unit tests and
modernizes the parsing.
This change adds support for the following new model_size_stats
fields:
- categorized_doc_count
- total_category_count
- frequent_category_count
- rare_category_count
- dead_category_count
- categorization_status
Backport of #51879
in preparation for feature importance and split information gain, adding `number_samples` field to `TreeNode` definition.
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
The main purpose of this commit is to add a single autoscaling REST
endpoint skeleton, for the purpose of starting to build out the build
and testing infrastructure that will surround it. For example, rather
than commiting a fully-functioning autoscaling API, we introduce here
the skeleton so that we can start wiring up the build and testing
infrastructure, establish security roles/permissions, an so on. This
way, in a forthcoming PR that introduces actual functionality, that PR
will be smaller and have less distractions around that sort of
infrastructure.
SecurityIT.testGetUser creates a user for testing purposes, but did
not delete the user at the end of the test. This could leave the
cluster in an unexpected state for other tests.
This commit:
- Deletes the user at the end of `testGetUser`
- Adds the test-name as metadata to the users that are created in `SecurityIT`
so that their origin is clear if they do interfere with other tests
- Enables SecurityDocumentationIT.testGetUsers on the expectation that
the new cleanup step will resolve the unreliability of that test.
Relates: #48440
Co-authored-by: Tim Vernum <tim@adjective.org>
Currently, the same class `FieldCapabilities` is used both to represent the
capabilities for one index, and also the merged capabilities across indices. To
help clarify the logic, this PR proposes to create a separate class
`IndexFieldCapabilities` for the capabilities in one index. The refactor will
also help when adding `source_path` information in #49264, since the merged
source path field will have a different structure from the field for a single index.
Individual changes:
* Add a new class IndexFieldCapabilities.
* Remove extra constructor from FieldCapabilities.
* Combine the add and merge methods in FieldCapabilities.Builder.
While we use `== false` as a more visible form of boolean negation
(instead of `!`), the true case is implied and the true value does not
need to explicitly checked. This commit converts cases that have slipped
into the code checking for `== true`.
* Rename ILM history index enablement setting
The previous setting was `index.lifecycle.history_index_enabled`, this commit changes it to
`indices.lifecycle.history_index_enabled` to indicate this is not an index-level setting (it's node
level).
* [ML][Inference] Fix weighted mode definition (#51648)
Weighted mode inaccurately assumed that the "max value" of the input values would be the maximum class value. This does not make sense.
Weighted Mode should know how many classes there are. Hence the new parameter `num_classes`. This indicates what the maximum class value to be expected.
The audit index is re-created for every testrun and therefore potential useful debug information
gets lost. This change reads out the audit index and logs the results, which makes them available
for debugging CI issues.
relates #51549
This commit creates a new index privilege named `maintenance`.
The privilege grants the following actions: `refresh`, `flush` (also synced-`flush`),
and `force-merge`. Previously the actions were only under the `manage` privilege
which in some situations was too permissive.
Co-authored-by: Amir H Movahed <arhd83@gmail.com>
* [ML][Inference] add tags url param to GET (#51330)
Adds a new URL parameter, `tags` to the GET _ml/inference/<model_id> endpoint.
This parameter allows the list of models to be further reduced to those who contain all the provided tags.
This change adds a new `kibana_admin` role, and deprecates
the old `kibana_user` and`kibana_dashboard_only_user`roles.
The deprecation is implemented via a new reserved metadata
attribute, which can be consumed from the API and also triggers
deprecation logging when used (by a user authenticating to
Elasticsearch).
Some docs have been updated to avoid references to these
deprecated roles.
Backport of: #46456
Co-authored-by: Larry Gregory <lgregorydev@gmail.com>
* [ML][Inference] Adding classification_weights to ensemble models
classification_weights are a way to allow models to
prefer specific classification results over others
this might be advantageous if classification value
probabilities are a known quantity and can improve
model error rates.
Adds a new parameter to regression and classification that enables computation
of importance for the top most important features. The computation of the importance
is based on SHAP (SHapley Additive exPlanations) method.
Backport of #50914
Aleksandr Maus
Benjamin Trent
Dimitris Athanasiou
Hendrik Muhs
Christoph Büscher
Nik Everett
Costin Leau
Dan Hermann
Josh Devins
Jake Landis
Sachin Frayne
Przemko Robakowski
Igor Motov
David Kyle
David Roberts
Martijn van Groningen
Jason Tedor
Ioannis Kakavas
Julie Tibshirani
James Rodewig
Ryan Ernst
Lee Hinman
Albert Zaharovits
Nhat Nguyen
Tim Vernum