Prepares classification analysis to support more than just
two classes. It introduces a new parameter to the process config
which dictates the `num_classes` to the process. It also
changes the max classes limit to `30` provisionally.
Backport of #53539
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`.
A previous change (#53029) is causing analysis jobs to wait for certain indices to be made available. While this it is good for jobs to wait, they could fail early on _start.
This change will cause the persistent task to continually retry node assignment when the failure is due to shards not being available.
If the shards are not available by the time `timeout` is reached by the predicate, it is treated as a _start failure and the task is canceled.
For tasks seeking a new assignment after a node failure, that behavior is unchanged.
closes#53188
Tests have been periodically failing due to a race condition on checking a recently `STOPPED` task's state. The `.ml-state` index is not created until the task has already been transitioned to `STARTED`. This allows the `_start` API call to return. But, if a user (or test) immediately attempts to `_stop` that job, the job could stop and the task removed BEFORE the `.ml-state|stats` indices are created/updated.
This change moves towards the task cleaning up itself in its main execution thread. `stop` flips the flag of the task to `isStopping` and now we check `isStopping` at every necessary method. Allowing the task to gracefully stop.
closes#53007
Currently _rollup_search requires manage privilege to access. It should really be
a read only operation. This PR changes the requirement to be read indices privilege.
Resolves: #50245
* [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 adds machine learning model feature importance calculations to the inference processor.
The new flag in the configuration matches the analytics parameter name: `num_top_feature_importance_values`
Example:
```
"inference": {
"field_mappings": {},
"model_id": "my_model",
"inference_config": {
"regression": {
"num_top_feature_importance_values": 3
}
}
}
```
This will write to the document as follows:
```
"inference" : {
"feature_importance" : {
"FlightTimeMin" : -76.90955548511226,
"FlightDelayType" : 114.13514762158526,
"DistanceMiles" : 13.731580450792187
},
"predicted_value" : 108.33165831875137,
"model_id" : "my_model"
}
```
This is done through calculating the [SHAP values](https://arxiv.org/abs/1802.03888).
It requires that models have populated `number_samples` for each tree node. This is not available to models that were created before 7.7.
Additionally, if the inference config is requesting feature_importance, and not all nodes have been upgraded yet, it will not allow the pipeline to be created. This is to safe-guard in a mixed-version environment where only some ingest nodes have been upgraded.
NOTE: the algorithm is a Java port of the one laid out in ml-cpp: https://github.com/elastic/ml-cpp/blob/master/lib/maths/CTreeShapFeatureImportance.cc
usability blocked by: https://github.com/elastic/ml-cpp/pull/991
When `PUT` is called to store a trained model, it is useful to return the newly create model config. But, it is NOT useful to return the inflated definition.
These definitions can be large and returning the inflated definition causes undo work on the server and client side.
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
This adds `_all` to Calendar searches. This enables users to supply the `_all` string in the `job_ids` array when creating a Calendar. That calendar will now be applied to all jobs (existing and newly created).
Closes#45013
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
ML mappings and index templates have so far been created
programmatically. While this had its merits due to static typing,
there is consensus it would be clear to maintain those in json files.
In addition, we are going to adding ILM policies to these indices
and the component for a plugin to register ILM policies is
`IndexTemplateRegistry`. It expects the templates to be in resource
json files.
For the above reasons this commit refactors ML mappings and index
templates into json resource files that are registered via
`MlIndexTemplateRegistry`.
Backport of #51765
Changes the misleading error message when attempting to open
a job while the "cluster.persistent_tasks.allocation.enable"
setting is set to "none" to a clearer message that names the
setting.
Closes#51956
If the configs are removed (by some horrific means), we should still allow tasks to be cleaned up easily.
Datafeeds and jobs with missing configs are now visible in their respective _stats calls and can be stopped/closed.
* [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.
Data frame analytics classification currently only supports 2 classes for the
dependent variable. We were checking that the field's cardinality is not higher
than 2 but we should also check it is not less than that as otherwise the process
fails.
Backport of #51232
* [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
* [ML][Inference] PUT API (#50852)
This adds the `PUT` API for creating trained models that support our format.
This includes
* HLRC change for the API
* API creation
* Validations of model format and call
* fixing backport
This adds the necessary named XContent classes to the HLRC for the lang ident model. This is so the HLRC can call `GET _ml/inference/lang_ident_model_1?include_definition=true` without XContent parsing errors.
The constructors are package private as since this classes are used exclusively within the pre-packaged model (and require the specific weights, etc. to be of any use).
