We were previously checking at least one supported field existed
when the _explain API was called. However, in the case of analyses
with required fields (e.g. regression) we were not accounting that
the dependent variable is not a feature and thus if the source index
only contains the dependent variable field there are no features to
train a model on.
This commit adds a validation that at least one feature is available
for analysis. Note that we also move that validation away from
`ExtractedFieldsDetector` and the _explain API and straight into
the _start API. The reason for doing this is to allow the user to use
the _explain API in order to understand why they would be seeing an
error like this one.
For example, the user might be using an index that has fields but
they are of unsupported types. If they start the job and get
an error that there are no features, they will wonder why that is.
Calling the _explain API will show them that all their fields are
unsupported. If the _explain API was failing instead, there would
be no way for the user to understand why all those fields are
ignored.
Closes#55593
Backport of #55876
Also unmutes the integ test that stops and restarts
an outlier detection job with the hope of learning more
of the failure in #55068.
Backport of #55545
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
Adds a "node" field to the response from the following endpoints:
1. Open anomaly detection job
2. Start datafeed
3. Start data frame analytics job
If the job or datafeed is assigned to a node immediately then
this field will return the ID of that node.
In the case where a job or datafeed is opened or started lazily
the node field will contain an empty string. Clients that want
to test whether a job or datafeed was opened or started lazily
can therefore check for this.
Backport of #55473
`updateAndGet` could actually call the internal method more than once on contention.
If I read the JavaDocs, it says:
```* @param updateFunction a side-effect-free function```
So, it could be getting multiple updates on contention, thus having a race condition where stats are double counted.
To fix, I am going to use a `ReadWriteLock`. The `LongAdder` objects allows fast thread safe writes in high contention environments. These can be protected by the `ReadWriteLock::readLock`.
When stats are persisted, I need to call reset on all these adders. This is NOT thread safe if additions are taking place concurrently. So, I am going to protect with `ReadWriteLock::writeLock`.
This should prevent race conditions while allowing high (ish) throughput in the highly contention paths in inference.
I did some simple throughput tests and this change is not significantly slower and is simpler to grok (IMO).
closes https://github.com/elastic/elasticsearch/issues/54786
* [ML] fix native ML test log spam (#55459)
This adds a dependency to ingest common. This removes the log spam resulting from basic plugins being enabled that require the common ingest processors.
* removing unnecessary changes
* removing unused imports
* removing unnecessary java setting
Removing the deprecated "xpack.monitoring.enabled" setting introduced
log spam and potentially some failures in ML tests. It's possible to use
a different, non-deprecated setting to disable monitoring, so we do that
here.
We believe there's no longer a need to be able to disable basic-license
features completely using the "xpack.*.enabled" settings. If users don't
want to use those features, they simply don't need to use them. Having
such features always available lets us build more complex features that
assume basic-license features are present.
This commit deprecates settings of the form "xpack.*.enabled" for
basic-license features, excluding "security", which is a special case.
It also removes deprecated settings from integration tests and unit
tests where they're not directly relevant; e.g. monitoring and ILM are
no longer disabled in many integration tests.
This fixes the long muted testHRDSplit. Some minor adjustments for modern day elasticsearch changes :).
The cause of the failure is that a new `by` field entering the model with an exceptionally high count does not cause an anomaly. We have since stopped combining the `rare` and `by` in this manner. New entries in a `by` field are not anomalous because we have no history on them yet.
closes https://github.com/elastic/elasticsearch/issues/32966
When a datafeed transitions from lookback to real-time we request
that state is persisted from the autodetect process in the
background.
This PR adds a test to prove that for a categorization job the
state that is persisted includes the categorization state.
Without the fix from elastic/ml-cpp#1137 this test fails. After
that C++ fix is merged this test should pass.
Backport of #55243
I've noticed that a lot of our tests are using deprecated static methods
from the Hamcrest matchers. While this is not a big deal in any
objective sense, it seems like a small good thing to reduce compilation
warnings and be ready for a new release of the matcher library if we
need to upgrade. I've also switched a few other methods in tests that
have drop-in replacements.
We needlessly send documents to be persisted. If there are no stats added, then we should not attempt to persist them.
Also, this PR fixes the race condition that caused issue: https://github.com/elastic/elasticsearch/issues/54786
* [ML] Start gathering and storing inference stats (#53429)
This PR enables stats on inference to be gathered and stored in the `.ml-stats-*` indices.
Each node + model_id will have its own running stats document and these will later be summed together when returning _stats to the user.
`.ml-stats-*` is ILM managed (when possible). So, at any point the underlying index could change. This means that a stats document that is read in and then later updated will actually be a new doc in a new index. This complicates matters as this means that having a running knowledge of seq_no and primary_term is complicated and almost impossible. This is because we don't know the latest index name.
We should also strive for throughput, as this code sits in the middle of an ingest pipeline (or even a query).
Guava was removed from Elasticsearch many years ago, but remnants of it
remain due to transitive dependencies. When a dependency pulls guava
into the compile classpath, devs can inadvertently begin using methods
from guava without realizing it. This commit moves guava to a runtime
dependency in the modules that it is needed.
Note that one special case is the html sanitizer in watcher. The third
party dep uses guava in the PolicyFactory class signature. However, only
calling a method on the PolicyFactory actually causes the class to be
loaded, a reference alone does not trigger compilation to look at the
class implementation. There we utilize a MethodHandle for invoking the
relevant method at runtime, where guava will continue to exist.
The test results are affected by the off-by-one error that is
fixed by https://github.com/elastic/ml-cpp/pull/1122
This test can be unmuted once that fix is merged and has been
built into ml-cpp snapshots.
This adds training_percent parameter to the analytics process for Classification and Regression. This parameter is then used to give more accurate memory estimations.
See native side pr: elastic/ml-cpp#1111
* [ML] add new inference_config field to trained model config (#54421)
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.
* fixing for backport
* [ML] prefer secondary authorization header for data[feed|frame] authz (#54121)
Secondary authorization headers are to be used to facilitate Kibana spaces support + ML jobs/datafeeds.
Now on PUT/Update/Preview datafeed, and PUT data frame analytics the secondary authorization is preferred over the primary (if provided).
closes https://github.com/elastic/elasticsearch/issues/53801
* fixing for backport
* [ML] add num_matches and preferred_to_categories to category defintion objects (#54214)
This adds two new fields to category definitions.
- `num_matches` indicating how many documents have been seen by this category
- `preferred_to_categories` indicating which other categories this particular category supersedes when messages are categorized.
These fields are only guaranteed to be up to date after a `_flush` or `_close`
native change: https://github.com/elastic/ml-cpp/pull/1062
* adjusting for backport
This is a simple naming change PR, to fix the fact that "metadata" is a
single English word, and for too long we have not followed general
naming conventions for it. We are also not consistent about it, for
example, METADATA instead of META_DATA if we were trying to be
consistent with MetaData (although METADATA is correct when considered
in the context of "metadata"). This was a simple find and replace across
the code base, only taking a few minutes to fix this naming issue
forever.
This PR:
1. Fixes the bug where a cardinality estimate of zero could cause
a 500 status
2. Adds tests for that scenario and a few others
3. Adds sensible estimates for the cases that were previously TODO
Backport of #54462
When get filters is called without setting the `size`
paramter only up to 10 filters are returned. However,
100 filters should be returned. This commit fixes this
and adds an integ test to guard it.
It seems this was accidentally broken in #39976.
Closes#54206
Backport of #54207
This commit instruments data frame analytics
with stats for the data that are being analyzed.
In particular, we count training docs, test docs,
and skipped docs.
In order to account docs with missing values as skipped
docs for analyses that do not support missing values,
this commit changes the extractor so that it only ignores
docs with missing values when it collects the data summary,
which is used to estimate memory usage.
Backport of #53998
Adds multi-class feature importance calculation.
Feature importance objects are now mapped as follows
(logistic) Regression:
```
{
"feature_name": "feature_0",
"importance": -1.3
}
```
Multi-class [class names are `foo`, `bar`, `baz`]
```
{
“feature_name”: “feature_0”,
“importance”: 2.0, // sum(abs()) of class importances
“foo”: 1.0,
“bar”: 0.5,
“baz”: -0.5
},
```
For users to get the full benefit of aggregating and searching for feature importance, they should update their index mapping as follows (before turning this option on in their pipelines)
```
"ml.inference.feature_importance": {
"type": "nested",
"dynamic": true,
"properties": {
"feature_name": {
"type": "keyword"
},
"importance": {
"type": "double"
}
}
}
```
The mapping field name is as follows
`ml.<inference.target_field>.<inference.tag>.feature_importance`
if `inference.tag` is not provided in the processor definition, it is not part of the field path.
`inference.target_field` is defaulted to `ml.inference`.
//cc @lcawl ^ Where should we document this?
If this makes it in for 7.7, there shouldn't be any feature_importance at inference BWC worries as 7.7 is the first version to have it.
Feature importance storage format is changing to encompass multi-class.
Feature importance objects are now mapped as follows
(logistic) Regression:
```
{
"feature_name": "feature_0",
"importance": -1.3
}
```
Multi-class [class names are `foo`, `bar`, `baz`]
```
{
“feature_name”: “feature_0”,
“importance”: 2.0, // sum(abs()) of class importances
“foo”: 1.0,
“bar”: 0.5,
“baz”: -0.5
},
```
This change adjusts the mapping creation for analytics so that the field is mapped as a `nested` type.
Native side change: https://github.com/elastic/ml-cpp/pull/1071
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 PR moves the majority of the Watcher REST tests under
the Watcher x-pack plugin.
Specifically, moves the Watcher tests from:
x-pack/plugin/test
x-pack/qa/smoke-test-watcher
x-pack/qa/smoke-test-watcher-with-security
x-pack/qa/smoke-test-monitoring-with-watcher
to:
x-pack/plugin/watcher/qa/rest (/test and /qa/smoke-test-watcher)
x-pack/plugin/watcher/qa/with-security
x-pack/plugin/watcher/qa/with-monitoring
Additionally, this disables Watcher from the main
x-pack test cluster and consolidates the stop/start logic
for the tests listed.
No changes to the tests (beyond moving them) are included.
3rd party tests and doc tests (which also touch Watcher)
are not included in the changes here.
* 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 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>
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
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
Allows ML datafeeds to work with time fields that have
the "date_nanos" type _and make use of the extra precision_.
(Previously datafeeds only worked with time fields that were
exact multiples of milliseconds. So datafeeds would work
with "date_nanos" only if the extra precision over "date" was
not used.)
Relates #49889
* [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 commit removes validation logic of source and dest indices
for data frame analytics and replaces it with using the common
`SourceDestValidator` class which is already used by transforms.
This way the validations and their messages become consistent
while we reduce code.
This means that where these validations fail the error messages
will be slightly different for data frame analytics.
Backport of #50841
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).
Eclipse 4.13 shows a type mismatch error in the affected line because it cannot
correctly infer the boolean return type for the method call. Assigning return
value to a local variable resolves this problem.
Dimitris Athanasiou
Przemysław Witek
David Roberts
Stuart Tettemer
Benjamin Trent
William Brafford
Mark Vieira
Ryan Ernst
Jason Tedor
Jake Landis
Tom Veasey
Mayya Sharipova
James Baiera
Yang Wang
David Kyle
Jack Conradson
Christoph Büscher