Refactor SearchHit to have separate document and meta fields.
This is a part of bigger refactoring of issue #24422 to remove
dependency on MapperService to check if a field is metafield.
Relates to PR: #38373
Relates to issue #24422
Co-authored-by: sandmannn <bohdanpukalskyi@gmail.com>
This is a follow up to a previous commit that renamed MetaData to
Metadata in all of the places. In that commit in master, we renamed
META_DATA to METADATA, but lost this on the backport. This commit
addresses that.
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
Backport of #53982
In order to prepare the `AliasOrIndex` abstraction for the introduction of data streams,
the abstraction needs to be made more flexible, because currently it really can be only
an alias or an index.
* Renamed `AliasOrIndex` to `IndexAbstraction`.
* Introduced a `IndexAbstraction.Type` enum to indicate what a `IndexAbstraction` instance is.
* Replaced the `isAlias()` method that returns a boolean with the `getType()` method that returns the new Type enum.
* Moved `getWriteIndex()` up from the `IndexAbstraction.Alias` to the `IndexAbstraction` interface.
* Moved `getAliasName()` up from the `IndexAbstraction.Alias` to the `IndexAbstraction` interface and renamed it to `getName()`.
* Removed unnecessary casting to `IndexAbstraction.Alias` by just checking the `getType()` method.
Relates to #53100
Today the machine learning plugin stashes a copy of the environment in
its constructor, and uses the stashed copy to construct its components
even though it is provided with an environment to create these
components. What is more, the environment it creates in its constructor
is not fully initialized, as it does not have the final copy of the
settings, but the environment passed in while creating components
does. This commit removes that stashed copy of the environment.
The NodesStatsRequest class uses a set of strings for its internal
serialization. This commit updates the class's interface so that we
no longer use hard-coded getters and setters, but rather
methods that add strings directly. For example, the old way of
adding "os" metrics to a request would be to call request.os(true).
The new way of doing this is to call request.addMetric("os").
For the time being, the canonical list of metrics is an enum in
NodesStatsRequest. This will eventually be replaced with something
pluggable.
This commit populates the _stats API response with sensible "empty"
`data_counts` and `memory_usage` objects when the job itself
has not started reporting them.
Backport of #54210
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
As classification now works for multiple classes, randomly
picking training/test data frame rows is not good enough.
This commit introduces a stratified cross validation splitter
that maintains the proportion of the each class in the dataset
in the sample that is used for training the model.
Backport of #54087
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.
Backport of #53918
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
Since a data frame analytics job may have associated docs
in the .ml-stats-* indices, when the job is deleted we
should delete those docs too.
Backport of #53933
While `CustomProcessor` is generic and allows for flexibility, there
are new requirements that make cross validation a concept it's hard
to abstract behind custom processor. In particular, we would like to
add data_counts to the DFA jobs stats. Counting training VS. test
docs would be a useful statistic. We would also want to add a
different cross validation strategy for multiclass classification.
This commit renames custom processors to cross validation splitters
which allows for those enhancements without cryptically doing
things as a side effect of the abstract custom processing.
Backport of #53915
It's simple to deprecate a field used in an ObjectParser just by adding deprecation
markers to the relevant ParseField objects. The warnings themselves don't currently
have any context - they simply say that a deprecated field has been used, but not
where in the input xcontent it appears. This commit adds the parent object parser
name and XContentLocation to these deprecation messages.
Note that the context is automatically stripped from warning messages when they
are asserted on by integration tests and REST tests, because randomization of
xcontent type during these tests means that the XContentLocation is not constant
Adds parsing and indexing of analysis instrumentation stats.
The latest one is also returned from the get-stats API.
Note that we chose to duplicate objects even where they are currently
similar. There are already ideas on how these will diverge in the future
and while the duplication looks ugly at the moment, it is the option
that offers the highest flexibility.
Backport of #53788
* [ML] only retry persistence failures when the failure is intermittent and stop retrying when analytics job is stopping (#53725)
This fixes two issues:
- Results persister would retry actions even if they are not intermittent. An example of an persistent failure is a doc mapping problem.
- Data frame analytics would continue to retry to persist results even after the job is stopped.
closes https://github.com/elastic/elasticsearch/issues/53687
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
the ML portion of the x-pack info API was erroneously counting configuration documents and definition documents. The underlying implementation of our storage separates the two out.
This PR filters the query so that only trained model config documents are counted.
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`.
