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[ML] adding new KS test pipeline aggregation #73334

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4 changes: 4 additions & 0 deletions docs/reference/aggregations/pipeline.asciidoc
Original file line number Diff line number Diff line change
Expand Up @@ -271,6 +271,10 @@ include::pipeline/avg-bucket-aggregation.asciidoc[]

include::pipeline/bucket-script-aggregation.asciidoc[]

include::pipeline/bucket-count-ks-test-aggregation.asciidoc[]

include::pipeline/bucket-correlation-aggregation.asciidoc[]

include::pipeline/bucket-selector-aggregation.asciidoc[]

include::pipeline/bucket-sort-aggregation.asciidoc[]
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Original file line number Diff line number Diff line change
Expand Up @@ -103,13 +103,13 @@ POST correlate_latency/_search?size=0&filter_path=aggregations
{
"aggs": {
"buckets": {
"terms": {
"terms": { <1>
"field": "version",
"size": 2
},
"aggs": {
"latency_ranges": {
"range": {
"range": { <2>
"field": "latency",
"ranges": [
{ "to": 0.0 },
Expand All @@ -126,7 +126,7 @@ POST correlate_latency/_search?size=0&filter_path=aggregations
]
}
},
"bucket_correlation": {
"bucket_correlation": { <3>
"bucket_correlation": {
"buckets_path": "latency_ranges>_count",
"function": {
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Original file line number Diff line number Diff line change
@@ -0,0 +1,291 @@
[role="xpack"]
[testenv="basic"]
[[search-aggregations-bucket-count-ks-test-aggregation]]
=== Bucket count K-S test correlation aggregation
++++
<titleabbrev>Bucket count K-S test aggregation</titleabbrev>
++++

experimental::[]

A sibling pipeline aggregation which executes a two sample Kolmogorov–Smirnov test
(referred to as a "K-S test" from now own) against a provided distribution and
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(referred to as a "K-S test" from now own) against a provided distribution and
(referred to as a "K-S test" from now on) against a provided distribution and

the distribution of documents counts in the configured sibling aggregation.

This test is useful to determine if two samples (represented by `fractions` and `buckets_path`) are
drawn from the same distribution.

[[bucket-count-ks-test-agg-syntax]]
==== Parameters

`buckets_path`::
(Required, string)
Path to the buckets that contain one set of values to correlate. Must be a `_count` path
For syntax, see <<buckets-path-syntax>>.

`alternative`::
(Required, list)
A list of string values indicating which K-S test alternative to calculate.
The valid values are: "greater", "less", "two_sided". This parameter is key for
determining the K-S statistic used when calculating the K-S test.

`fractions`::
(Optional, list)
A list of doubles indicating the distribution of the samples with which to compare to the
`buckets_path` results. The default is a uniform distribution of the same length as the
`buckets_path` buckets.
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We have rather specific requirements on what fractions mean. To produce a meaningful result from this aggregation they should be related to some metric distribution which is then used to create the sibling aggregation. A natural choice is to use equal percentile range queries to construct the sibling aggregation in which case the default is correct. I think it is worth capturing something along these lines.

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I'd propose something like:

A list of doubles indicating the distribution of the samples with which to compare to the
`buckets_path` results. In typical usage this is the overall proportion of documents in
each bucket, which is compared with the actual document proportions in each bucket
from the sibling aggregation counts. The default is to assume that overall documents
are uniformly distributed on these buckets, which they would be if one used equal
percentiles of a metric to define the bucket end points.


`sampling_method`::
(Optional, string)
Indicates the sampling methodology when calculating the K-S test. Note, this is sampling
of the returned values. This determines the cumulative distribution function (cdf) points
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of the returned values. This determines the cumulative distribution function (cdf) points
of the returned values. This determines the cumulative distribution function (CDF) points

Capitalised CDF is used in the next sentence

used comparing the two samples. Default is `upper_tail`, which emphasizes the upper
end of the CDF points. Valid options are: `upper_tail`, `uniform`, and `lower_tail`.

==== Syntax

A `bucket_count_ks_test` aggregation looks like this in isolation:

[source,js]
--------------------------------------------------
{
"bucket_count_ks_test": {
"buckets_path": "range_values>_count", <1>
"alternative": ["less", "greater", "two_sided"], <2>
"sampling_method": "upper_tail" <3>
}
}
--------------------------------------------------
// NOTCONSOLE
<1> The buckets containing the values to test against.
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<2> The alternatives to calculate
<3> The sampling method for the K-S statistic


[[bucket-count-ks-test-agg-example]]
==== Example

The following snippet runs the `bucket_count_ks_test` on the individual terms in the field `version` against a uniform distribution.
The uniform distribution reflects the `latency` percentile buckets. Not shown is the pre-calculation of the `latency` indicator values,
which was done utilizing the
<<search-aggregations-metrics-percentile-aggregation,percentiles>> aggregation.

This example is only using the 10s percentiles.
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This example is only using the 10s percentiles.
This example is only using the deciles of `latency`.


[source,console]
-------------------------------------------------
POST correlate_latency/_search?size=0&filter_path=aggregations
{
"aggs": {
"buckets": {
"terms": { <1>
"field": "version",
"size": 2
},
"aggs": {
"latency_ranges": {
"range": { <2>
"field": "latency",
"ranges": [
{ "to": 0.0 },
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Could 0.0 be replaced with 0 so that it is an integer, like numbers in other ranges?

{ "from": 0, "to": 105 },
{ "from": 105, "to": 225 },
{ "from": 225, "to": 445 },
{ "from": 445, "to": 665 },
{ "from": 665, "to": 885 },
{ "from": 885, "to": 1115 },
{ "from": 1115, "to": 1335 },
{ "from": 1335, "to": 1555 },
{ "from": 1555, "to": 1775 },
{ "from": 1775 }
]
}
},
"ks_test": { <3>
"bucket_count_ks_test": {
"buckets_path": "latency_ranges>_count",
"alternative": ["less", "greater", "two_sided"]
}
}
}
}
}
}
-------------------------------------------------
// TEST[setup:correlate_latency]

<1> The term buckets containing a range aggregation and the bucket correlation aggregation. Both are utilized to calculate
the correlation of the term values with the latency.
<2> The range aggregation on the latency field. The ranges were created referencing the percentiles of the latency field.
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It would be nice to have a full example somewhere in docs of showing how the ranges were found

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Possibly, I think though that is a larger "how correlations + K-S tests work" type of docs.

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Actually this isn't so bad as you explain that the latency ranges were calculated using a percentiles agg

<3> The bucket count K-S test aggregation that determines if the count samples are from the same distribution as the uniform
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Could this sentence be simplified to:
The bucket count K-S test aggregation that determines if the count samples come from the uniform distribution
?

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technically, no. I will think of a better way of phrasing it.

The two-sampled K-S test (which we are doing here) is verifying that two samples come from the same distribution. In this particular case, we are testing if one sample (a uniform fraction sample) and another (these _counts) both come from the same distribution.

distribution.

And the following may be the response:

[source,console-result]
----
{
"aggregations" : {
"buckets" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "1.0",
"doc_count" : 100,
"latency_ranges" : {
"buckets" : [
{
"key" : "*-0.0",
"to" : 0.0,
"doc_count" : 0
},
{
"key" : "0.0-105.0",
"from" : 0.0,
"to" : 105.0,
"doc_count" : 1
},
{
"key" : "105.0-225.0",
"from" : 105.0,
"to" : 225.0,
"doc_count" : 9
},
{
"key" : "225.0-445.0",
"from" : 225.0,
"to" : 445.0,
"doc_count" : 0
},
{
"key" : "445.0-665.0",
"from" : 445.0,
"to" : 665.0,
"doc_count" : 0
},
{
"key" : "665.0-885.0",
"from" : 665.0,
"to" : 885.0,
"doc_count" : 0
},
{
"key" : "885.0-1115.0",
"from" : 885.0,
"to" : 1115.0,
"doc_count" : 10
},
{
"key" : "1115.0-1335.0",
"from" : 1115.0,
"to" : 1335.0,
"doc_count" : 20
},
{
"key" : "1335.0-1555.0",
"from" : 1335.0,
"to" : 1555.0,
"doc_count" : 20
},
{
"key" : "1555.0-1775.0",
"from" : 1555.0,
"to" : 1775.0,
"doc_count" : 20
},
{
"key" : "1775.0-*",
"from" : 1775.0,
"doc_count" : 20
}
]
},
"ks_test" : {
"less" : 2.248673241788478E-4,
"greater" : 1.0,
"two_sided" : 2.248673241788478E-4
}
},
{
"key" : "2.0",
"doc_count" : 100,
"latency_ranges" : {
"buckets" : [
{
"key" : "*-0.0",
"to" : 0.0,
"doc_count" : 0
},
{
"key" : "0.0-105.0",
"from" : 0.0,
"to" : 105.0,
"doc_count" : 19
},
{
"key" : "105.0-225.0",
"from" : 105.0,
"to" : 225.0,
"doc_count" : 11
},
{
"key" : "225.0-445.0",
"from" : 225.0,
"to" : 445.0,
"doc_count" : 20
},
{
"key" : "445.0-665.0",
"from" : 445.0,
"to" : 665.0,
"doc_count" : 20
},
{
"key" : "665.0-885.0",
"from" : 665.0,
"to" : 885.0,
"doc_count" : 20
},
{
"key" : "885.0-1115.0",
"from" : 885.0,
"to" : 1115.0,
"doc_count" : 10
},
{
"key" : "1115.0-1335.0",
"from" : 1115.0,
"to" : 1335.0,
"doc_count" : 0
},
{
"key" : "1335.0-1555.0",
"from" : 1335.0,
"to" : 1555.0,
"doc_count" : 0
},
{
"key" : "1555.0-1775.0",
"from" : 1555.0,
"to" : 1775.0,
"doc_count" : 0
},
{
"key" : "1775.0-*",
"from" : 1775.0,
"doc_count" : 0
}
]
},
"ks_test" : {
"less" : 0.9642895789647244,
"greater" : 4.58718174664754E-9,
"two_sided" : 4.58718174664754E-9
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I would expect greater to be very nearly two_sided / 2 in this case.

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Yeah, the two_sided isn't accurate. I think Hodge's approximation only works in the sided case. We will probably have to do something else.

}
}
]
}
}
}
----
Original file line number Diff line number Diff line change
Expand Up @@ -97,29 +97,33 @@ public GapPolicy gapPolicy() {
@Override
protected abstract PipelineAggregator createInternal(Map<String, Object> metadata);

@Override
protected void validate(ValidationContext context) {
if (bucketsPaths.length != 1) {
context.addBucketPathValidationError("must contain a single entry for aggregation [" + name + "]");
return;
}
protected void validateBucketPath(ValidationContext context, String bucketsPath) {
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Could this method be private?
I cannot see it being used outside of this class.

// Need to find the first agg name in the buckets path to check its a
// multi bucket agg: aggs are split with '>' and can optionally have a
// metric name after them by using '.' so need to split on both to get
// just the agg name
final String firstAgg = bucketsPaths[0].split("[>\\.]")[0];
final String firstAgg = bucketsPath.split("[>\\.]")[0];
Optional<AggregationBuilder> aggBuilder = context.getSiblingAggregations().stream()
.filter(builder -> builder.getName().equals(firstAgg))
.findAny();
.filter(builder -> builder.getName().equals(firstAgg))
.findAny();
if (aggBuilder.isEmpty()) {
context.addBucketPathValidationError("aggregation does not exist for aggregation [" + name + "]: " + bucketsPaths[0]);
context.addBucketPathValidationError("aggregation does not exist for aggregation [" + name + "]: " + bucketsPath);
return;
}
if (aggBuilder.get().bucketCardinality() != AggregationBuilder.BucketCardinality.MANY) {
context.addValidationError("The first aggregation in " + PipelineAggregator.Parser.BUCKETS_PATH.getPreferredName()
+ " must be a multi-bucket aggregation for aggregation [" + name + "] found :"
+ aggBuilder.get().getClass().getName() + " for buckets path: " + bucketsPaths[0]);
+ " must be a multi-bucket aggregation for aggregation [" + name + "] found :"
+ aggBuilder.get().getClass().getName() + " for buckets path: " + bucketsPath);
}
}

@Override
protected void validate(ValidationContext context) {
if (bucketsPaths.length != 1) {
context.addBucketPathValidationError("must contain a single entry for aggregation [" + name + "]");
return;
}
validateBucketPath(context, bucketsPaths[0]);
}

@Override
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Original file line number Diff line number Diff line change
Expand Up @@ -237,6 +237,7 @@
import org.elasticsearch.xpack.ml.action.TransportValidateJobConfigAction;
import org.elasticsearch.xpack.ml.aggs.correlation.BucketCorrelationAggregationBuilder;
import org.elasticsearch.xpack.ml.aggs.correlation.CorrelationNamedContentProvider;
import org.elasticsearch.xpack.ml.aggs.kstest.BucketCountKSTestAggregationBuilder;
import org.elasticsearch.xpack.ml.annotations.AnnotationPersister;
import org.elasticsearch.xpack.ml.autoscaling.MlAutoscalingDeciderService;
import org.elasticsearch.xpack.ml.autoscaling.MlAutoscalingNamedWritableProvider;
Expand Down Expand Up @@ -1087,7 +1088,8 @@ public Map<String, AnalysisProvider<TokenizerFactory>> getTokenizers() {
public List<PipelineAggregationSpec> getPipelineAggregations() {
return Arrays.asList(
InferencePipelineAggregationBuilder.buildSpec(modelLoadingService, getLicenseState()),
BucketCorrelationAggregationBuilder.buildSpec()
BucketCorrelationAggregationBuilder.buildSpec(),
BucketCountKSTestAggregationBuilder.buildSpec()
);
}

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