AD model performance benchmark #731
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
kaituo
force-pushed
the
precision_2.2
branch
from
cbac525
to
6d63da4
Compare
This PR adds an AD model performance benchmark so that we can compare model performance across versions. Regarding benchmark data, we randomly generated synthetic data with known anomalies inserted throughout the signal. In particular, these are one/two/four dimensional data where each dimension is a noisy cosine wave. Anomalies are inserted into one dimension with 0.003 probability. Anomalies across each dimension can be independent or dependent. We have approximately 5000 observations per data set. The data set is generated using the same random seed so the result is comparable across versions. We also backported opensearch-project#600 so that we can capture the performance data in CI output. Testing done: * added unit tests to run the benchmark. Signed-off-by: Kaituo Li <kaituo@amazon.com>
kaituo
force-pushed
the
precision_2.2
branch
from
6d63da4
to
f403fa3
Compare
ohltyler
approved these changes
Nov 17, 2022
amitgalitz
approved these changes
Nov 17, 2022
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR adds an AD model performance benchmark so that we can compare model performance across versions.
For the single stream detector, we refactored tests in DetectionResultEvalutationIT and moved it to SingleStreamModelPerfIT.
For the HCAD detector, we randomly generated synthetic data with known anomalies inserted throughout the signal. In particular, these are one/two/four dimensional data where each dimension is a noisy cosine wave. Anomalies are inserted into one dimension with 0.003 probability. Anomalies across each dimension can be independent or dependent. We have approximately 5000 observations per data set. The data set is generated using the same random seed so the result is comparable across versions.
Testing done:
Signed-off-by: Kaituo Li kaituo@amazon.com
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
For more information on following Developer Certificate of Origin and signing off your commits, please check here.