Tools to compare metrics between datasets, accounting for population differences and invariant features.

microsoft, updated 🕥 2023-02-11 01:02:25

Lumos

Lumos is a library to compare metrics between two datasets, accounting for population differences and invariant features. Lumos is described in this technical paper:

@inproceedings{Pool2020, title="Lumos: A Library for Diagnosing Metric Regressions in Web-Scale Applications", author="Jamie Pool, Ebrahim Beyrami, Vishak Gopal, Ashkan Aazami, Jayant Gupchup, Jeff Rowland, Binlong Li, Pritesh Kanani, Ross Cutler, Johannes Gehrke", booktitle="Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining", year="2020" }

Install

You can install latest release of Lumos directly from source: pip install git+https://github.com/microsoft/MS-Lumos

Examples

Please refer to the examples folder.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Issues

Bump ipython from 7.5.0 to 8.10.0

opened on 2023-02-11 01:02:21 by dependabot[bot]

Bumps ipython from 7.5.0 to 8.10.0.

Release notes

Sourced from ipython's releases.

See https://pypi.org/project/ipython/

We do not use GitHub release anymore. Please see PyPI https://pypi.org/project/ipython/

7.9.0

No release notes provided.

7.8.0

No release notes provided.

7.7.0

No release notes provided.

7.6.1

No release notes provided.

7.6.0

No release notes provided.

Commits
  • 15ea1ed release 8.10.0
  • 560ad10 DOC: Update what's new for 8.10 (#13939)
  • 7557ade DOC: Update what's new for 8.10
  • 385d693 Merge pull request from GHSA-29gw-9793-fvw7
  • e548ee2 Swallow potential exceptions from showtraceback() (#13934)
  • 0694b08 MAINT: mock slowest test. (#13885)
  • 8655912 MAINT: mock slowest test.
  • a011765 Isolate the attack tests with setUp and tearDown methods
  • c7a9470 Add some regression tests for this change
  • fd34cf5 Swallow potential exceptions from showtraceback()
  • Additional commits viewable in compare view


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Bump numpy from 1.16.3 to 1.22.0

opened on 2022-06-22 02:23:51 by dependabot[bot]

Bumps numpy from 1.16.3 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

  • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
  • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
  • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
  • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
  • A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits


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Bump jinja2 from 2.10.1 to 2.11.3

opened on 2021-03-20 04:26:56 by dependabot[bot]

Bumps jinja2 from 2.10.1 to 2.11.3.

Release notes

Sourced from jinja2's releases.

2.11.3

This contains a fix for a speed issue with the urlize filter. urlize is likely to be called on untrusted user input. For certain inputs some of the regular expressions used to parse the text could take a very long time due to backtracking. As part of the fix, the email matching became slightly stricter. The various speedups apply to urlize in general, not just the specific input cases.

2.11.2

2.11.1

This fixes an issue in async environment when indexing the result of an attribute lookup, like {{ data.items[1:] }}.

2.11.0

This is the last version to support Python 2.7 and 3.5. The next version will be Jinja 3.0 and will support Python 3.6 and newer.

2.10.3

2.10.2

Changelog

Sourced from jinja2's changelog.

Version 2.11.3

Released 2021-01-31

  • Improve the speed of the urlize filter by reducing regex backtracking. Email matching requires a word character at the start of the domain part, and only word characters in the TLD. :pr:1343

Version 2.11.2

Released 2020-04-13

  • Fix a bug that caused callable objects with __getattr__, like :class:~unittest.mock.Mock to be treated as a :func:contextfunction. :issue:1145
  • Update wordcount filter to trigger :class:Undefined methods by wrapping the input in :func:soft_str. :pr:1160
  • Fix a hang when displaying tracebacks on Python 32-bit. :issue:1162
  • Showing an undefined error for an object that raises AttributeError on access doesn't cause a recursion error. :issue:1177
  • Revert changes to :class:~loaders.PackageLoader from 2.10 which removed the dependency on setuptools and pkg_resources, and added limited support for namespace packages. The changes caused issues when using Pytest. Due to the difficulty in supporting Python 2 and :pep:451 simultaneously, the changes are reverted until 3.0. :pr:1182
  • Fix line numbers in error messages when newlines are stripped. :pr:1178
  • The special namespace() assignment object in templates works in async environments. :issue:1180
  • Fix whitespace being removed before tags in the middle of lines when lstrip_blocks is enabled. :issue:1138
  • :class:~nativetypes.NativeEnvironment doesn't evaluate intermediate strings during rendering. This prevents early evaluation which could change the value of an expression. :issue:1186

Version 2.11.1

Released 2020-01-30

  • Fix a bug that prevented looking up a key after an attribute ({{ data.items[1:] }}) in an async template. :issue:1141

... (truncated)

Commits


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Feature Request: Auto Clustering

opened on 2021-01-26 01:33:51 by caltonji

I am using the feature ranking within MCT for automated Root Cause Analysis of incidents for our Rest API (e.g. increase in InternalServerError responses from our service). Our dataset is our IncomingRequests (uri, datacenter, responsecode, latency, requestId, etc.), merged with OutgoingRequests (target, responsecode, latency, requestId, etc.). If, for example, we are returning InternalServerError because we received 429 in our first call to DocDB, then our metric column, ResponseCode will equal InternalServerError and a feature column DocDB_GetThead_ResponseCode will equal 429 and we expect our automated Root Cause Analysis tool to tell us that the reason for InternalServerError increase is DocDB_GetThead_ResponseCode == 429.

Ours is a situation of multicollinearity. If the first call to DocDB fails, then all subsequent calls will not happen so for all of the failures, another column, say DocDB_UpdateThead_ResponseCode will be empty. We would like auto clustering, so that instead of producing some 200 "Features Explaining Metric Difference" with the actual root cause buried beneath the noise, we instead produce a handful of combinations of features that are correlated to our metric.

Thank you for your awesome work with this tool!

Microsoft

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