The standalone logic (not related to PAL internals)
is now available on PyPI [2],
meaning you can do "pip install jumpavg".

Currently there is zero documentation
on how to actually use the library
(unless you are willing to read comments in the code itself).
I will add something when I make PAL
use the library from pip (not from CSIT git).

Vratko.

[2] https://pypi.org/project/jumpavg/

From: Vratko Polak -X (vrpolak - PANTHEON TECHNOLOGIES at Cisco)
Sent: Monday, 2018-June-11 18:27
To: csit-...@lists.fd.io
Cc: 'vpp-dev@lists.fd.io' <vpp-dev@lists.fd.io>
Subject: new anomaly detection

By now, you should be familiar
with Continuous Performance Trending page [0]
leading to various graphs and tables
tracking performance changes of VPP code.

We are planning to replace the old ways
of how do Trendline Graphs look.
There are still few things missing,
but the basic functionality is there,
and you can see the new graphs already
by visiting temporary new page [1]
(which is not reachable from the original one).

As maintaining two pages takes time,
we are planning to replace the old graphs by new graphs
(and delete [1]) some time next week,
depending on the feedback we get.

The old graphs had trending line reflecting
the current and few older runs,
and anomaly (progression, regression, outlier)
was determined by a single run having "unprobable" value.

The new graphs are different.
A trend now consists of several groups of runs,
and the trend line is constant within a group (showing group's average).
Anomaly now means one group ends and other begins,
so red and green circles are at the start of horizontal line
(as opposed to the first dot belonging to the group).

The "magic" is only in deciding group boundaries,
the new algorithm needs a "statistical significance"
to separate two groups.
This means a small regression can go undetected for a few days,
just to be marked when enough subsequent runs confirm it.

Also, currently there are no outliers anymore (only short groups).

Any feedback is appreciated.

Vratko.

[0] https://docs.fd.io/csit/master/trending
[1] https://docs.fd.io/csit/master/trending/new


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