online: online statistics

[ bsd3, library, statistics ] [ Propose Tags ]

transformation of statistics to online algorithms


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Versions [faq] 0.2.0, 0.2.1.0, 0.2.2.0, 0.2.3.0, 0.3.0.0, 0.4.0.0
Dependencies base (>=4.7 && <5), foldl, formatting, numhask, online, optparse-generic, perf, protolude, scientific, tdigest, text, vector, vector-algorithms [details]
License BSD-3-Clause
Copyright Tony Day
Author Tony Day
Maintainer tonyday567@gmail.com
Category statistics
Home page https://github.com/tonyday567/online#readme
Bug tracker https://github.com/tonyday567/online/issues
Source repo head: git clone https://github.com/tonyday567/online
Uploaded by tonyday567 at Sun Jan 28 03:46:22 UTC 2018
Distributions NixOS:0.4.0.0
Executables online-bench
Downloads 1402 total (132 in the last 30 days)
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Status Hackage Matrix CI
Docs available [build log]
Last success reported on 2018-01-28 [all 1 reports]

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Readme for online-0.2.1.0

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online

Build
Status Hackage lts nightly

online turns a statistic (in haskell this can usually be thought of as a fold of a foldable) into an online algorithm.

motivation

Imagine a data stream, like an ordered indexed container or a time-series of measurements. An exponential moving average can be calculated as a repeated iteration over a stream of xs:

$$ ema_t = ema_{t-1} * 0.9 + x_t * 0.1 $$

The 0.1 is akin to the learning rate in machine learning, or 0.9 can be thought of as a decaying or a rate of forgetting. An exponential moving average learns about what the value of x has been lately, where lately is, on average, about 1/0.1 = 10 x's ago. All very neat.

The first bit of neat is speed. There's 2 times and a plus. The next is space: an ema is representing the recent xs in a size as big as a single x. Compare that with a simple moving average where you have to keep the history of the last n xs around to keep up (just try it).

It's so neat, it's probably a viable monoidal category all by itself.

online

Haskell allows us to abstract the compound ideas in an ema and create polymorphic routines over a wide variety of statistics, so that they all retain these properties of speed, space and rigour.

av xs = L.fold (online identity (.* 0.9)) xs
-- av [0..10] == 6.030559401413827
-- av [0..100] == 91.00241448887785

online identity (.* 0.9) is how you express an ema with a decay rate of 0.9.

online works for any statistic. Here's the construction of standard deviation using applicative style:

std :: Double -> L.Fold Double Double
std r = (\s ss -> sqrt (ss - s**2)) <$> ma r <*> sqma r
  where
    ma r = online identity (.*r)
    sqma r = online (**2) (.*r)

perf

1 cycle = 0.4 nanoseconds.

sum to 1,000
run                       first     2nd     3rd     4th     5th  40th %
sumInt [0..]             8.69e3  1.59e3  1.54e3  1.60e3  1.64e3 1.57e3 cycles
sumDouble [0..]          3.00e5  2.82e5  3.27e5  2.99e5  2.61e5 9.38e4 cycles
sumPoly [0..]            9.34e4  1.05e5  1.09e5  1.18e5  1.64e5 7.81e4 cycles
sum Int                  1.67e4  1.17e4  1.16e4  1.17e4  1.16e4 1.17e4 cycles
sum Double               2.63e4  1.18e4  1.17e4  1.16e4  1.16e4 1.16e4 cycles
sum Poly                 1.28e4  1.18e4  1.17e4  1.17e4  1.18e4 1.17e4 cycles
fold sum                 1.23e4  1.19e4  1.18e4  1.17e4  1.18e4 1.18e4 cycles
fold av                  1.23e4  1.18e4  1.18e4  1.18e4  1.18e4 1.18e4 cycles
fold ma                  1.24e4  1.19e4  1.20e4  1.19e4  1.19e4 1.19e4 cycles
fold std                 2.04e5  1.15e5  6.14e5  1.16e5  1.16e5 1.12e5 cycles
fold maL1                9.11e4  8.32e4  1.27e5  3.34e5  1.04e5 1.02e5 cycles
fold absmaL1             6.82e4  6.71e4  6.64e4  6.66e4  2.97e5 6.65e4 cycles

recipe

stack build --test --exec "$(stack path --local-install-root)/bin/online-bench" --exec "$(stack path --local-bin)/pandoc -f markdown -i other/header.md other/readme_.md other/footer.md -t html -o index.html --filter pandoc-include --mathjax" --exec "$(stack path --local-bin)/pandoc -f markdown -i other/readme_.md -t markdown -o readme.md --filter pandoc-include --mathjax" --file-watch