Tuple-sum-filter - Library to play with filtering numeric sequences by sums of their pairs, triplets, etc. With a bonus CLI demo

Last update: Feb 16, 2022

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Overview

Tuple Sum Filter

A library to play with filtering numeric sequences by sums of their pairs, triplets, etc.

Comes with a bonus CLI to demo the functionality.

Requires (and CI tests on) python 3.8 to 3.10. If you need to use python 3.7 then try replacing math.prod(some_iterable) with functools.reduce(lambda x, y: x * y, some_iterable)

Approach

We're thinking of this mostly as a library with the CLI as only for demo purposes. Ways you can see this in the code:

logging should really handled by the consumer,
- our get_logger should be something that is passed into the lib
the CLI is pretty light on automated tests
we use pretty loose production dependency pinning
- rather than pip freeze > requirements.txt of a deployed app
- we want to keep things loose so that consumers can keep installing us alongside other things
- we should probably set up tox/nox test runs against v.latest of our dependencies

Running the demo

in a fresh virtualenv (python>=3.8)

# install project and deps
pip install git+https://github.com/lbillingham/tuple-sum-filter.git

# create a suitable input file
echo "1721\n979\n366\n299\n675\n1456\n" > example.txt

# run the demo
filter_demo --input_file=example.txt --sum_target=2020 --dimension=2

you should see output like

checking for pairs of numbers that sum to 2020 in example.txt
Results pair (1721, 299) match: sum to 2020 and multiply to 514579

Consuming the library

The main filtering functions are pairs_that_sum_to and triplets_that_sum_to. They both have signatures (numbers: Sequence[int|float], sum_target: int|float) -> things_that_passed_the_filter list[tuple].

There is also a file-reading helper numbers_in_file exported at the top level.

Developing

Run the following to install the project (and dev dependencies) into your active virtualenv:

make dev_install

day-to-day development tasks can be orchestrated via make

dependency management
test/lint/typecheck running
coverage reporting
run make without any arguments to see a list

There is a CI suite which runs lint and test on several python versions. We don't run typechecking as a gate in CI because we think that turns a sometimes-useful tool into a Goodhart target.

Performance

We have not been optimizing for performance and it kind of shows.

When we run the benchmarking suite we see ~0.4 seconds fairly consistently for the triplet/3D problem.

We have at least 3 ideas of how to speed things up: several of them include dropping floating-point support.

$ make benchmark

tests/performance_check.py ..                                                                                                                                [100%]


------------------------------------------------------------------------------------- benchmark: 2 tests ------------------------------------------------------------------------------------
Name (time in ms)             Min                 Max                Mean            StdDev              Median               IQR            Outliers       OPS            Rounds  Iterations
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_input1_pairs          5.4665 (1.0)        6.2297 (1.0)        5.6687 (1.0)      0.1018 (1.0)        5.6575 (1.0)      0.1289 (1.0)          47;3  176.4077 (1.0)         172           1
test_input1_triplets     384.6154 (70.36)    386.5000 (62.04)    385.4776 (68.00)    0.8287 (8.14)     385.4333 (68.13)    1.5047 (11.67)         2;0    2.5942 (0.01)          5           1
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

🍪 ✂️ cookiecut from lbillingham's python-cli-template

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Comments

Perf: :zap: merge if you want to go faster but don't need float support

This moves away from the shared itertools implimentations for finding pairs, triplets of the input numbers that sum to a given target.

Instead, we

1st expose the underlying $~O^{dimensions}$ nested loops
trade some extra memory and some $O^{1}$ lookups to give us $~O^{dimensions-1}$
get a >= 170x speedup in our benchmarks

However, we:

loose the ability to properly work with floating point input
- the fast lookup uses hasing and hashing floats gets weird due to floating point equality
can't trivially extend to higher-dimension problems: 4-element-tuples etc.

I've moved the float input tests out the their own file and away from the CI test path

Performance benchmarks

with these changes:

$ make benchmark
tests/performance_check.py ..                                                                                                                             [100%]

-------------------------------------------------------------------------------------------- benchmark: 2 tests --------------------------------------------------------------------------------------------
Name (time in us)               Min                   Max                  Mean              StdDev                Median                 IQR            Outliers          OPS            Rounds  Iterations
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_input1_pairs           22.1660 (1.0)        166.3790 (1.0)         23.6089 (1.0)        5.0170 (1.0)         23.0000 (1.0)        0.5123 (1.0)        87;521  42,356.8183 (1.0)        7677           1
test_input1_triplets     1,994.8000 (89.99)    3,561.1120 (21.40)    2,152.1272 (91.16)    204.1428 (40.69)    2,033.1040 (88.40)    299.1878 (584.07)       41;4     464.6565 (0.01)        341           1
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

itertools, but non-float supporting version

$ make benchmark
tests/performance_check.py ..                                                                                                      [100%]

------------------------------------------------------------------------------------- benchmark: 2 tests ------------------------------------------------------------------------------------
Name (time in ms)             Min                 Max                Mean            StdDev              Median               IQR            Outliers       OPS            Rounds  Iterations
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_input1_pairs          2.8727 (1.0)        4.2386 (1.0)        3.1265 (1.0)      0.1638 (1.0)        3.1067 (1.0)      0.1888 (1.0)          78;9  319.8414 (1.0)         326           1
test_input1_triplets     211.6325 (73.67)    213.3950 (50.35)    212.4042 (67.94)    0.6555 (4.00)     212.2717 (68.33)    0.8081 (4.28)          2;0    4.7080 (0.01)          5           1
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
=========================================================== 2 passed in 3.59s ============================================================

Note the change in units this branch is in microseconds, the itertools version is in milliseconds

opened by lbillingham 0

Releases(v0.0.1)

v0.0.1(Feb 17, 2022)

Initial release. Lib allows filtering by sum over pairs and triplets of numbers loaded from a local file. Plus a bonus CLI app that can be used for demoing the lib.

Solution is itertools-y and rather slow (probably $O^{n}$ where pairs->n=2 and triplets->n=3).

This is the version shown to MM
Source code(tar.gz)
Source code(zip)

Owner

Laurence Billingham

+ sustainable software + data science

GitHub Repository

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