29 releases
0.9.1 | Jan 17, 2024 |
---|---|
0.9.0 | Jul 17, 2023 |
0.8.13 | Mar 11, 2023 |
0.8.12 | Nov 19, 2022 |
0.2.0 | Mar 12, 2019 |
#147 in Command line utilities
112 downloads per month
71KB
1K
SLoC
rpick
is a command line tool that helps you to pick items from a list, using
configurable algorithms.
An example use case for this is picking a restaurant. You might want to generally go to restaurants you haven't visited in a while, but you also might not want to use a strict least recently used model and spice things up with some element of chance, with restaurants you've least recently visited getting a boost in their chances.
Install
You can install rpick
with an Arch AUR package:
$ git clone https://aur.archlinux.org/rpick.git
$ cd rpick
$ makepkg -sirc
Or in Fedora:
$ sudo dnf install rpick
Or in Gentoo:
$ sudo emerge app-misc/rpick
Or in MacOS, assuming you have installed brew:
$ brew install rust
# Be sure to read the output of this command and adjust your PATH as instructed.
$ cargo install rpick
If you are not using one of the systems described above, rpick
is also
available on crates.io. You can install rpick this way by first
installing Rust, and then using Rust's cargo tool to
install rpick:
# Be sure to read the output of this command and adjust your PATH as instructed.
$ cargo install rpick
Quick start
rpick
keeps its state in a YAML file in your home config directory called
rpick.yml
. For now, users must create this file by hand, and rpick
will manage it from
there. To get started with some examples, create ~/.config/rpick.yml
in Linux,
~/Library/Preferences/rpick.yml
on MacOS, or C:\Users\Alice\AppData\Roaming\rpick.yml
on
Windows, like this:
---
prs:
model: even
choices:
- paper
- rock
- scissors
restaurant:
model: gaussian
choices:
- Spirits
- Lucky 32
- Centro
- Sitti
- Cookout
Then you can ask rpick
to pick a game of paper rock scissors for you:
$ rpick prs
Choice is scissors. Accept? (Y/n)
Note that it would be bad to use the Gaussian model for paper rock scissors, because you have a statistical advantage of guessing that model's results. Let's take a look at the Gaussian model:
$ rpick restaurant
Choice is Lucky 32. Accept? (Y/n)
If you say yes, it will rewrite the yaml file like this since we used the Gaussian model:
---
prs:
model: even
choices:
- paper
- rock
- scissors
restaurant:
model: gaussian
stddev_scaling_factor: 3.0
choices:
- Spirits
- Centro
- Sitti
- Cookout
- Lucky 32
Note that we passed prs
and then restaurant
as arguments when we called rpick
-
this told rpick
to look for those objects in rpick.yml
to find out which models to use
and which choices were available. This parameter is required, but its possible values are defined by
you in your config file.
The model
field in the config file defines which mathematical
model to use to pick from the given choices. See the Models section below for more information about
which models are available and how you can configure them.
It added one setting to your restaurant object that wasn't there originally:
stddev_scaling_factor
. You can read more about this setting in the Gaussian model
documentation below.
Parameters
The CLI accepts a few parameters:
-c/--config
: This can be used to specify an alternate path for a config file forrpick
to use. You can also set theRPICK_CONFIG
environment variable.-h/--help
: Print help text.-v/--verbose
: Print more information about the pick.-V/--version
: Print the rpick version.
Models
rpick
is capable of a few different algorithms for picking choices: even, gaussian, inventory,
lottery, lru, and weighted.
Even
The even
distribution model is the simplest available choice model. It will give an even
chance to each item in the list of strings to be chosen. It requires two keys:
model
: This must be set to the string "even", in order to select this model.choices
: This is a list of strings that are the options for the model to choose from.
Example:
convertible_top:
model: even
choices:
- up
- down
You might want to consult the weather before using rpick
for this use case…
Gaussian
To understand the Gaussian distribution model, we need to first study this XKCD comic:
The gaussian
distribution model is more complex. It uses the
Gaussian distribution to prefer choices that
have been less recently chosen. Things near the top of the list of choices have the highest
probability of being chosen, while things at the end of the list have the lowest chance. Once an
item has been picked and the user has accepted the choice, the list is saved to disk with the picked
item moved to the end of the list. This model accepts three keys:
model
: This must be set to the string "gaussian", in order to select this model.stddev_scaling_factor
is used to derive the standard deviation; the standard deviation is the length of the list of choices, divided by this scaling factor. Thus, a larger scaling factor will result in a stronger preference for items near the top of the list, and a smaller scaling factor will result in a more even distribution among the choices. Note that the smaller the scaling factor is, the longer rpick will take to make a decision, on average. The default is3.0
, which is chosen because it places the last item on the list at three standard deviations, giving it a 0.03% chance of being chosen. This key is optional, and defaults to 3.0.choices
: This is a list of strings that are the options for the model to choose from.
Example:
album:
model: gaussian
stddev_scaling_factor: 5.0
choices:
- Fountains of Wayne/Fountains Of Wayne
- Beck/Odelay
- "Townes Van Zandt/High, Low and In Between"
- Tori Amos/From The Choirgirl Hotel
- Zao/Parade Of Chaos
To show a visual representation of the relative frequencies of this model, here is a graph that was
generated by calling the Gaussian model 10,000 times with the choices set to the numbers 0 to 99
sequentially and a default stddev_scaling_factor
of 3.0
:
Inventory
The inventory
distribution model is a dynamic version of the weighted
model. Each of the
choices has a certain number of lottery tickets that influence how likely they are to be picked that
round. Once an item is picked, it loses one ticket, i.e., dropping the inventory of that particular
item by one. It accepts two keys:
model
: This must be set to the string "inventory", in order to select this model.choices
: This must be a list of objects. Each object accepts two keys:name
: This is required, and is the name of the choice.tickets
: The current number of lottery tickets that this choice has. This is optional, an integer, and defaults to 1.
Example:
tea:
model: inventory
choices:
- name: "Tea… Earl Grey… Hot"
tickets: 15
- name: Black
tickets: 2
Lottery
The lottery
distribution model is also a dynamic version of the weighted
model. Each of the choices has a certain number of lottery tickets that
influence how likely they are to be picked that round. Once an item is picked,
it gets its lottery tickets reset to reset
tickets and every choice that
wasn't picked gains more lottery tickets. It accepts two keys:
model
: This must be set to the string "lottery", in order to select this model.choices
: This must be a list of objects. Each object accepts four keys:name
: This is required, and is the name of the choice.reset
: How many tickets the choice is reset to when picked. Defaults to 0.tickets
: The current number of lottery tickets that this choice has. This is optional, an integer, and defaults to 1.weight
: This is an integer expressing how many lottery tickets are given to this choice when it is not chosen. You can use this to influence how often this item gets favored relative to the other choices. It is optional, and defaults to 1.
Example:
activity:
model: lottery
choices:
- name: exercise
- name: read documentation
- name: watch tv
weight: 1000
LRU
The lru
(Least Recently Used) model is a simple ordered FIFO (First In, First Out) list. The
least recently chosen item is the first item in the list. On each pick, rpick simply picks the
first item in the list that the user says yes to, and then moves that item to the end of the
list. It accepts two keys:
model
: This must be set to the string "lru", in order to select this model.choices
: This must be a list of strings. The least recently chosen item should be at the beginning of the list.
Example:
code:
model: lru
choices:
- Add a feature
- Fix a bug
- Write some docs
Weighted
The weighted
distribution model is a more general version of the even
model that allows
you to express different weights for each of the choices. It accepts two keys:
model
: This must be set to the string "weighted", in order to select this model.choices
: This must be a list of objects. Each object accepts two keys:name
: This is required, and is the name of the choice.weight
: This is an integer expressing the weight for the choice. It is optional, and defaults to 1.
Example:
cereal:
model: weighted
choices:
- name: generic bran flakes
- name: cracklin oat bran
weight: 1000
Changelog
See the Changelog.
Contribute
If you would like to contribute to rpick, send me a patch!
There is a Makefile that is handy for development, if you have podman on your system. It's default target is a help menu that describes the available targets.
# Note that root is not required.
$ make check
The podman development environment looks for an rpick config at config.yml
.
Dependencies
~10–19MB
~252K SLoC