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https://github.com/juliaai/scientifictypes.jl

An API for dispatching on the "scientific" type of data instead of the machine type

https://github.com/juliaai/scientifictypes.jl

Science Score: 23.0%

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    2 of 8 committers (25.0%) from academic institutions
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    Low similarity (13.4%) to scientific vocabulary

Keywords

julia machine-learning mlj statistics

Keywords from Contributors

numeric programming-language probability-distributions tuning-parameters stacking predictive-modeling pipelines ensemble-learning julialang data-frame
Last synced: 10 months ago · JSON representation

Repository

An API for dispatching on the "scientific" type of data instead of the machine type

Basic Info
  • Host: GitHub
  • Owner: JuliaAI
  • License: mit
  • Language: Julia
  • Default Branch: dev
  • Homepage:
  • Size: 704 KB
Statistics
  • Stars: 99
  • Watchers: 8
  • Forks: 9
  • Open Issues: 13
  • Releases: 46
Topics
julia machine-learning mlj statistics
Created almost 7 years ago · Last pushed 11 months ago
Metadata Files
Readme

README.md

ScientificTypes.jl

| Linux | Coverage | Documentation | | :-----------: | :------: | :-----------: | | Build Status | codecov |

This package makes a distinction between machine type and scientific type of a Julia object:

  • The machine type refers to the Julia type being used to represent the object (for instance, Float64).

  • The scientific type is one of the types defined in ScientificTypesBase.jl reflecting how the object should be interpreted (for instance, Continuous or Multiclass).

Contents

Installation

julia using Pkg Pkg.add("ScientificTypes")

Who is this repository for?

  • developers of statistical and scientific software who want to articulate their data type requirements in a generic, purpose-oriented way, and who are furthermore happy to adopt an existing convention about what data types should be used for what purpose (a convention first developed for the MLJ ecosystem, but useful in a general context)

What's provided here?

The module ScientificTypes defined in this repo rexports the scientific types and associated methods defined in ScientificTypesBase.jl and provides:

  • a collection of scitype definitions that articulate a default convention.

  • a coerce function, for changing machine types to reflect a specified scientific interpretation (scientific type)

  • an autotype fuction for "guessing" the intended scientific type of data

Very quick start

For more information and examples please refer to the manual.

julia using ScientificTypes, DataFrames X = DataFrame( a = randn(5), b = [-2.0, 1.0, 2.0, missing, 3.0], c = [1, 2, 3, 4, 5], d = [0, 1, 0, 1, 0], e = ['M', 'F', missing, 'M', 'F'], ) sch = schema(X)

will print

┌───────┬────────────────────────────┬─────────────────────────┐ │ names │ scitypes │ types │ ├───────┼────────────────────────────┼─────────────────────────┤ │ a │ Continuous │ Float64 │ │ b │ Union{Missing, Continuous} │ Union{Missing, Float64} │ │ c │ Count │ Int64 │ │ d │ Count │ Int64 │ │ e │ Union{Missing, Unknown} │ Union{Missing, Char} │ └───────┴────────────────────────────┴─────────────────────────┘

Detail is obtained in the obvious way; for example:

julia julia> sch.names (:a, :b, :c, :d, :e)

To specify that instead b should be regared as Count, and that both d and e are Multiclass, we use the coerce function:

julia Xc = coerce(X, :b=>Count, :d=>Multiclass, :e=>Multiclass) schema(Xc)

which prints

``` ┌───────┬───────────────────────────────┬────────────────────────────────────────────────┐ │ names │ scitypes │ types │ ├───────┼───────────────────────────────┼────────────────────────────────────────────────┤ │ a │ Continuous │ Float64 │ │ b │ Union{Missing, Count} │ Union{Missing, Int64} │ │ c │ Count │ Int64 │ │ d │ Multiclass{2} │ CategoricalValue{Int64, UInt32} │ │ e │ Union{Missing, Multiclass{2}} │ Union{Missing, CategoricalValue{Char, UInt32}} │ └───────┴───────────────────────────────┴────────────────────────────────────────────────┘

```

Acknowledgements and history

ScientificTypes is based on code from MLJScientificTypes.jl (now deprecated) and in particular builds on contributions of Anthony Blaom (@ablaom), Thibaut Lienart (@tlienart), Samuel Okon (@OkonSamuel), and others not recorded in the ScientificTypes commit history.

ScientificTypes.jl 2.0 implements the DefaultConvention, which coincides with the deprecated MLJ convention of MLJScientificTypes.jl 0.4.8. The code at ScientificTypes 1.1.2 (which defined only the API) became ScientificTypesBase.jl 1.0.

Owner

  • Name: JuliaAI
  • Login: JuliaAI
  • Kind: organization

Home for repositories of the MLJ (Machine Learning in Julia) project

GitHub Events

Total
  • Release event: 1
  • Watch event: 3
  • Issue comment event: 13
  • Push event: 7
  • Pull request review event: 1
  • Pull request event: 12
  • Fork event: 1
  • Create event: 5
Last Year
  • Release event: 1
  • Watch event: 3
  • Issue comment event: 13
  • Push event: 7
  • Pull request review event: 1
  • Pull request event: 12
  • Fork event: 1
  • Create event: 5

Committers

Last synced: about 1 year ago

All Time
  • Total Commits: 289
  • Total Committers: 8
  • Avg Commits per committer: 36.125
  • Development Distribution Score (DDS): 0.356
Past Year
  • Commits: 7
  • Committers: 2
  • Avg Commits per committer: 3.5
  • Development Distribution Score (DDS): 0.143
Top Committers
Name Email Commits
Anthony D. Blaom a****m@g****m 186
Thibaut Lienart l****b@m****m 76
Júlio Hoffimann j****n@g****m 10
OkonSamuel o****0@g****m 10
Dilum Aluthge d****m@a****m 4
github-actions[bot] 4****] 1
fkiraly f****y@u****k 1
Felix Cremer f****r@d****e 1
Committer Domains (Top 20 + Academic)
dlr.de: 1 ucl.ac.uk: 1 aluthge.com: 1 me.com: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 37
  • Total pull requests: 79
  • Average time to close issues: 3 months
  • Average time to close pull requests: 4 days
  • Total issue authors: 11
  • Total pull request authors: 8
  • Average comments per issue: 4.49
  • Average comments per pull request: 1.16
  • Merged pull requests: 69
  • Bot issues: 0
  • Bot pull requests: 7
Past Year
  • Issues: 2
  • Pull requests: 11
  • Average time to close issues: N/A
  • Average time to close pull requests: 20 minutes
  • Issue authors: 2
  • Pull request authors: 3
  • Average comments per issue: 0.5
  • Average comments per pull request: 0.45
  • Merged pull requests: 7
  • Bot issues: 0
  • Bot pull requests: 4
View more stats
Top Authors
Issue Authors
  • ablaom (22)
  • juliohm (4)
  • DilumAluthge (2)
  • tlienart (2)
  • dom-linkevicius (1)
  • igibek (1)
  • EssamWisam (1)
  • OkonSamuel (1)
  • mtsch (1)
  • ParadaCarleton (1)
  • JuliaTagBot (1)
Pull Request Authors
  • ablaom (52)
  • tlienart (13)
  • github-actions[bot] (7)
  • OkonSamuel (2)
  • juliohm (2)
  • DilumAluthge (1)
  • abhro (1)
  • felixcremer (1)
Top Labels
Issue Labels
docs (2) breaking (2) bug (2) low priority (1) code organization (1) waiting on external fix (1) enhancement (1) next minor release (1) next breaking release (1)
Pull Request Labels
easy (1) triage (1) breaking (1)

Packages

  • Total packages: 1
  • Total downloads:
    • julia 1,369 total
  • Total dependent packages: 30
  • Total dependent repositories: 0
  • Total versions: 46
juliahub.com: ScientificTypes

An API for dispatching on the "scientific" type of data instead of the machine type

  • Versions: 46
  • Dependent Packages: 30
  • Dependent Repositories: 0
  • Downloads: 1,369 Total
Rankings
Dependent packages count: 3.2%
Stargazers count: 8.1%
Average: 9.4%
Dependent repos count: 9.9%
Forks count: 16.2%
Last synced: 10 months ago

Dependencies

.github/workflows/CompatHelper.yml actions
  • julia-actions/setup-julia latest composite
.github/workflows/TagBot.yml actions
  • JuliaRegistries/TagBot v1 composite
.github/workflows/ci.yml actions
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  • julia-actions/julia-buildpkg v1 composite
  • julia-actions/julia-processcoverage v1 composite
  • julia-actions/julia-runtest v1 composite
  • julia-actions/setup-julia v1 composite