https://github.com/aclai-lab/solemodels.jl

Symbolic modeling in Julia!

Science Score: 46.0%

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Low similarity (14.3%) to scientific vocabulary

Keywords

machine-learning symbolic-learning

Keywords from Contributors

logic decision-trees modal-logic time-series-classification

Last synced: 7 months ago · JSON representation

Repository

Symbolic modeling in Julia!

Basic Info

Host: GitHub
Owner: aclai-lab
License: mit
Language: Julia
Default Branch: main
Homepage: https://aclai-lab.github.io/SoleModels.jl/
Size: 2.58 MB

Statistics

Stars: 12
Watchers: 3
Forks: 1
Open Issues: 0
Releases: 32

Topics

machine-learning symbolic-learning

Created over 4 years ago · Last pushed 7 months ago

Metadata Files

Readme License

SoleModels.jl – Symbolic Learning Models

In a nutshell

SoleModels.jl defines the building blocks of symbolic modeling and learning. It features: - Definitions for symbolic models (decision trees/forests, rules, branches, etc.); - Tools for evaluate them, and extracting rules from them; - Support for mixed, neuro-symbolic computation.

These definitions provide a unified base for implementing symbolic algorithms, such as: - Decision tree/random forest learning; - Classification/regression rule extraction; - Association rule mining.

Models

Basic models are: - Leaf models: wrapping native Julia computation (e.g., constants, functions); - Rules: structures with IF antecedent THEN consequent END semantics; - Branches: structures with IF antecedent THEN pos_consequent ELSE neg_consequent END semantics.

Remember that: - An antecedent is a logical formula that can be checked on a logical interpretation (that is, an instance of a symbolic learning dataset), yielding a truth value (e.g., true/false); - A consequent is another model, for example, a (final) constant model or branch to be applied.

Within this framework, a decision tree is no other than a branch with branch and final consequents. Note that antecedents can consist of logical formulas and, in such case, the symbolic models are can be applied to logical interpretations. For more information, refer to SoleLogics.jl, the underlying logical layer.

Other noteworthy models include: - Decision List (or decision table): see Wikipedia; - Decision Tree: see Wikipedia; - Decision Forest (or tree ensemble): see Wikipedia; - Mixed Symbolic Model: a nested structure, mixture of many symbolic models.

Usage: rule extraction from a decision tree

First, train a decision tree: ```julia

Load packages

begin Pkg.add("MLJ"); using MLJ Pkg.add("MLJDecisionTreeInterface"); using MLJDecisionTreeInterface Pkg.add("DataFrames"); using DataFrames Pkg.add("Random"); using Random end

Load dataset

X, y = begin X, y = @load_iris; X = DataFrame(X) X, y end

Split dataset

Xtrain, ytrain, Xtest, ytest = begin train, test = partition(eachindex(y), 0.8, shuffle=true, rng = Random.MersenneTwister(42)); Xtrain, ytrain = X[train, :], y[train]; Xtest, ytest = X[test, :], y[test]; Xtrain, ytrain, Xtest, ytest end;

Train tree

mach = begin Tree = MLJ.@load DecisionTreeClassifier pkg=DecisionTree model = Tree(maxdepth=-1, rng = Random.MersenneTwister(42)) machine(model, Xtrain, y_train) |> fit! end

Inspect the tree

🌱 = fitted_params(mach).tree ```

Then, port it to Sole and play with it: ```julia Pkg.add("DecisionTree"); import DecisionTree as DT

Convert to 🌞-compliant model

🌲 = solemodel(🌱);

Print model

printmodel(🌲);

Inspect the rules

listrules(🌲)

Inspect rule metrics

metricstable(🌲)

Inspect normalized rule metrics

metricstable(🌲, normalize = true)

Make test instances flow into the model, so that test metrics can, then, be computed.

apply!(🌲, Xtest, ytest)

Pretty table of rules and their metrics

metricstable(🌲; normalize = true, metricskwargs = (; additionalmetrics = (; height = r->SoleLogics.height(antecedent(r)))))

Join some rules for the same class into a single, sufficient and necessary condition for that class

metricstable(joinrules(🌲; min_ncovered = 1, normalize = true)) ```

Want to know more?

The formal foundations of the Sole framework are given in giopaglia's PhD thesis: Modal Symbolic Learning: from theory to practice, G. Pagliarini (2024)

About

The package is developed by the ACLAI Lab @ University of Ferrara.

SoleModels.jl mainly builds upon SoleLogics.jl and SoleData.jl, and it is the core module of Sole.jl, an open-source framework for symbolic machine learning.

Owner

Name: Applied Computational Logic and Artificial Intelligence Laboratory
Login: aclai-lab
Kind: organization
Email: aclai@unife.it
Location: Italy

Website: aclai.unife.it
Repositories: 14
Profile: https://github.com/aclai-lab

Applied Computational Logic and Artificial Intelligence (ACLAI) Laboratory of the Department of Mathematics and Computer Science, University of Ferrara

GitHub Events

Total

Create event: 29
Commit comment event: 3
Issues event: 5
Release event: 3
Watch event: 1
Delete event: 32
Issue comment event: 36
Push event: 230
Pull request review event: 13
Pull request review comment event: 15
Pull request event: 59
Fork event: 2

Last Year

Create event: 29
Commit comment event: 3
Issues event: 5
Release event: 3
Watch event: 1
Delete event: 32
Issue comment event: 36
Push event: 230
Pull request review event: 13
Pull request review comment event: 15
Pull request event: 59
Fork event: 2

Committers

Last synced: 12 months ago

All Time

Total Commits: 614
Total Committers: 12
Avg Commits per committer: 51.167
Development Distribution Score (DDS): 0.277

Past Year

Commits: 169
Committers: 8
Avg Commits per committer: 21.125
Development Distribution Score (DDS): 0.296

Top Committers

Name	Email	Commits
giopaglia	2****a	444
Michele21	g**2@g**m	66
mauro-milella	m**o@l**t	50
edo-007	e**7@g**m	22
PasoStudio73	p**3@g**m	9
CompatHelper Julia	c**y@j**g	7
Alberto Paparella	p**t@u**t	5
Federico Manzella	f**a@g**m	4
mauro.milella	m**a@e**t	2
eduardstan	s**d@g**m	2
Perro2110	p**1@g**m	2
paraandrea	a**i@g**m	1

Committer Domains (Top 20 + Academic)

edu.unife.it: 1 unife.it: 1 julialang.org: 1 live.it: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time

Total issues: 6
Total pull requests: 76
Average time to close issues: 28 days
Average time to close pull requests: 3 months
Total issue authors: 4
Total pull request authors: 8
Average comments per issue: 20.17
Average comments per pull request: 0.59
Merged pull requests: 22
Bot issues: 0
Bot pull requests: 56

Past Year

Issues: 4
Pull requests: 43
Average time to close issues: about 2 months
Average time to close pull requests: 30 days
Issue authors: 2
Pull request authors: 7
Average comments per issue: 0.0
Average comments per pull request: 1.05
Merged pull requests: 17
Bot issues: 0
Bot pull requests: 25

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Top Authors

Issue Authors

PasoStudio73 (3)
giopaglia (1)
alberto-paparella (1)
JuliaTagBot (1)

Pull Request Authors

github-actions[bot] (52)
Perro2110 (5)
mauro-milella (5)
PasoStudio73 (4)
dependabot[bot] (4)
alberto-paparella (3)
giopaglia (2)
paraandrea (2)

Top Labels

Issue Labels

enhancement (2)

Pull Request Labels

dependencies (4) github_actions (4) enhancement (4)

Packages

Total packages: 1
Total downloads:
- julia 4 total

Total dependent packages: 2
Total dependent repositories: 0
Total versions: 32

juliahub.com: SoleModels

Symbolic modeling in Julia!

Homepage: https://aclai-lab.github.io/SoleModels.jl/
Documentation: https://docs.juliahub.com/General/SoleModels/stable/
License: MIT
Latest release: 0.10.3
published 7 months ago

Versions: 32
Dependent Packages: 2
Dependent Repositories: 0
Downloads: 4 Total

Rankings

Dependent repos count: 9.8%

Average: 37.3%

Dependent packages count: 38.4%

Stargazers count: 46.4%

Forks count: 54.6%

Last synced: 7 months ago

Dependencies

.github/workflows/TagBot.yml actions

JuliaRegistries/TagBot v1 composite

.github/workflows/CompatHelper.yml actions

julia-actions/setup-julia v1 composite

.github/workflows/Documentation.yml actions

actions/checkout v2 composite
julia-actions/setup-julia latest composite

.github/workflows/ci.yml actions

actions/checkout v2 composite
codecov/codecov-action v3 composite
julia-actions/julia-buildpkg v1 composite
julia-actions/julia-processcoverage v1 composite
julia-actions/julia-runtest v1 composite
julia-actions/setup-julia v1 composite

https://github.com/aclai-lab/solemodels.jl

Science Score: 46.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

SoleModels.jl – Symbolic Learning Models

In a nutshell

Models

Usage: rule extraction from a decision tree

Load packages

Load dataset

Split dataset

Train tree

Inspect the tree

Convert to 🌞-compliant model

Print model

Inspect the rules

Inspect rule metrics

Inspect normalized rule metrics

Make test instances flow into the model, so that test metrics can, then, be computed.

Pretty table of rules and their metrics

Join some rules for the same class into a single, sufficient and necessary condition for that class

Want to know more?

About

Owner

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

juliahub.com: SoleModels

Rankings

Dependencies