https://github.com/aclai-lab/modaldecisiontrees.jl
Julia implementation of Modal Decision Trees & Forests, for interpretable classification of spatial and temporal data. Long live Symbolic Learning!!
https://github.com/aclai-lab/modaldecisiontrees.jl
decision-trees logic machine-learning modal-logic symbolic-learning time-series-classification
Last synced: about 2 months ago
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Julia implementation of Modal Decision Trees & Forests, for interpretable classification of spatial and temporal data. Long live Symbolic Learning!!
- Host: GitHub
- URL: https://github.com/aclai-lab/modaldecisiontrees.jl
- Owner: aclai-lab
- License: other
- Created: 2023-07-14T16:39:11.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-10T11:43:24.000Z (8 months ago)
- Last Synced: 2024-10-17T09:44:31.942Z (7 months ago)
- Topics: decision-trees, logic, machine-learning, modal-logic, symbolic-learning, time-series-classification
- Language: Julia
- Homepage:
- Size: 1.02 MB
- Stars: 11
- Watchers: 2
- Forks: 0
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.bib
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README
# Modal Decision Trees & Forests
[](https://aclai-lab.github.io/ModalDecisionTrees.jl)
[](https://aclai-lab.github.io/ModalDecisionTrees.jl/dev)
[](https://cirrus-ci.com/github/aclai-lab/ModalDecisionTrees.jl)
[](https://codecov.io/gh/aclai-lab/ModalDecisionTrees.jl)
[](https://mybinder.org/v2/gh/aclai-lab/ModalDecisionTrees.jl/HEAD?labpath=pluto-demo.jl)
### Interpretable models for native time-series & image classification!
This package provides algorithms for learning *decision trees* and *decision forests* with enhanced abilities.
Leveraging the express power of Modal Logic, these models can extract *temporal/spatial patterns*, and can natively handle *time series* and *images* (without any data preprocessing). Currently available via [MLJ.jl](https://github.com/alan-turing-institute/MLJ.jl) and [*Sole.jl*](https://github.com/aclai-lab/Sole.jl).
#### Features & differences with [DecisionTree.jl](https://github.com/JuliaAI/DecisionTree.jl):
The MLJ models provided (`ModalDecisionTree` and `ModalRandomForest`) can act as drop in replacements for DecisionTree.jl's tree and forest models. The main difference is that the two models provided are [probabilistic](https://alan-turing-institute.github.io/MLJ.jl/dev/adding_models_for_general_use/#Overview) and can perform both classification (with y labels of type `String` or `CategoricalValue`), and regression (with numeric y labels).
Additionally, these models:
- Are able to handle variables that are `AbstractVector{<:Real}` or `AbstractMatrix{<:Real}`;
- Support [multimodal](https://en.wikipedia.org/wiki/Multimodal_learning) learning (e.g., learning from *combinations* of scalars, time series and images);
- A unique algorithm that extends CART and C4.5;
#### Current limitations (also see [TODOs](#todos)):
- Only supports numeric features;
- Does not support `missing` or `NaN` values.
#### JuliaCon 2022 8-minute talk
## Installation & Usage
Simply type the following commands in Julia's REPL:
```julia
# Install package
using Pkg; Pkg.add("MLJ");
using Pkg; Pkg.add("ModalDecisionTrees");
# Import packages
using MLJ
using ModalDecisionTrees
using Random
# Load an example dataset (a temporal one)
X, y = ModalDecisionTrees.load_japanesevowels()
N = length(y)
# Instantiate an MLJ machine based on a Modal Decision Tree with ≥ 4 samples at leaf
mach = machine(ModalDecisionTree(min_samples_leaf=4), X, y)
# Split dataset
p = randperm(N)
train_idxs, test_idxs = p[1:round(Int, N*.8)], p[round(Int, N*.8)+1:end]
# Fit
fit!(mach, rows=train_idxs)
# Perform predictions, compute accuracy
yhat = predict_mode(mach, X[test_idxs,:])
accuracy = MLJ.accuracy(yhat, y[test_idxs])
# Print model
report(mach).printmodel(3)
# Access raw model
model = fitted_params(mach).model
```
## Want to know more?
[Della Monica, Dario, et al. "Decision trees with a modal flavor." International Conference of the Italian Association for Artificial Intelligence. Cham: Springer International Publishing, 2022.](https://link.springer.com/chapter/10.1007/978-3-031-27181-6_4)
Most of the works in *symbolic learning* are based either on Propositional Logics (PLs) or First-order Logics (FOLs); PLs are the simplest kind of logic and can only handle *tabular data*, while FOLs can express complex entity-relation concepts. Machine Learning with FOLs enables handling data with complex topologies, such as time series, images, or videos; however, these logics are computationally challenging. Instead, Modal Logics (e.g. [Interval Logic](https://en.wikipedia.org/wiki/Interval_temporal_logic)) represent a perfect trade-off in terms of computational tractability and expressive power, and naturally lend themselves for expressing some forms of *temporal/spatial reasoning*.
Recently, symbolic learning techniques such as Decision Trees, Random Forests and Rule-Based models have been extended to the use of Modal Logics of time and space. *Modal Decision Trees* and *Modal Random Forests* have been applied to classification tasks, showing statistical performances that are often comparable to those of functional methods (e.g., neural networks), while providing, at the same time, highly-interpretable classification models. Examples of these tasks are COVID-19 diagnosis from cough/breath audio [[1]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4102488), [[2]](https://drops.dagstuhl.de/opus/volltexte/2021/14783/pdf/LIPIcs-TIME-2021-7.pdf), land cover classification from aereal images [[3]](https://arxiv.org/abs/2109.08325), EEG-related tasks [[4]](https://link.springer.com/chapter/10.1007/978-3-031-06242-1_53), and gas turbine trip prediction.
This technology also offers a natural extension for *multimodal* learning [[5]](http://ceur-ws.org/Vol-2987/paper7.pdf).
## Credits
*ModalDecisionTrees.jl* lives within the [*Sole.jl*](https://github.com/aclai-lab/Sole.jl) framework for *symbolic machine learning*.
The package is developed by the [ACLAI Lab](https://aclai.unife.it/en/) @ University of Ferrara.
Thanks to Ben Sadeghi ([@bensadeghi](https://github.com/bensadeghi/)), author of [DecisionTree.jl](https://github.com/JuliaAI/DecisionTree.jl),
which inspired the construction of this package.