tests locally working

Author: PasoStudio73

Project.toml

@@ -22,17 +22,14 @@ SoleModels = "4249d9c7-3290-4ddd-961c-e1d3ec2467f8"
 SolePostHoc = "cf1aa0c3-12c9-4ebe-9bdc-bd6c2ca79b72"
 XGBoost = "009559a3-9522-5dbb-924b-0b6ed2b22bb9"
 
-[sources]
-SolePostHoc = {rev = "devPaso", url = "https://github.com/aclai-lab/SolePostHoc.jl"}
-
 [compat]
-CategoricalArrays = "1"
+CategoricalArrays = "0.10 - 1"
 DataFrames = "1"
 DataTreatments = "0.2"
 DimensionalData = "0.29"
 Imbalance = "0.1"
 JLD2 = "0.6"
-MLJ = "0.22"
+MLJ = "0.20 - 0.22"
 MLJDecisionTreeInterface = "0.4"
 MLJParticleSwarmOptimization = "0.1"
 MLJXGBoostInterface = "0.3"

TODO.md

@@ -66,13 +66,6 @@ rivederne la propagazione: ad oggi non è ancora formalmente testata
 # ---------------------------------------------------------------------------- #
 Bisogna pensare anche ai modelli non supervisionati: apply(m, X)
 
-# ---------------------------------------------------------------------------- #
-#                                   timeout                                    #
-# ---------------------------------------------------------------------------- #
-l'algoritmo è pronto, sia il mio che quello mio di perry
-il più performante vince
-ma lo sviluppo sarà su un altra repo.
-
 # ---------------------------------------------------------------------------- #
 #                                multi thread                                  #
 # ---------------------------------------------------------------------------- #

docs/src/index.md

@@ -15,8 +15,8 @@ Key features:
 * Works also on time-series based datasets using modal logic.
 * View rules and their metrics.
 * Inspect logical formulas and their evaluation.
-* View modal rule associations.
-* Integrated GUI.
+<!-- * View modal rule associations. -->
+<!-- * Integrated GUI. -->
 
 ## Installation
 ```julia
@@ -78,7 +78,7 @@ SoleXplorer extensively uses the following packages:
 * [`SoleLogics`](https://github.com/aclai-lab/SoleLogics.jl): modal and temporal logic systems.
 * [`MLJ`](https://github.com/JuliaAI/MLJ.jl): provides all machine learning frameworks.
 * [`SolePostHoc`](https://github.com/aclai-lab/SolePostHoc.jl): for rule extraction.
-* [`ModalAssociationRules`](https://github.com/aclai-lab/ModalAssociationRules.jl): for mining association rules.
+<!-- * [`ModalAssociationRules`](https://github.com/aclai-lab/ModalAssociationRules.jl): for mining association rules. -->
 
 ## About
 The package is developed by the [ACLAI Lab](https://aclai.unife.it/en/) @ University of Ferrara.

docs/src/symbolic_analysis.md

@@ -20,7 +20,7 @@ ModelSet
 dsetup(m::ModelSet)
 solemodels(m::ModelSet)
 rules(m::ModelSet)
-associations(m::ModelSet)
+<!-- associations(m::ModelSet) -->
 performance(m::ModelSet)
 ```
 

docs/src/treatement.md

@@ -18,7 +18,7 @@ With multidimensional datasets there are two possible types of work:
 treatment(X::AbstractDataFrame, treat::Symbol)
 ```
 
-Windowing strategies availables for reduce/aggregation time-series datasets.
+<!-- Windowing strategies availables for reduce/aggregation time-series datasets.
 
 ```@docs
 MovingWindow
@@ -27,21 +27,21 @@ SplitWindow
 adaptivewindow
 AbstractWinFunction
 WinFunction
-```
+``` -->
 
 # [Featuresets](@id featuresets)
 
 ### Basic Statistics
 Standard statistical measures: `maximum`, `minimum`, `mean`, `median`, `std`, `cov`
 
-### Catch22 Features
+<!-- ### Catch22 Features
 Canonical time-series characteristics covering:
 - Distribution properties and extreme events
 - Linear and nonlinear autocorrelation structures  
 - Forecasting performance and scaling properties
-- Symbolic dynamics and transition patterns
+- Symbolic dynamics and transition patterns -->
 
-### Predefined Feature Sets
+<!-- ### Predefined Feature Sets
 
 - [`base_set`](@ref): Minimal statistical features (4 features)
 - [`catch9`](@ref): Curated subset combining statistics + key Catch22 (9 features)  
@@ -52,20 +52,20 @@ Canonical time-series characteristics covering:
 
 The Catch22 features are based on the Canonical Time-series Characteristics:
 - **Repository**: https://github.com/DynamicsAndNeuralSystems/catch22
-- **Paper**: Lubba, C.H., Sethi, S.S., Knaute, P. et al. "catch22: CAnonical Time-series CHaracteristics." *Data Min Knowl Disc* 33, 1821–1852 (2019). https://doi.org/10.1007/s10618-019-00647-x
+- **Paper**: Lubba, C.H., Sethi, S.S., Knaute, P. et al. "catch22: CAnonical Time-series CHaracteristics." *Data Min Knowl Disc* 33, 1821–1852 (2019). https://doi.org/10.1007/s10618-019-00647-x -->
 
-```@docs
+<!-- ```@docs
 base_set
 catch9
 catch22_set
 complete_set
-```
+``` -->
 
 See also: [`treatment`](@ref), [`setup_dataset`](@ref)
 
 ## All Catch22 Features
 
-```@docs
+<!-- ```@docs
 mode_5
 mode_10  
 embedding_dist
@@ -88,4 +88,4 @@ low_freq_power
 centroid_freq
 transition_variance
 periodicity
-```
+``` -->

src/extractrules.jl

@@ -31,12 +31,13 @@ function extractrules(
         X_test, y_test = get_X(ds, :test)[i], get_y(ds, :test)[i]
         RuleExtraction.modalextractrules(
             extractor,
+            model,
             scalarlogiset(X_test; allow_propositional = true),
-            y_test,
-            model
+            y_test
         )
     end
 end
+# TODO swap model and y_test as soon as we introduce new algo in PostHoc
 
 # ---------------------------------------------------------------------------- #
 #                              LumenRuleExtractor                              #
@@ -108,7 +109,8 @@ function extractrules(
     solem     :: Vector{AbstractModel}
 )::Vector{DecisionSet}
     map(enumerate(solem)) do (i, model)
-        X_test = get_X(ds, :test)[i]
+        X_test = DataFrame(get_X(ds, :test)[i])
         RuleExtraction.modalextractrules(extractor, model, X_test; params...)
     end
 end
+# TODO open a PR to let Trepan accepts AbstractDataFrame

test/robustness.jl

@@ -3,7 +3,7 @@ using SoleXplorer
 const SX = SoleXplorer
 
 using MLJ, DataFrames, Random
-using JLD2
+using CategoricalArrays, JLD2
 
 Xc, yc = @load_iris
 Xc = DataFrame(Xc)