Merge pull request #577 from JuliaAI/update-registry-2024-11-16

Update registry: Add LaplaceRedux models

Author: ablaom

.github/codecov.yml

@@ -2,4 +2,8 @@ coverage:
   status:
     project:
       default:
-        threshold: 0.5%
+        threshold: 0.5%
+        removed_code_behavior: fully_covered_patch
+    patch:
+      default:
+        target: 80

.github/workflows/ci.yml

@@ -17,6 +17,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
+          - '1.6'
           - '1.10'
           - '1'
         os:
@@ -42,6 +43,8 @@ jobs:
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
       - uses: julia-actions/julia-processcoverage@v1
-      - uses: codecov/codecov-action@v3
+      - uses: codecov/codecov-action@v4
         with:
-          file: lcov.info
+          token: ${{ secrets.CODECOV_TOKEN }}
+          fail_ci_if_error: false
+          verbose: true 

Project.toml

@@ -49,7 +49,7 @@ StatisticalTraits = "3"
 Statistics = "<0.0.1, 1"
 StatsBase = "0.32,0.33, 0.34"
 Tables = "0.2,1.0"
-julia = "1.10"
+julia = "1.6"
 
 [extras]
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"

README.md

@@ -1,6 +1,7 @@
 # MLJModels
 
 [![Build Status](https://github.com/JuliaAI/MLJModels.jl/workflows/CI/badge.svg)](https://github.com/JuliaAI/MLJModels.jl/actions)
+[![codecov](https://codecov.io/gh/JuliaAI/MLJModels.jl/graph/badge.svg?token=KgarnnCc0K)](https://codecov.io/gh/JuliaAI/MLJModels.jl)
 
 Repository of the "built-in" models available for use in the
 [MLJ](https://github.com/JuliaAI/MLJ.jl) MLJ machine

src/registry/Metadata.toml

@@ -17,13 +17,14 @@ MLJBalancing = ["BalancedBaggingClassifier", "BalancedModel"]
 Imbalance = ["RandomOversampler", "SMOTENC", "TomekUndersampler", "ClusterUndersampler", "SMOTE", "SMOTEN", "ROSE", "RandomUndersampler", "ENNUndersampler", "BorderlineSMOTE1", "RandomWalkOversampler"]
 MLJTuning = ["TunedModel"]
 FeatureSelection = ["FeatureSelector", "RecursiveFeatureElimination"]
-Clustering = ["HierarchicalClustering", "DBSCAN", "KMeans", "KMedoids", "AffinityPropagation"]
+Clustering = ["HierarchicalClustering", "DBSCAN", "KMeans", "AffinityPropagation", "KMedoids"]
 EvoLinear = ["EvoSplineRegressor", "EvoLinearRegressor"]
 MLJText = ["TfidfTransformer", "CountTransformer", "BM25Transformer"]
 LightGBM = ["LGBMClassifier", "LGBMRegressor"]
+LaplaceRedux = ["LaplaceClassifier", "LaplaceRegressor"]
 XGBoost = ["XGBoostCount", "XGBoostRegressor", "XGBoostClassifier"]
-SymbolicRegression = ["MultitargetSRRegressor", "SRRegressor"]
 EvoTrees = ["EvoTreeClassifier", "EvoTreeGaussian", "EvoTreeMLE", "EvoTreeRegressor", "EvoTreeCount"]
+SymbolicRegression = ["MultitargetSRRegressor", "SRRegressor"]
 MLJModels = ["ConstantClassifier", "Standardizer", "DeterministicConstantClassifier", "UnivariateTimeTypeToContinuous", "OneHotEncoder", "ContinuousEncoder", "UnivariateBoxCoxTransformer", "InteractionTransformer", "ConstantRegressor", "UnivariateDiscretizer", "BinaryThresholdPredictor", "FillImputer", "DeterministicConstantRegressor", "UnivariateStandardizer", "UnivariateFillImputer"]
 OneRule = ["OneRuleClassifier"]
 OutlierDetectionPython = ["MCDDetector", "COPODDetector", "HBOSDetector", "IForestDetector", "SOSDetector", "ABODDetector", "LOFDetector", "PCADetector", "INNEDetector", "OCSVMDetector", "ECODDetector", "SODDetector", "LODADetector", "KDEDetector", "CDDetector", "KNNDetector", "GMMDetector", "COFDetector", "CBLOFDetector", "LOCIDetector", "LMDDDetector", "RODDetector"]

src/registry/Models.toml

@@ -6,6 +6,7 @@ EvoTrees = "f6006082-12f8-11e9-0c9c-0d5d367ab1e5"
 FeatureSelection = "33837fe5-dbff-4c9e-8c2f-c5612fe2b8b6"
 Imbalance = "c709b415-507b-45b7-9a3d-1767c89fde68"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
+LaplaceRedux = "c52c1a26-f7c5-402b-80be-ba1e638ad478"
 LightGBM = "7acf609c-83a4-11e9-1ffb-b912bcd3b04a"
 MLJBalancing = "45f359ea-796d-4f51-95a5-deb1a414c586"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"

src/registry/Project.toml

@@ -4,7 +4,7 @@
 **Private method.**
 
 Return a dictionary of all subtypes of MLJ.Model, keyed on constructor. Where multiple
-types share a single constructor, there can only be one key, and which key appears is
+types share a single constructor, there can only be one value, and which value appears is
 ambiguous.
 
 Typically a model type and it's constructor have the same name, but for wrappers, such as