39 refactor mljflux interface

.github/workflows/CI.yml

@@ -21,8 +21,6 @@ jobs:
       matrix:
         version:
           - '1.7'
-          - '1.8'
-          - '1.9'
           - '1.10'
         os:
           - ubuntu-latest
@@ -33,9 +31,6 @@ jobs:
           - os: windows-latest
             version: '1.7'
             arch: x64
-          - os: windows-latest
-            version: '1.8'
-            arch: x64
           - os: windows-latest
             version: '1'
             arch: x64
@@ -45,9 +40,6 @@ jobs:
           - os: macOS-latest
             version: '1.7'
             arch: x64
-          - os: macOS-latest
-            version: '1.8'
-            arch: x64
     steps:
       - uses: actions/checkout@v2
       - uses: julia-actions/setup-julia@v1
@@ -60,9 +52,10 @@ jobs:
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
       - uses: julia-actions/julia-processcoverage@v1
-      - uses: codecov/codecov-action@v2
-        with:
-          files: lcov.info
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v4
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
   docs:
     name: Documentation
     runs-on: ubuntu-latest

.github/workflows/FormatCheck.yml

@@ -25,10 +25,4 @@ jobs:
         run: |
           using JuliaFormatter
           format("."; verbose=true)
-        shell: julia --color=yes {0}
-      - name: Suggest formatting changes
-        uses: reviewdog/action-suggester@v1
-        if: github.event_name == 'pull_request'
-        with:
-          tool_name: JuliaFormatter
-          fail_on_error: true
+        shell: julia --color=yes {0}

CHANGELOG.md

@@ -6,6 +6,46 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 
 *Note*: We try to adhere to these practices as of version [v0.2.1].
 
+## Version [0.3.0] - 2024-06-8
+
+### Changed
+
+- Updated codecov workflow in CI.yml. [#39]
+- fixed test functions [#39]
+- adapted the LaplaceClassification and the LaplaceRegression struct to use the new @mlj_model macro from MLJBase.[#39]
+- Changed the fit! method arguments. [#39]
+- Changed the predict functions for both LaplaceClassification and  LaplaceRegression.[#39]
+
+### Removed
+
+- Removed the shape, build and clean! functions.[#39]
+- Removed Review dog for code format suggestions. [#39]
+
+## Version [0.2.3] - 2024-05-31
+
+### Changed
+
+- Removed the link_approx parameter in LaplaceRegression since it is not required.
+- Changed MMI.clean! to check the value of link_approx only in the case likelihood is set to `:classification`
+- Now the likelihood type in LaplaceClassification and LaplaceRegression is automatically set by the inner constructor. The user is not required to provide it as a parameter anymore.
+
+
+
+
+## Version [0.2.2] - 2024-05-30
+
+### Changed
+
+- Unified duplicated function MMI.clean!: previously MMI.clean! consisted of two separate functions for handling :classification and :regression types respectively. Now, a single MMI.clean! function handles both cases efficiently.[#39]
+- Split LaplaceApproximation struct in two different structs:LaplaceClassification and LaplaceRegression  [#39] 
+- Unified the MLJFlux.shape and the MLJFlux.build functions to handle both :classification and :regression tasks. In particular, shape now handles multi-output regression cases too [[#39](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl/issues/39)]
+- Changed model metadata for LaplaceClassification and LaplaceRegression
+
+### Added
+ Added Distributions to LaplaceRedux dependency ( needed for MMI.predict(model::LaplaceRegression, fitresult, Xnew) )
+
+
+
 ## Version [0.2.1] - 2024-05-29
 
 ### Changed

Project.toml

@@ -7,11 +7,14 @@ version = "0.2.1"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845"
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
+Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
@@ -24,19 +27,22 @@ Aqua = "0.8"
 ChainRulesCore = "1.23.0"
 Compat = "4.7.0"
 ComputationalResources = "0.3.2"
+Distributions = "0.25.109"
 Flux = "0.12, 0.13, 0.14"
-LinearAlgebra = "1.6, 1.7, 1.8, 1.9, 1.10"
-MLJFlux = "0.2.10, 0.3, 0.4"
+LinearAlgebra = "1.7, 1.10"
+MLJBase = "0, 1.4.0"
+MLJFlux = "0.2.10, 0.3, 0.4, 0.5.1"
 MLJModelInterface = "1.8.0"
 MLUtils = "0.4.3"
+Optimisers = "0.3.3"
 ProgressMeter = "1.7.2"
-Random = "1.6, 1.7, 1.8, 1.9, 1.10"
+Random = "1.7, 1.10"
 Statistics = "1"
 Tables = "1.10.1"
-Test = "1.6, 1.7, 1.8, 1.9, 1.10"
+Test = "1.7, 1.10"
 Tullio = "0.3.5"
 Zygote = "0.6"
-julia = "1.6, 1.7, 1.8, 1.9, 1.10"
+julia = "1.7, 1.10"
 
 [extras]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"

dev/notebooks/mlj-interfacing/mlj.ipynb

@@ -74,7 +74,7 @@
       "│ \n",
       "│ In general, data in `machine(model, data...)` is expected to satisfy\n",
       "│ \n",
-      "│     scitype(data) <: MLJ.fit_data_scitype(model)\n",
+      "│     scitype(data) <: MLJBase.fit_data_scitype(model)\n",
       "│ \n",
       "│ In the present case:\n",
       "│ \n",
@@ -194,7 +194,7 @@
     }
    ],
    "source": [
-    "MLJ.fit!(mach)"
+    "MLJBase.fit!(mach)"
    ]
   },
   {
@@ -214,7 +214,7 @@
     }
    ],
    "source": [
-    "MLJ.predict(mach, MLJBase.table(rand(Float32, 1, 4)))"
+    "MLJBase.predict(mach, MLJBase.table(rand(Float32, 1, 4)))"
    ]
   },
   {
@@ -323,7 +323,7 @@
     }
    ],
    "source": [
-    "MLJ.predict(mach, X)"
+    "MLJBase.predict(mach, X)"
    ]
   },
   {