Merge pull request #91 from JuliaTrustworthyAI/mljflux-update

Mljflux update

Author: pat-alt

Project.toml

@@ -1,7 +1,7 @@
 name = "ConformalPrediction"
 uuid = "98bfc277-1877-43dc-819b-a3e38c30242f"
 authors = ["Patrick Altmeyer"]
-version = "0.1.11"
+version = "0.1.12"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -29,7 +29,7 @@ ComputationalResources = "0.3"
 Flux = "0.13.16, 0.14"
 MLJBase = "0.20, 0.21"
 MLJEnsembles = "0.3.3"
-MLJFlux = "0.2.10"
+MLJFlux = "0.2.10, 0.3"
 MLJModelInterface = "1"
 MLUtils = "0.4.2"
 NaturalSort = "1"

README.md

@@ -8,9 +8,9 @@
 
 ## 🏃 Quick Tour
 
-> First time here? Take a quick interactive [tour](https://binder.plutojl.org/v0.19.12/open?url=https%253A%252F%252Fraw.githubusercontent.com%252Fpat-alt%252FConformalPrediction.jl%252Fmain%252Fdocs%252Fpluto%252Fintro.jl) to see what this package can do: [![Binder](https://mybinder.org/badge_logo.svg)](https://binder.plutojl.org/v0.19.12/open?url=https%253A%252F%252Fraw.githubusercontent.com%252Fpat-alt%252FConformalPrediction.jl%252Fmain%252Fdocs%252Fpluto%252Fintro.jl)
+> First time here? Take a quick interactive [tour](https://juliahub.com/ui/Notebooks/juliahub/Tutorials/ConformalPrediction.jl) to see what this package can do right on [JuliaHub](https://juliahub.com/ui/Notebooks/juliahub/Tutorials/ConformalPrediction.jl) (To run the notebook, hit login and then edit).
 
-The button takes you to a [`Pluto.jl`](https://github.com/fonsp/Pluto.jl) 🎈 notebook hosted on [binder](https://mybinder.org/). In my own experience, this may take some time to load, certainly long enough to get yourself a hot beverage ☕. Alternatively, you can run the notebook locally or skip the tour for now and read on below.
+This [`Pluto.jl`](https://github.com/fonsp/Pluto.jl) 🎈 notebook won the 2nd Price in the [JuliaCon 2023 Notebook Competition](https://info.juliahub.com/pluto-notebook-winner-23).
 
 ### Local Tour
 
@@ -111,11 +111,11 @@ ŷ[1:show_first]
 ```
 
     5-element Vector{Tuple{Float64, Float64}}:
-     (0.0458889297242715, 1.9182762960257687)
-     (-1.9174452847238976, -0.04505791842240037)
-     (-1.2544275358451678, 0.6179598304563294)
-     (-0.2818835218505735, 1.5905038444509236)
-     (0.01299565032151917, 1.8853830166230163)
+     (-0.04087262272113379, 1.8635644669554758)
+     (0.04647464096907805, 1.9509117306456876)
+     (-0.24248802236397216, 1.6619490673126376)
+     (-0.07841928163933476, 1.8260178080372749)
+     (-0.02268628324126465, 1.881750806435345)
 
 For simple models like this one, we can call a custom `Plots` recipe on our instance, fit result and data to generate the chart below:
 
@@ -139,20 +139,20 @@ println("SSC: $(round(_eval.measurement[2], digits=3))")
 ```
 
     PerformanceEvaluation object with these fields:
-      measure, operation, measurement, per_fold,
+      model, measure, operation, measurement, per_fold,
       per_observation, fitted_params_per_fold,
-      report_per_fold, train_test_rows
+      report_per_fold, train_test_rows, resampling, repeats
     Extract:
     ┌──────────────────────────────────────────────┬───────────┬─────────────┬──────
     │ measure                                      │ operation │ measurement │ 1.9 ⋯
     ├──────────────────────────────────────────────┼───────────┼─────────────┼──────
-    │ ConformalPrediction.emp_coverage             │ predict   │ 0.948       │ 0.0 ⋯
-    │ ConformalPrediction.size_stratified_coverage │ predict   │ 0.948       │ 0.0 ⋯
+    │ ConformalPrediction.emp_coverage             │ predict   │ 0.953       │ 0.0 ⋯
+    │ ConformalPrediction.size_stratified_coverage │ predict   │ 0.953       │ 0.0 ⋯
     └──────────────────────────────────────────────┴───────────┴─────────────┴──────
                                                                    2 columns omitted
 
-    Empirical coverage: 0.948
-    SSC: 0.948
+    Empirical coverage: 0.953
+    SSC: 0.953
 
 ## 📚 Read on
 
@@ -240,14 +240,14 @@ Contributions are welcome! A good place to start is the [list](https://github.co
 
 ## 🙏 Thanks
 
-To build this package I have read and re-read both Angelopoulos and Bates (2021) and Barber et al. (2021). The Awesome Conformal Prediction [repository](https://github.com/valeman/awesome-conformal-prediction) (Manokhin, n.d.) has also been a fantastic place to get started. Thanks also to [@aangelopoulos](https://github.com/aangelopoulos), [@valeman](https://github.com/valeman) and others for actively contributing to discussions on here. Quite a few people have also recently started using and contributing to the package for which I am very grateful. Finally, many thanks to Anthony Blaom ([@ablaom](https://github.com/ablaom)) for many helpful discussions about how to interface this package to `MLJ.jl`.
+To build this package I have read and re-read both Angelopoulos and Bates (2021) and Barber et al. (2021). The Awesome Conformal Prediction [repository](https://github.com/valeman/awesome-conformal-prediction) (Manokhin 2022) has also been a fantastic place to get started. Thanks also to [@aangelopoulos](https://github.com/aangelopoulos), [@valeman](https://github.com/valeman) and others for actively contributing to discussions on here. Quite a few people have also recently started using and contributing to the package for which I am very grateful. Finally, many thanks to Anthony Blaom ([@ablaom](https://github.com/ablaom)) for many helpful discussions about how to interface this package to `MLJ.jl`.
 
 ## 🎓 References
 
-Angelopoulos, Anastasios N., and Stephen Bates. 2021. “A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification.” <https://arxiv.org/abs/2107.07511>.
+Angelopoulos, Anastasios N, and Stephen Bates. 2021. “A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification.” *arXiv Preprint arXiv:2107.07511*.
 
 Barber, Rina Foygel, Emmanuel J. Candès, Aaditya Ramdas, and Ryan J. Tibshirani. 2021. “Predictive Inference with the Jackknife+.” *The Annals of Statistics* 49 (1): 486–507. <https://doi.org/10.1214/20-AOS1965>.
 
 Blaom, Anthony D., Franz Kiraly, Thibaut Lienart, Yiannis Simillides, Diego Arenas, and Sebastian J. Vollmer. 2020. “MLJ: A Julia Package for Composable Machine Learning.” *Journal of Open Source Software* 5 (55): 2704. <https://doi.org/10.21105/joss.02704>.
 
-Manokhin, Valery. n.d. “Awesome Conformal Prediction.”
+Manokhin, Valery. 2022. “Awesome Conformal Prediction.” Zenodo. <https://doi.org/10.5281/zenodo.6467205>.