Develop

Project.toml

@@ -8,6 +8,7 @@ MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+Term = "22787eb5-b846-44ae-b979-8e399b8463ab"
 
 [compat]
 MLJ = "0.18"

_freeze/docs/src/classification/execute-results/md.json

@@ -0,0 +1,10 @@
+{
+  "hash": "a5e7b7c00cea9c148884a97a62ddf49b",
+  "result": {
+    "markdown": "\n\n::: {.cell execution_count=2}\n``` {.julia .cell-code}\nusing MLJ\nX, y = MLJ.make_blobs(1000, 2; centers=3, cluster_std=1.0)\ntrain, test = partition(eachindex(y), 0.4, 0.4, shuffle=true)\n```\n:::\n\n\n::: {.cell execution_count=3}\n``` {.julia .cell-code}\nEvoTreeClassifier = @load EvoTreeClassifier pkg=EvoTrees\nmodel = EvoTreeClassifier() \n```\n:::\n\n\n::: {.cell execution_count=4}\n``` {.julia .cell-code}\nusing ConformalPrediction\nconf_model = conformal_model(model)\nmach = machine(conf_model, X, y)\nfit!(mach, rows=train)\n```\n:::\n\n\n::: {.cell execution_count=5}\n``` {.julia .cell-code}\nrows = rand(test, 10)\nXtest = selectrows(X, rows)\nytest = y[rows]\npredict(mach, Xtest)\n```\n\n::: {.cell-output .cell-output-display execution_count=6}\n```\n╭───────────────────────────────────────────────────────────────────╮\n│                                                                   │\n│       (1)   UnivariateFinite{Multiclass {#90CAF9}3} (1=>0.82{/#90CAF9})     │\n│       (2)   UnivariateFinite{Multiclass {#90CAF9}3} (3=>0.82{/#90CAF9})     │\n│       (3)   UnivariateFinite{Multiclass {#90CAF9}3} (1=>0.82{/#90CAF9})     │\n│       (4)   UnivariateFinite{Multiclass {#90CAF9}3} (1=>0.82{/#90CAF9})     │\n│       (5)   UnivariateFinite{Multiclass {#90CAF9}3} (1=>0.82{/#90CAF9})     │\n│       (6)   UnivariateFinite{Multiclass {#90CAF9}3} (3=>0.82{/#90CAF9})     │\n│       (7)   UnivariateFinite{Multiclass {#90CAF9}3} (3=>0.82{/#90CAF9})     │\n│       (8)   UnivariateFinite{Multiclass {#90CAF9}3} (2=>0.82{/#90CAF9})     │\n│       (9)   UnivariateFinite{Multiclass {#90CAF9}3} (1=>0.82{/#90CAF9})     │\n│      (10)   UnivariateFinite{Multiclass {#90CAF9}3} (3=>0.82{/#90CAF9})     │\n│                                                                   │\n│                                                                   │\n╰────────────────────────────────────────────────────── 10 items ───╯\n```\n:::\n:::\n\n\n",
+    "supporting": [
+      "classification_files"
+    ],
+    "filters": []
+  }
+}

_freeze/docs/src/index/execute-results/md.json

@@ -1,7 +1,7 @@
 {
   "hash": "c56dcfed5fce5fece3f8dd90b08af0bd",
   "result": {
-    "markdown": "```@meta\nCurrentModule = ConformalPrediction\n```\n\n# ConformalPrediction\n\nDocumentation for [ConformalPrediction.jl](https://github.com/pat-alt/ConformalPrediction.jl).\n\n```@meta\nCurrentModule = ConformalPrediction\n```\n\n\n\n`ConformalPrediction.jl` is a package for Uncertainty Quantification (UQ) through Conformal Prediction (CP) in Julia. It is designed to work with supervised models trained in [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/). Conformal Prediction is distribution-free, easy-to-understand, easy-to-use and model-agnostic. \n\n## Installation 🚩\n\nYou can install the first stable release from the general registry:\n\n```julia\nusing Pkg\nPkg.add(\"ConformalPrediction\")\n```\n\nThe development version can be installed as follows:\n\n```julia\nusing Pkg\nPkg.add(url=\"https://github.com/pat-alt/ConformalPrediction.jl\")\n```\n\n## Status 🔁\n\nThis package is in its very early stages of development and therefore still subject to changes to the core architecture. The following approaches have been implemented in the development version:\n\n**Regression**:\n\n- Inductive \n- Naive Transductive \n- Jackknife \n- Jackknife+ \n- Jackknife-minmax\n- CV+\n- CV-minmax\n\n**Classification**:\n\n- Inductive (LABEL [@sadinle2019least])\n- Adaptive Inductive\n\nI have only tested it for a few of the supervised models offered by [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/).\n\n## Usage Example 🔍\n\nTo illustrate the intended use of the package, let's have a quick look at a simple regression problem. Using [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) we first generate some synthetic data and then determine indices for our training, calibration and test data:\n\n::: {.cell execution_count=2}\n``` {.julia .cell-code}\nusing MLJ\nX, y = MLJ.make_regression(1000, 2)\ntrain, test = partition(eachindex(y), 0.4, 0.4)\n```\n:::\n\n\nWe then import a decision tree ([DecisionTree](https://github.com/Evovest/DecisionTree.jl)) following the standard [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) procedure.\n\n::: {.cell execution_count=3}\n``` {.julia .cell-code}\nDecisionTreeRegressor = @load DecisionTreeRegressor pkg=DecisionTree\nmodel = DecisionTreeRegressor() \n```\n:::\n\n\nTo turn our conventional model into a conformal model, we just need to declare it as such by using `conformal_model` wrapper function. The generated conformal model instance can wrapped in data to create a *machine*. Finally, we proceed by fitting the machine on training data using the generic `fit!` method:\n\n::: {.cell execution_count=4}\n``` {.julia .cell-code}\nusing ConformalPrediction\nconf_model = conformal_model(model)\nmach = machine(conf_model, X, y)\nfit!(mach, rows=train)\n```\n:::\n\n\nPredictions can then be computed using the generic `predict` method. The code below produces predictions for the first `n` samples. Each tuple contains the lower and upper bound for the prediction interval.\n\n::: {.cell execution_count=5}\n``` {.julia .cell-code}\nn = 10\nXtest = selectrows(X, first(test,n))\nytest = y[first(test,n)]\npredict(mach, Xtest)\n```\n\n::: {.cell-output .cell-output-display execution_count=6}\n```\n╭──────────────────────────────────────────────────────────────────╮\n│                                                                  │\n│       (1)   ([-0.16035036780321532], [1.4939904924997824])       │\n│       (2)   ([-1.086589388667894], [0.5677514716351038])         │\n│       (3)   ([-1.086589388667894], [0.5677514716351038])         │\n│       (4)   ([-1.6661164684544767], [-0.011775608151479156])     │\n│       (5)   ([-3.0116018507211617], [-1.3572609904181638])       │\n│       (6)   ([0.5337083913933376], [2.1880492516963352])         │\n│       (7)   ([-1.2219266921060266], [0.43241416819697115])       │\n│       (8)   ([-1.6867950029289869], [-0.032454142625989335])     │\n│       (9)   ([-2.0599181285783263], [-0.4055772682753287])       │\n│      (10)   ([-0.06499897951385392], [1.5893418807891437])       │\n│                                                                  │\n│                                                                  │\n╰───────────────────────────────────────────────────── 10 items ───╯\n```\n:::\n:::\n\n\n## Contribute 🛠\n\nContributions are welcome! Please follow the [SciML ColPrac guide](https://github.com/SciML/ColPrac).\n\n## References 🎓\n\n",
+    "markdown": "```@meta\nCurrentModule = ConformalPrediction\n```\n\n# ConformalPrediction\n\nDocumentation for [ConformalPrediction.jl](https://github.com/pat-alt/ConformalPrediction.jl).\n\n\n\n`ConformalPrediction.jl` is a package for Uncertainty Quantification (UQ) through Conformal Prediction (CP) in Julia. It is designed to work with supervised models trained in [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/). Conformal Prediction is distribution-free, easy-to-understand, easy-to-use and model-agnostic. \n\n## Installation 🚩\n\nYou can install the first stable release from the general registry:\n\n```{.julia}\nusing Pkg\nPkg.add(\"ConformalPrediction\")\n```\n\nThe development version can be installed as follows:\n\n```{.julia}\nusing Pkg\nPkg.add(url=\"https://github.com/pat-alt/ConformalPrediction.jl\")\n```\n\n## Status 🔁\n\nThis package is in its very early stages of development and therefore still subject to changes to the core architecture. The following approaches have been implemented in the development version:\n\n**Regression**:\n\n- Inductive \n- Naive Transductive \n- Jackknife \n- Jackknife+ \n- Jackknife-minmax\n- CV+\n- CV-minmax\n\n**Classification**:\n\n- Inductive (LABEL [@sadinle2019least])\n- Adaptive Inductive\n\nI have only tested it for a few of the supervised models offered by [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/).\n\n## Usage Example 🔍\n\nTo illustrate the intended use of the package, let's have a quick look at a simple regression problem. Using [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) we first generate some synthetic data and then determine indices for our training, calibration and test data:\n\n::: {.cell execution_count=2}\n``` {.julia .cell-code}\nusing MLJ\nX, y = MLJ.make_regression(1000, 2)\ntrain, test = partition(eachindex(y), 0.4, 0.4)\n```\n:::\n\n\nWe then import a decision tree ([`EvoTrees.jl`](https://github.com/Evovest/EvoTrees.jl)) following the standard [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) procedure.\n\n::: {.cell execution_count=3}\n``` {.julia .cell-code}\nEvoTreeRegressor = @load EvoTreeRegressor pkg=EvoTrees\nmodel = EvoTreeRegressor() \n```\n:::\n\n\nTo turn our conventional model into a conformal model, we just need to declare it as such by using `conformal_model` wrapper function. The generated conformal model instance can wrapped in data to create a *machine*. Finally, we proceed by fitting the machine on training data using the generic `fit!` method:\n\n::: {.cell execution_count=4}\n``` {.julia .cell-code}\nusing ConformalPrediction\nconf_model = conformal_model(model)\nmach = machine(conf_model, X, y)\nfit!(mach, rows=train)\n```\n:::\n\n\nPredictions can then be computed using the generic `predict` method. The code below produces predictions for the first `n` samples. Each tuple contains the lower and upper bound for the prediction interval.\n\n::: {.cell execution_count=5}\n``` {.julia .cell-code}\nn = 10\nXtest = selectrows(X, first(test,n))\nytest = y[first(test,n)]\npredict(mach, Xtest)\n```\n\n::: {.cell-output .cell-output-display execution_count=6}\n```\n╭────────────────────────────────────────────────────────────────╮\n│                                                                │\n│       (1)   ([-5.4338268970328265], [0.25968688121283146])     │\n│       (2)   ([-3.327102611742035], [2.3664111665036227])       │\n│       (3)   ([-6.487880423821674], [-0.7943666455760168])      │\n│       (4)   ([1.5074870726446568], [7.201000850890314])        │\n│       (5)   ([-6.668012325746515], [-0.9744985475008576])      │\n│       (6)   ([-2.36920384417906], [3.3243099340665974])        │\n│       (7)   ([-0.4783861002145251], [5.215127678031132])       │\n│       (8)   ([-5.554310900530298], [0.1392028777153591])       │\n│       (9)   ([-1.4607932119178935], [4.232720566327764])       │\n│      (10)   ([-5.190158387746367], [0.5033553904992907])       │\n│                                                                │\n│                                                                │\n╰─────────────────────────────────────────────────── 10 items ───╯\n```\n:::\n:::\n\n\n## Contribute 🛠\n\nContributions are welcome! Please follow the [SciML ColPrac guide](https://github.com/SciML/ColPrac).\n\n## References 🎓\n\n",
     "supporting": [
       "index_files"
     ],

_freeze/docs/src/intro/execute-results/md.json

@@ -0,0 +1,10 @@
+{
+  "hash": "1b0ee553524705afa5795d1e05898476",
+  "result": {
+    "markdown": "\n\n`ConformalPrediction.jl` is a package for Uncertainty Quantification (UQ) through Conformal Prediction (CP) in Julia. It is designed to work with supervised models trained in [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/). Conformal Prediction is distribution-free, easy-to-understand, easy-to-use and model-agnostic. \n\n## Installation 🚩\n\nYou can install the first stable release from the general registry:\n\n```{.julia}\nusing Pkg\nPkg.add(\"ConformalPrediction\")\n```\n\nThe development version can be installed as follows:\n\n```{.julia}\nusing Pkg\nPkg.add(url=\"https://github.com/pat-alt/ConformalPrediction.jl\")\n```\n\n## Status 🔁\n\nThis package is in its very early stages of development and therefore still subject to changes to the core architecture. The following approaches have been implemented in the development version:\n\n**Regression**:\n\n- Inductive \n- Naive Transductive \n- Jackknife \n- Jackknife+ \n- Jackknife-minmax\n- CV+\n- CV-minmax\n\n**Classification**:\n\n- Inductive (LABEL [@sadinle2019least])\n- Adaptive Inductive\n\nI have only tested it for a few of the supervised models offered by [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/).\n\n## Usage Example 🔍\n\nTo illustrate the intended use of the package, let's have a quick look at a simple regression problem. Using [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) we first generate some synthetic data and then determine indices for our training, calibration and test data:\n\n::: {.cell execution_count=2}\n``` {.julia .cell-code}\nusing MLJ\nX, y = MLJ.make_regression(1000, 2)\ntrain, test = partition(eachindex(y), 0.4, 0.4)\n```\n:::\n\n\nWe then import a decision tree ([`EvoTrees.jl`](https://github.com/Evovest/EvoTrees.jl)) following the standard [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) procedure.\n\n::: {.cell execution_count=3}\n``` {.julia .cell-code}\nEvoTreeRegressor = @load EvoTreeRegressor pkg=EvoTrees\nmodel = EvoTreeRegressor() \n```\n:::\n\n\nTo turn our conventional model into a conformal model, we just need to declare it as such by using `conformal_model` wrapper function. The generated conformal model instance can wrapped in data to create a *machine*. Finally, we proceed by fitting the machine on training data using the generic `fit!` method:\n\n::: {.cell execution_count=4}\n``` {.julia .cell-code}\nusing ConformalPrediction\nconf_model = conformal_model(model)\nmach = machine(conf_model, X, y)\nfit!(mach, rows=train)\n```\n:::\n\n\nPredictions can then be computed using the generic `predict` method. The code below produces predictions for the first `n` samples. Each tuple contains the lower and upper bound for the prediction interval.\n\n::: {.cell execution_count=5}\n``` {.julia .cell-code}\nn = 10\nXtest = selectrows(X, first(test,n))\nytest = y[first(test,n)]\npredict(mach, Xtest)\n```\n\n::: {.cell-output .cell-output-display execution_count=6}\n```\n╭─────────────────────────────────────────────────────────────────╮\n│                                                                 │\n│       (1)   ([-0.20063113789390163], [1.323655530145934])       │\n│       (2)   ([-0.061147489871723804], [1.4631391781681118])     │\n│       (3)   ([-1.4486105066363675], [0.07567616140346822])      │\n│       (4)   ([-0.7160881365817455], [0.8081985314580902])       │\n│       (5)   ([-1.7173644161988695], [-0.19307774815903367])     │\n│       (6)   ([-1.2158809697881832], [0.3084056982516525])       │\n│       (7)   ([-1.7173644161988695], [-0.19307774815903367])     │\n│       (8)   ([0.26510754559144056], [1.7893942136312764])       │\n│       (9)   ([-0.8716996456392521], [0.6525870224005836])       │\n│      (10)   ([0.43084861624955606], [1.9551352842893919])       │\n│                                                                 │\n│                                                                 │\n╰──────────────────────────────────────────────────── 10 items ───╯\n```\n:::\n:::\n\n\n## Contribute 🛠\n\nContributions are welcome! Please follow the [SciML ColPrac guide](https://github.com/SciML/ColPrac).\n\n## References 🎓\n\n",
+    "supporting": [
+      "intro_files"
+    ],
+    "filters": []
+  }
+}

_quarto.yml

@@ -12,5 +12,5 @@ execute:
   echo: true
   eval: true
   output: false
-
+jupyter: julia-1.8