Merge pull request #1113 from DilumAluthge/dpa/repo-transfer

Updates now that MLJ.jl has been moved to the JuliaAI GitHub organization

Author: ablaom

examples/lightning_tour/lightning_tour.ipynb

@@ -1,7 +1,7 @@
 # # Lightning tour of MLJ
 
 # *For a more elementary introduction to MLJ, see [Getting
-# Started](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/).*
+# Started](https://juliaai.github.io/MLJ.jl/dev/getting_started/).*
 
 # **Note.** Be sure this file has not been separated from the
 # accompanying Project.toml and Manifest.toml files, which should not

examples/lightning_tour/lightning_tour.jl

@@ -12,7 +12,7 @@
    "metadata": {},
    "source": [
     "An application of the [MLJ\n",
-    "toolbox](https://alan-turing-institute.github.io/MLJ.jl/dev/) to the\n",
+    "toolbox](https://juliaai.github.io/MLJ.jl/dev/) to the\n",
     "Telco Customer Churn dataset, aimed at practicing data scientists\n",
     "new to MLJ (Machine Learning in Julia). This tutorial does not\n",
     "cover exploratory data analysis."
@@ -31,9 +31,9 @@
    "metadata": {},
    "source": [
     "For other MLJ learning resources see the [Learning\n",
-    "MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/learning_mlj/)\n",
+    "MLJ](https://juliaai.github.io/MLJ.jl/dev/learning_mlj/)\n",
     "section of the\n",
-    "[manual](https://alan-turing-institute.github.io/MLJ.jl/dev/)."
+    "[manual](https://juliaai.github.io/MLJ.jl/dev/)."
    ]
   },
   {
@@ -132,7 +132,7 @@
     "the notebook, package instantiation and pre-compilation may take a\n",
     "minute or so to complete. **This step will fail** if the [correct\n",
     "Manifest.toml and Project.toml\n",
-    "files](https://github.com/alan-turing-institute/MLJ.jl/tree/dev/examples/telco)\n",
+    "files](https://github.com/JuliaAI/MLJ.jl/tree/dev/examples/telco)\n",
     "are not in the same directory as this notebook."
    ]
   },
@@ -203,7 +203,7 @@
    "metadata": {},
    "source": [
     "This section is a condensed adaption of the [Getting Started\n",
-    "example](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/#Fit-and-predict)\n",
+    "example](https://juliaai.github.io/MLJ.jl/dev/getting_started/#Fit-and-predict)\n",
     "in the MLJ documentation."
    ]
   },
@@ -448,7 +448,7 @@
    "metadata": {},
    "source": [
     "A machine stores some other information enabling [warm\n",
-    "restart](https://alan-turing-institute.github.io/MLJ.jl/dev/machines/#Warm-restarts)\n",
+    "restart](https://juliaai.github.io/MLJ.jl/dev/machines/#Warm-restarts)\n",
     "for some models, but we won't go into that here. You are allowed to\n",
     "access and mutate the `model` parameter:"
    ]
@@ -1140,7 +1140,7 @@
    "metadata": {},
    "source": [
     "For tools helping us to identify suitable models, see the [Model\n",
-    "Search](https://alan-turing-institute.github.io/MLJ.jl/dev/model_search/#model_search)\n",
+    "Search](https://juliaai.github.io/MLJ.jl/dev/model_search/#model_search)\n",
     "section of the manual. We will build a gradient tree-boosting model,\n",
     "a popular first choice for structured data like we have here. Model\n",
     "code is contained in a third-party package called\n",
@@ -1379,7 +1379,7 @@
    "source": [
     "Note that the component models appear as hyper-parameters of\n",
     "`pipe`. Pipelines are an implementation of a more general [model\n",
-    "composition](https://alan-turing-institute.github.io/MLJ.jl/dev/composing_models/#Composing-Models)\n",
+    "composition](https://juliaai.github.io/MLJ.jl/dev/composing_models/#Composing-Models)\n",
     "interface provided by MLJ that advanced users may want to learn about."
    ]
   },
@@ -2152,7 +2152,7 @@
    "metadata": {},
    "source": [
     "We choose a `StratifiedCV` resampling strategy; the complete list of options is\n",
-    "[here](https://alan-turing-institute.github.io/MLJ.jl/dev/evaluating_model_performance/#Built-in-resampling-strategies)."
+    "[here](https://juliaai.github.io/MLJ.jl/dev/evaluating_model_performance/#Built-in-resampling-strategies)."
    ]
   },
   {
@@ -2393,7 +2393,7 @@
    "metadata": {},
    "source": [
     "First, we select appropriate controls from [this\n",
-    "list](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/#Controls-provided):"
+    "list](https://juliaai.github.io/MLJ.jl/dev/controlling_iterative_models/#Controls-provided):"
    ]
   },
   {
@@ -2559,7 +2559,7 @@
     "wanting to visualize the effect of changes to a *single*\n",
     "hyper-parameter (which could be an iteration parameter). See, for\n",
     "example, [this section of the\n",
-    "manual](https://alan-turing-institute.github.io/MLJ.jl/dev/learning_curves/)\n",
+    "manual](https://juliaai.github.io/MLJ.jl/dev/learning_curves/)\n",
     "or [this\n",
     "tutorial](https://github.com/ablaom/MLJTutorial.jl/blob/dev/notebooks/04_tuning/notebook.ipynb)."
    ]
@@ -2689,7 +2689,7 @@
    "metadata": {},
    "source": [
     "Next, we choose an optimization strategy from [this\n",
-    "list](https://alan-turing-institute.github.io/MLJ.jl/dev/tuning_models/#Tuning-Models):"
+    "list](https://juliaai.github.io/MLJ.jl/dev/tuning_models/#Tuning-Models):"
    ]
   },
   {

examples/telco/notebook.ipynb

@@ -1,7 +1,7 @@
 # # MLJ for Data Scientists in Two Hours
 
 # An application of the [MLJ
-# toolbox](https://alan-turing-institute.github.io/MLJ.jl/dev/) to the
+# toolbox](https://juliaai.github.io/MLJ.jl/dev/) to the
 # Telco Customer Churn dataset, aimed at practicing data scientists
 # new to MLJ (Machine Learning in Julia). This tutorial does not
 # cover exploratory data analysis.
@@ -10,9 +10,9 @@
 # deep-learning).
 
 # For other MLJ learning resources see the [Learning
-# MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/learning_mlj/)
+# MLJ](https://juliaai.github.io/MLJ.jl/dev/learning_mlj/)
 # section of the
-# [manual](https://alan-turing-institute.github.io/MLJ.jl/dev/).
+# [manual](https://juliaai.github.io/MLJ.jl/dev/).
 
 # **Topics covered**: Grabbing and preparing a dataset, basic
 # fit/predict workflow, constructing a pipeline to include data
@@ -78,7 +78,7 @@
 # the notebook, package instantiation and pre-compilation may take a
 # minute or so to complete. **This step will fail** if the [correct
 # Manifest.toml and Project.toml
-# files](https://github.com/alan-turing-institute/MLJ.jl/tree/dev/examples/telco)
+# files](https://github.com/JuliaAI/MLJ.jl/tree/dev/examples/telco)
 # are not in the same directory as this notebook.
 
 using Pkg
@@ -94,7 +94,7 @@ Pkg.instantiate()
 # don't fully grasp should become clearer in the Telco study.
 
 # This section is a condensed adaption of the [Getting Started
-# example](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/#Fit-and-predict)
+# example](https://juliaai.github.io/MLJ.jl/dev/getting_started/#Fit-and-predict)
 # in the MLJ documentation.
 
 # First, using the built-in iris dataset, we load and inspect the features
@@ -137,7 +137,7 @@ fit!(mach, rows=train_rows)
 fitted_params(mach)
 
 # A machine stores some other information enabling [warm
-# restart](https://alan-turing-institute.github.io/MLJ.jl/dev/machines/#Warm-restarts)
+# restart](https://juliaai.github.io/MLJ.jl/dev/machines/#Warm-restarts)
 # for some models, but we won't go into that here. You are allowed to
 # access and mutate the `model` parameter:
 
@@ -292,7 +292,7 @@ const ytest, Xtest = unpack(df_test, ==(:Churn), !=(:customerID));
 # > Introduces: `@load`, `input_scitype`, `target_scitype`
 
 # For tools helping us to identify suitable models, see the [Model
-# Search](https://alan-turing-institute.github.io/MLJ.jl/dev/model_search/#model_search)
+# Search](https://juliaai.github.io/MLJ.jl/dev/model_search/#model_search)
 # section of the manual. We will build a gradient tree-boosting model,
 # a popular first choice for structured data like we have here. Model
 # code is contained in a third-party package called
@@ -340,7 +340,7 @@ pipe = ContinuousEncoder() |> booster
 
 # Note that the component models appear as hyperparameters of
 # `pipe`. Pipelines are an implementation of a more general [model
-# composition](https://alan-turing-institute.github.io/MLJ.jl/dev/composing_models/#Composing-Models)
+# composition](https://juliaai.github.io/MLJ.jl/dev/composing_models/#Composing-Models)
 # interface provided by MLJ that advanced users may want to learn about.
 
 # From the above display, we see that component model hyperparameters
@@ -464,7 +464,7 @@ plot!([0, 1], [0, 1], linewidth=2, linestyle=:dash, color=:black)
 # `acceleration=CPUThreads()` to parallelize the computation.
 
 # We choose a `StratifiedCV` resampling strategy; the complete list of options is
-# [here](https://alan-turing-institute.github.io/MLJ.jl/dev/evaluating_model_performance/#Built-in-resampling-strategies).
+# [here](https://juliaai.github.io/MLJ.jl/dev/evaluating_model_performance/#Built-in-resampling-strategies).
 
 e_pipe = evaluate(pipe, X, y,
                   resampling=StratifiedCV(nfolds=6, rng=123),
@@ -535,7 +535,7 @@ pipe2 = ContinuousEncoder() |>
 # [MLJFlux.jl](https://github.com/FluxML/MLJFlux.jl).
 
 # First, we select appropriate controls from [this
-# list](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/#Controls-provided):
+# list](https://juliaai.github.io/MLJ.jl/dev/controlling_iterative_models/#Controls-provided):
 
 controls = [
     Step(1),              # to increment iteration parameter (`pipe.nrounds`)
@@ -580,7 +580,7 @@ fit!(mach_iterated_pipe);
 # wanting to visualize the effect of changes to a *single*
 # hyperparameter (which could be an iteration parameter). See, for
 # example, [this section of the
-# manual](https://alan-turing-institute.github.io/MLJ.jl/dev/learning_curves/)
+# manual](https://juliaai.github.io/MLJ.jl/dev/learning_curves/)
 # or [this
 # tutorial](https://github.com/ablaom/MLJTutorial.jl/blob/dev/notebooks/04_tuning/notebook.ipynb).
 
@@ -618,7 +618,7 @@ r2 = range(iterated_pipe, p2, lower=2, upper=6)
 # and `upper`.
 
 # Next, we choose an optimization strategy from [this
-# list](https://alan-turing-institute.github.io/MLJ.jl/dev/tuning_models/#Tuning-Models):
+# list](https://juliaai.github.io/MLJ.jl/dev/tuning_models/#Tuning-Models):
 
 tuning = RandomSearch(rng=123)
 
@@ -755,4 +755,3 @@ ŷ_basic = predict(mach_basic, Xtest);
       auc(ŷ_basic, ytest),
       accuracy(mode.(ŷ_basic), ytest)
       )
-

examples/telco/notebook.jl

@@ -10,7 +10,7 @@ md"# MLJ for Data Scientists in Two Hours"
 # ╔═╡ 8a6670b8-96a8-4a5d-b795-033f6f2a0674
 md"""
 An application of the [MLJ
-toolbox](https://alan-turing-institute.github.io/MLJ.jl/dev/) to the
+toolbox](https://juliaai.github.io/MLJ.jl/dev/) to the
 Telco Customer Churn dataset, aimed at practicing data scientists
 new to MLJ (Machine Learning in Julia). This tutorial does not
 cover exploratory data analysis.
@@ -25,9 +25,9 @@ deep-learning).
 # ╔═╡ b04c4790-59e0-42a3-af2a-25235e544a31
 md"""
 For other MLJ learning resources see the [Learning
-MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/learning_mlj/)
+MLJ](https://juliaai.github.io/MLJ.jl/dev/learning_mlj/)
 section of the
-[manual](https://alan-turing-institute.github.io/MLJ.jl/dev/).
+[manual](https://juliaai.github.io/MLJ.jl/dev/).
 """
 
 # ╔═╡ 4eb8dff4-c23a-4b41-8af5-148d95ea2900
@@ -106,7 +106,7 @@ used to develop this tutorial. If this is your first time running
 the notebook, package instantiation and pre-compilation may take a
 minute or so to complete. **This step will fail** if the [correct
 Manifest.toml and Project.toml
-files](https://github.com/alan-turing-institute/MLJ.jl/tree/dev/examples/telco)
+files](https://github.com/JuliaAI/MLJ.jl/tree/dev/examples/telco)
 are not in the same directory as this notebook.
 """
 
@@ -131,7 +131,7 @@ don't fully grasp should become clearer in the Telco study.
 # ╔═╡ 33ca287e-8cba-47d1-a0de-1721c1bc2df2
 md"""
 This section is a condensed adaption of the [Getting Started
-example](https://alan-turing-institute.github.io/MLJ.jl/dev/getting_started/#Fit-and-predict)
+example](https://juliaai.github.io/MLJ.jl/dev/getting_started/#Fit-and-predict)
 in the MLJ documentation.
 """
 
@@ -197,7 +197,7 @@ end
 # ╔═╡ 0f978839-cc95-4c3a-8a29-32f11452654a
 md"""
 A machine stores some other information enabling [warm
-restart](https://alan-turing-institute.github.io/MLJ.jl/dev/machines/#Warm-restarts)
+restart](https://juliaai.github.io/MLJ.jl/dev/machines/#Warm-restarts)
 for some models, but we won't go into that here. You are allowed to
 access and mutate the `model` parameter:
 """
@@ -324,7 +324,7 @@ begin
           return x
       end
   end
-  
+
   df0.TotalCharges = fix_blanks(df0.TotalCharges);
 end
 
@@ -424,7 +424,7 @@ md"> Introduces: `@load`, `input_scitype`, `target_scitype`"
 # ╔═╡ f97969e2-c15c-42cf-a6fa-eaf14df5d44b
 md"""
 For tools helping us to identify suitable models, see the [Model
-Search](https://alan-turing-institute.github.io/MLJ.jl/dev/model_search/#model_search)
+Search](https://juliaai.github.io/MLJ.jl/dev/model_search/#model_search)
 section of the manual. We will build a gradient tree-boosting model,
 a popular first choice for structured data like we have here. Model
 code is contained in a third-party package called
@@ -497,7 +497,7 @@ pipe = ContinuousEncoder() |> booster
 md"""
 Note that the component models appear as hyperparameters of
 `pipe`. Pipelines are an implementation of a more general [model
-composition](https://alan-turing-institute.github.io/MLJ.jl/dev/composing_models/#Composing-Models)
+composition](https://juliaai.github.io/MLJ.jl/dev/composing_models/#Composing-Models)
 interface provided by MLJ that advanced users may want to learn about.
 """
 
@@ -693,7 +693,7 @@ observation space, for a total of 18 folds) and set
 # ╔═╡ 562887bb-b7fb-430f-b61c-748aec38e674
 md"""
 We choose a `StratifiedCV` resampling strategy; the complete list of options is
-[here](https://alan-turing-institute.github.io/MLJ.jl/dev/evaluating_model_performance/#Built-in-resampling-strategies).
+[here](https://juliaai.github.io/MLJ.jl/dev/evaluating_model_performance/#Built-in-resampling-strategies).
 """
 
 # ╔═╡ f9be989e-2604-44c2-9727-ed822e4fd85d
@@ -734,7 +734,7 @@ begin
       table = (measure=measure, measurement=measurement)
       return DataFrames.DataFrame(table)
   end
-  
+
   const confidence_intervals_basic_model = confidence_intervals(e_pipe)
 end
 
@@ -753,7 +753,7 @@ with low feature importance, to speed up later optimization:
 # ╔═╡ cdfe840d-4e87-467f-b582-dfcbeb05bcc5
 begin
   unimportant_features = filter(:importance => <(0.005), feature_importance_table).feature
-  
+
   pipe2 = ContinuousEncoder() |>
       FeatureSelector(features=unimportant_features, ignore=true) |> booster
 end
@@ -790,7 +790,7 @@ eg, the neural network models provided by
 # ╔═╡ 8fc99d35-d8cc-455f-806e-1bc580dc349d
 md"""
 First, we select appropriate controls from [this
-list](https://alan-turing-institute.github.io/MLJ.jl/dev/controlling_iterative_models/#Controls-provided):
+list](https://juliaai.github.io/MLJ.jl/dev/controlling_iterative_models/#Controls-provided):
 """
 
 # ╔═╡ 29f33708-4a82-4acc-9703-288eae064e2a
@@ -857,7 +857,7 @@ here is the `learning_curve` function, which can be useful when
 wanting to visualize the effect of changes to a *single*
 hyperparameter (which could be an iteration parameter). See, for
 example, [this section of the
-manual](https://alan-turing-institute.github.io/MLJ.jl/dev/learning_curves/)
+manual](https://juliaai.github.io/MLJ.jl/dev/learning_curves/)
 or [this
 tutorial](https://github.com/ablaom/MLJTutorial.jl/blob/dev/notebooks/04_tuning/notebook.ipynb).
 """
@@ -898,7 +898,7 @@ show(iterated_pipe, 2)
 begin
   p1 = :(model.evo_tree_classifier.η)
   p2 = :(model.evo_tree_classifier.max_depth)
-  
+
   r1 = range(iterated_pipe, p1, lower=-2, upper=-0.5, scale=x->10^x)
   r2 = range(iterated_pipe, p2, lower=2, upper=6)
 end
@@ -912,7 +912,7 @@ and `upper`.
 # ╔═╡ af3023e6-920f-478d-af76-60dddeecbe6c
 md"""
 Next, we choose an optimization strategy from [this
-list](https://alan-turing-institute.github.io/MLJ.jl/dev/tuning_models/#Tuning-Models):
+list](https://juliaai.github.io/MLJ.jl/dev/tuning_models/#Tuning-Models):
 """
 
 # ╔═╡ 93c17a9b-b49c-4780-9074-c069a0e97d7e
@@ -1105,9 +1105,9 @@ md"For comparison, here's the performance for the basic pipeline model"
 begin
   mach_basic = machine(pipe, X, y)
   fit!(mach_basic, verbosity=0)
-  
+
   ŷ_basic = predict(mach_basic, Xtest);
-  
+
   @info("Basic model measurements on test set:",
         brier_loss(ŷ_basic, ytest) |> mean,
         auc(ŷ_basic, ytest),