Update docstrings and markdown docs

Add language tags, fences in code examples. Replace tabs with spaces
because Documenter.jl uses them as 8 when 4 is meant and it breaks a lot
of lists. Also align hanging list items to keep them in the <li>.

Author: abhro

docs/src/document_strings.md

@@ -29,23 +29,39 @@ Your document string must include the following components, in order:
   implementation. Generally, defer details on the role of
   hyperparameters to the "Hyperparameters" section (see below).
 
-- Instructions on *how to import the model type* from MLJ (because a user can already inspect the doc-string in the Model Registry, without having loaded the code-providing package).
+- Instructions on *how to import the model type* from MLJ (because a user can
+  already inspect the doc-string in the Model Registry, without having loaded
+  the code-providing package).
 
 - Instructions on *how to instantiate* with default hyperparameters or with keywords.
 
-- A *Training data* section: explains how to bind a model to data in a machine with all possible signatures (eg, `machine(model, X, y)` but also `machine(model, X, y, w)` if, say, weights are supported);  the role and scitype requirements for each data argument should be itemized.
+- A *Training data* section: explains how to bind a model to data in a machine
+  with all possible signatures (eg, `machine(model, X, y)` but also
+  `machine(model, X, y, w)` if, say, weights are supported);  the role and
+  scitype requirements for each data argument should be itemized.
 
 - Instructions on *how to fit* the machine (in the same section).
 
 - A *Hyperparameters* section (unless there aren't any): an itemized list of the parameters, with defaults given.
 
-- An *Operations* section: each implemented operation (`predict`, `predict_mode`, `transform`, `inverse_transform`, etc ) is itemized and explained. This should include operations with no data arguments, such as `training_losses` and `feature_importances`.
+- An *Operations* section: each implemented operation (`predict`,
+  `predict_mode`, `transform`, `inverse_transform`, etc ) is itemized and
+  explained. This should include operations with no data arguments, such as
+  `training_losses` and `feature_importances`.
 
-- A *Fitted parameters* section: To explain what is returned by `fitted_params(mach)` (the same as `MLJModelInterface.fitted_params(model, fitresult)` -  see later) with the fields of that named tuple itemized.
+- A *Fitted parameters* section: To explain what is returned by `fitted_params(mach)`
+  (the same as `MLJModelInterface.fitted_params(model, fitresult)` -  see later)
+  with the fields of that named tuple itemized.
 
-- A *Report* section (if `report` is non-empty): To explain what, if anything, is included in the `report(mach)`  (the same as the `report` return value of `MLJModelInterface.fit`) with the fields itemized.
+- A *Report* section (if `report` is non-empty): To explain what, if anything,
+  is included in the `report(mach)`  (the same as the `report` return value of
+  `MLJModelInterface.fit`) with the fields itemized.
 
-- An optional but highly recommended *Examples* section, which includes MLJ examples, but which could also include others if the model type also implements a second "local" interface, i.e., defined in the same module. (Note that each module referring to a type can declare separate doc-strings which appear concatenated in doc-string queries.)
+- An optional but highly recommended *Examples* section, which includes MLJ
+  examples, but which could also include others if the model type also
+  implements a second "local" interface, i.e., defined in the same module. (Note
+  that each module referring to a type can declare separate doc-strings which
+  appear concatenated in doc-string queries.)
 
 - A closing *"See also"* sentence which includes a `@ref` link to the raw model type (if you are wrapping one).
 

docs/src/implementing_a_data_front_end.md

@@ -84,30 +84,34 @@ Suppose a supervised model type `SomeSupervised` supports sample
 weights, leading to two different `fit` signatures, and that it has a
 single operation `predict`:
 
-	fit(model::SomeSupervised, verbosity, X, y)
-	fit(model::SomeSupervised, verbosity, X, y, w)
+```julia
+fit(model::SomeSupervised, verbosity, X, y)
+fit(model::SomeSupervised, verbosity, X, y, w)
 
-	predict(model::SomeSupervised, fitresult, Xnew)
+predict(model::SomeSupervised, fitresult, Xnew)
+```
 
 Without a data front-end implemented, suppose `X` is expected to be a
 table and `y` a vector, but suppose the core algorithm always converts
 `X` to a matrix with features as rows (each record corresponds to
 a column in the table).  Then a new data-front end might look like
 this:
 
-	constant MMI = MLJModelInterface
-
-	# for fit:
-	MMI.reformat(::SomeSupervised, X, y) = (MMI.matrix(X)', y)
-	MMI.reformat(::SomeSupervised, X, y, w) = (MMI.matrix(X)', y, w)
-	MMI.selectrows(::SomeSupervised, I, Xmatrix, y) =
-		(view(Xmatrix, :, I), view(y, I))
-	MMI.selectrows(::SomeSupervised, I, Xmatrix, y, w) =
-		(view(Xmatrix, :, I), view(y, I), view(w, I))
-
-	# for predict:
-	MMI.reformat(::SomeSupervised, X) = (MMI.matrix(X)',)
-	MMI.selectrows(::SomeSupervised, I, Xmatrix) = (view(Xmatrix, :, I),)
+```julia
+constant MMI = MLJModelInterface
+
+# for fit:
+MMI.reformat(::SomeSupervised, X, y) = (MMI.matrix(X)', y)
+MMI.reformat(::SomeSupervised, X, y, w) = (MMI.matrix(X)', y, w)
+MMI.selectrows(::SomeSupervised, I, Xmatrix, y) =
+        (view(Xmatrix, :, I), view(y, I))
+MMI.selectrows(::SomeSupervised, I, Xmatrix, y, w) =
+        (view(Xmatrix, :, I), view(y, I), view(w, I))
+
+# for predict:
+MMI.reformat(::SomeSupervised, X) = (MMI.matrix(X)',)
+MMI.selectrows(::SomeSupervised, I, Xmatrix) = (view(Xmatrix, :, I),)
+```
 
 With these additions, `fit` and `predict` are refactored, so that `X`
 and `Xnew` represent matrices with features as rows.

docs/src/quick_start_guide.md

@@ -99,8 +99,7 @@ Further to the last point, `a::Float64 = 0.5::(_ > 0)` indicates that
 the field `a` is a `Float64`, takes `0.5` as its default value, and
 expects its value to be positive.
 
-Please see [this
-issue](https://github.com/JuliaAI/MLJBase.jl/issues/68)
+Please see [this issue](https://github.com/JuliaAI/MLJBase.jl/issues/68)
 for a known issue and workaround relating to the use of `@mlj_model`
 with negative defaults.
 
@@ -201,7 +200,7 @@ For a classifier, the steps are fairly similar to a regressor with these differe
 1. `y` will be a categorical vector and you will typically want to use
    the integer encoding of `y` instead of `CategoricalValue`s; use
    `MLJModelInterface.int` for this.
-1.  You will need to pass the full pool of target labels (not just
+2.  You will need to pass the full pool of target labels (not just
    those observed in the training data) and additionally, in the
    `Deterministic` case, the encoding, to make these available to
    `predict`. A simple way to do this is to pass `y[1]` in the
@@ -210,19 +209,19 @@ For a classifier, the steps are fairly similar to a regressor with these differe
    method for recovering categorical elements from their integer
    representations (e.g., `d(2)` is the categorical element with `2`
    as encoding).
-2. In the case of a *probabilistic* classifier you should pass all
+3. In the case of a *probabilistic* classifier you should pass all
    probabilities simultaneously to the [`UnivariateFinite`](@ref) constructor
    to get an abstract `UnivariateFinite` vector (type
    `UnivariateFiniteArray`) rather than use comprehension or
    broadcasting to get a vanilla vector. This is for performance
    reasons.
-   
+
 If implementing a classifier, you should probably consult the more
 detailed instructions at [The predict method](@ref).
 
 **Examples**:
 
--  GLM's [BinaryClassifier](https://github.com/JuliaAI/MLJModels.jl/blob/3687491b132be8493b6f7a322aedf66008caaab1/src/GLM.jl#L119-L131) (`Probabilistic`)
+- GLM's [BinaryClassifier](https://github.com/JuliaAI/MLJModels.jl/blob/3687491b132be8493b6f7a322aedf66008caaab1/src/GLM.jl#L119-L131) (`Probabilistic`)
 
 - LIBSVM's [SVC](https://github.com/JuliaAI/MLJModels.jl/blob/master/src/LIBSVM.jl) (`Deterministic`)
 
@@ -349,8 +348,8 @@ MLJModelInterface.metadata_model(YourModel1,
     output_scitype  = MLJModelInterface.Table(MLJModelInterface.Continuous),  # for an unsupervised, what output?
     supports_weights = false,                                                  # does the model support sample weights?
     descr   = "A short description of your model"
-	load_path    = "YourPackage.SubModuleContainingModelStructDefinition.YourModel1"
-    )
+    load_path    = "YourPackage.SubModuleContainingModelStructDefinition.YourModel1"
+)
 ```
 
 *Important.* Do not omit the `load_path` specification. Without a

docs/src/summary_of_methods.md

@@ -43,11 +43,11 @@ Optional, if `SomeSupervisedModel <: Probabilistic`:
 
 ```julia
 MMI.predict_mode(model::SomeSupervisedModel, fitresult, Xnew) =
-	mode.(predict(model, fitresult, Xnew))
+    mode.(predict(model, fitresult, Xnew))
 MMI.predict_mean(model::SomeSupervisedModel, fitresult, Xnew) =
-	mean.(predict(model, fitresult, Xnew))
+    mean.(predict(model, fitresult, Xnew))
 MMI.predict_median(model::SomeSupervisedModel, fitresult, Xnew) =
-	median.(predict(model, fitresult, Xnew))
+    median.(predict(model, fitresult, Xnew))
 ```
 
 Required, if the model is to be registered (findable by general users):

docs/src/supervised_models.md

@@ -19,15 +19,15 @@ The following sections were written with `Supervised` models in mind, but also c
 material relevant to general models:
 
 - [Summary of methods](@ref)
-- [The form of data for fitting and predicting](@ref) 
+- [The form of data for fitting and predicting](@ref)
 - [The fit method](@ref)
 - [The fitted_params method](@ref)
-- [The predict method](@ref) 
-- [The predict_joint method](@ref) 
-- [Training losses](@ref) 
-- [Feature importances](@ref) 
-- [Trait declarations](@ref) 
-- [Iterative models and the update! method](@ref) 
-- [Implementing a data front end](@ref) 
-- [Supervised models with a transform method](@ref) 
+- [The predict method](@ref)
+- [The predict_joint method](@ref)
+- [Training losses](@ref)
+- [Feature importances](@ref)
+- [Trait declarations](@ref)
+- [Iterative models and the update! method](@ref)
+- [Implementing a data front end](@ref)
+- [Supervised models with a transform method](@ref)
 - [Models that learn a probability distribution](@ref)

docs/src/the_fit_method.md

@@ -7,21 +7,21 @@ MMI.fit(model::SomeSupervisedModel, verbosity, X, y) -> fitresult, cache, report
 ```
 
 1. `fitresult` is the fitresult in the sense above (which becomes an
-	argument for `predict` discussed below).
+    argument for `predict` discussed below).
 
 2.  `report` is a (possibly empty) `NamedTuple`, for example,
-	`report=(deviance=..., dof_residual=..., stderror=..., vcov=...)`.
-	Any training-related statistics, such as internal estimates of the
-	generalization error, and feature rankings, should be returned in
-	the `report` tuple. How, or if, these are generated should be
-	controlled by hyperparameters (the fields of `model`). Fitted
-	parameters, such as the coefficients of a linear model, do not go
-	in the report as they will be extractable from `fitresult` (and
-	accessible to MLJ through the `fitted_params` method described below).
-
-3.	The value of `cache` can be `nothing`, unless one is also defining
-	an `update` method (see below). The Julia type of `cache` is not
-	presently restricted.
+    `report=(deviance=..., dof_residual=..., stderror=..., vcov=...)`.
+    Any training-related statistics, such as internal estimates of the
+    generalization error, and feature rankings, should be returned in
+    the `report` tuple. How, or if, these are generated should be
+    controlled by hyperparameters (the fields of `model`). Fitted
+    parameters, such as the coefficients of a linear model, do not go
+    in the report as they will be extractable from `fitresult` (and
+    accessible to MLJ through the `fitted_params` method described below).
+
+3.  The value of `cache` can be `nothing`, unless one is also defining
+    an `update` method (see below). The Julia type of `cache` is not
+    presently restricted.
 
 !!! note
 

docs/src/the_predict_method.md

@@ -6,8 +6,7 @@ A compulsory `predict` method has the form
 MMI.predict(model::SomeSupervisedModel, fitresult, Xnew) -> yhat
 ```
 
-Here `Xnew` will have the same form as the `X` passed to
-`fit`.
+Here `Xnew` will have the same form as the `X` passed to `fit`.
 
 Note that while `Xnew` generally consists of multiple observations
 (e.g., has multiple rows in the case of a table) it is assumed, in view of
@@ -44,26 +43,26 @@ may look something like this:
 
 ```julia
 function MMI.fit(model::SomeSupervisedModel, verbosity, X, y)
-	yint = MMI.int(y)
-	a_target_element = y[1]                # a CategoricalValue/String
-	decode = MMI.decoder(a_target_element) # can be called on integers
+    yint = MMI.int(y)
+    a_target_element = y[1]                # a CategoricalValue/String
+    decode = MMI.decoder(a_target_element) # can be called on integers
 
-	core_fitresult = SomePackage.fit(X, yint, verbosity=verbosity)
+    core_fitresult = SomePackage.fit(X, yint, verbosity=verbosity)
 
-	fitresult = (decode, core_fitresult)
-	cache = nothing
-	report = nothing
-	return fitresult, cache, report
+    fitresult = (decode, core_fitresult)
+    cache = nothing
+    report = nothing
+    return fitresult, cache, report
 end
 ```
 
 while a corresponding deterministic `predict` operation might look like this:
 
 ```julia
 function MMI.predict(model::SomeSupervisedModel, fitresult, Xnew)
-	decode, core_fitresult = fitresult
-	yhat = SomePackage.predict(core_fitresult, Xnew)
-	return decode.(yhat)
+    decode, core_fitresult = fitresult
+    yhat = SomePackage.predict(core_fitresult, Xnew)
+    return decode.(yhat)
 end
 ```
 
@@ -155,8 +154,8 @@ yhat = MLJModelInterface.UnivariateFinite([:FALSE, :TRUE], probs, augment=true,
 ```
 
 The constructor has a lot of options, including passing a dictionary
-instead of vectors. See
-`CategoricalDistributions.UnivariateFinite`](@ref) for details.
+instead of vectors. See [`CategoricalDistributions.UnivariateFinite`](@ref)
+for details.
 
 See
 [LinearBinaryClassifier](https://github.com/JuliaAI/MLJModels.jl/blob/master/src/GLM.jl)

docs/src/trait_declarations.md

@@ -27,8 +27,7 @@ MMI.input_scitype(::Type{<:DecisionTreeClassifier}) = Table(Continuous)
 ```
 
 If, instead, columns were allowed to have either: (i) a mixture of `Continuous` and `Missing`
-values, or (ii) `Count` (i.e., integer) values, then the
-declaration would be
+values, or (ii) `Count` (i.e., integer) values, then the declaration would be
 
 ```julia
 MMI.input_scitype(::Type{<:DecisionTreeClassifier}) = Table(Union{Continuous,Missing},Count)

docs/src/type_declarations.md

@@ -8,32 +8,32 @@ import MLJModelInterface
 const MMI = MLJModelInterface
 
 mutable struct RidgeRegressor <: MMI.Deterministic
-	lambda::Float64
+    lambda::Float64
 end
 ```
 
-Models (which are mutable) should not be given internal
-constructors. It is recommended that they be given an external lazy
-keyword constructor of the same name. This constructor defines default values
-for every field, and optionally corrects invalid field values by calling a
-`clean!` method (whose fallback returns an empty message string):
+Models (which are mutable) should not be given internal constructors.
+It is recommended that they be given an external lazy keyword constructor
+of the same name. This constructor defines default values for every field,
+and optionally corrects invalid field values by calling a `clean!`
+method (whose fallback returns an empty message string):
 
 ```julia
 function MMI.clean!(model::RidgeRegressor)
-	warning = ""
-	if model.lambda < 0
-		warning *= "Need lambda ≥ 0. Resetting lambda=0. "
-		model.lambda = 0
-	end
-	return warning
+    warning = ""
+    if model.lambda < 0
+        warning *= "Need lambda ≥ 0. Resetting lambda=0. "
+        model.lambda = 0
+    end
+    return warning
 end
 
 # keyword constructor
 function RidgeRegressor(; lambda=0.0)
-	model = RidgeRegressor(lambda)
-	message = MMI.clean!(model)
-	isempty(message) || @warn message
-	return model
+    model = RidgeRegressor(lambda)
+    message = MMI.clean!(model)
+    isempty(message) || @warn message
+    return model
 end
 ```
 
@@ -96,8 +96,8 @@ following example:
 
 ```julia
 @mlj_model mutable struct YourModel <: MMI.Deterministic
-	a::Float64 = 0.5::(_ > 0)
-	b::String  = "svd"::(_ in ("svd","qr"))
+    a::Float64 = 0.5::(_ > 0)
+    b::String  = "svd"::(_ in ("svd","qr"))
 end
 ```
 
@@ -115,22 +115,22 @@ expects its value to be positive.
 You cannot use the `@mlj_model` macro if your model struct has type
 parameters.
 
-#### Known issue with @mlj_macro
+#### Known issue with `@mlj_macro`
 
 Defaults with negative values can trip up the `@mlj_macro` (see [this
 issue](https://github.com/JuliaAI/MLJBase.jl/issues/68)). So,
 for example, this does not work:
 
 ```julia
 @mlj_model mutable struct Bar
-	a::Int = -1::(_ > -2)
+    a::Int = -1::(_ > -2)
 end
 ```
 
 But this does:
 
 ```julia
 @mlj_model mutable struct Bar
-	a::Int = (-)(1)::(_ > -2)
+    a::Int = (-)(1)::(_ > -2)
 end
 ```

docs/src/unsupervised_models.md

@@ -31,9 +31,9 @@ similar fashion. The main differences are:
   is the same as `transform`, as in
   `MLJModelInterface.inverse_transform(model, fitresult, Xout)`, which:
    - must make sense for any `Xout` for which `scitype(Xout) <:
-	 output_scitype(SomeSupervisedModel)` (see below); and
+     output_scitype(SomeSupervisedModel)` (see below); and
    - must return an object `Xin` satisfying `scitype(Xin) <:
-	 input_scitype(SomeSupervisedModel)`.
+     input_scitype(SomeSupervisedModel)`.
 
 For sample implementatations, see MLJ's [built-in
 transformers](https://github.com/JuliaAI/MLJModels.jl/blob/dev/src/builtins/Transformers.jl)