Merge pull request #50 from alan-turing-institute/dev

For a 0.2.8 release

Author: ablaom

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJModelInterface"
 uuid = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 authors = ["Thibaut Lienart and Anthony Blaom"]
-version = "0.2.7"
+version = "0.2.8"
 
 [deps]
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"

src/data_utils.jl

@@ -318,7 +318,7 @@ const UNIVARIATE_FINITE_DOCSTRING =
 
 Construct a discrete univariate distribution whose finite support is
 the elements of the vector `support`, and whose corresponding
-probabilities are elements of the vector `probs`. More generally,
+probabilities are elements of the vector `probs`. Alternatively,
 construct an abstract *array* of `UnivariateFinite` distributions by
 choosing `probs` to be an array of one higher dimension than the array
 generated.
@@ -328,13 +328,13 @@ Unless `pool` is specified, `support` should have type
  share the same categorical pool, which may be larger than `support`.
 
 *Important.* All levels of the common pool have associated
-probabilites, not just those in the specified `support`. However,
+probabilities, not just those in the specified `support`. However,
 these probabilities are always zero (see example below).
 
 If `probs` is a matrix, it should have a column for each class in
 `support` (or one less, if `augment=true`). More generally, `probs`
 will be an array whose size is of the form `(n1, n2, ..., nk, c)`,
-where `c = length(suppport)` (or one less, if `augment=true`) and the
+where `c = length(support)` (or one less, if `augment=true`) and the
 constructor then returns an array of size `(n1, n2, ..., nk)`.
 
 ```
@@ -345,7 +345,7 @@ julia> UnivariateFinite(classes(v), [0.2, 0.3, 0.5])
 UnivariateFinite{Multiclass{3}}(x=>0.2, y=>0.3, z=>0.5)
 
 julia> d = UnivariateFinite([v[1], v[end]], [0.1, 0.9])
-UnivariateFiniteMulticlass{3}(x=>0.1, z=>0.9)
+UnivariateFinite{Multiclass{3}(x=>0.1, z=>0.9)
 
 julia> rand(d, 3)
 3-element Array{Any,1}:
@@ -416,10 +416,13 @@ the set of keys of the provided dictionary, `prob_given_class`, and
 whose values specify the corresponding probabilities.
 
 The type requirements on the keys of the dictionary are the same as
-the elements of `support` given above. If the values (probabilities)
-are arrays instead of scalars, then an abstract array of
-`UnivariateFinite` elements is created, with the same size as the
-array.
+the elements of `support` given above with this exception: if
+non-categorical elements (raw labels) are used as keys, then
+`pool=...` must be specified and cannot be `missing`.
+
+If the values (probabilities) are arrays instead of scalars, then an
+abstract array of `UnivariateFinite` elements is created, with the
+same size as the array.
 
 """
 UNIVARIATE_FINITE_DOCSTRING