Convenience methods
MLJModelInterface.table — Functiontable(columntable; prototype=nothing)Convert a named tuple of vectors or tuples columntable, into a table of the "preferred sink type" of prototype. This is often the type of prototype itself, when prototype is a sink; see the Tables.jl documentation. If prototype is not specified, then a named tuple of vectors is returned.
table(A::AbstractMatrix; names=nothing, prototype=nothing)Wrap an abstract matrix A as a Tables.jl compatible table with the specified column names (a tuple of symbols). If names are not specified, names=(:x1, :x2, ..., :xn) is used, where n=size(A, 2).
If a prototype is specified, then the matrix is materialized as a table of the preferred sink type of prototype, rather than wrapped. Note that if prototype is not specified, then matrix(table(A)) is essentially a no-op.
MLJModelInterface.matrix — Functionmatrix(X; transpose=false)If X isa AbstractMatrix, return X or permutedims(X) if transpose=true. Otherwise if X is a Tables.jl compatible table source, convert X into a Matrix.
MLJModelInterface.int — Functionint(x)The positional integer of the CategoricalString or CategoricalValue x, in the ordering defined by the pool of x. The type of int(x) is the reference type of x.
Not to be confused with x.ref, which is unchanged by reordering of the pool of x, but has the same type.
int(X::CategoricalArray)
+int(W::Array{<:CategoricalString})
+int(W::Array{<:CategoricalValue})Broadcasted versions of int.
julia> v = categorical(["c", "b", "c", "a"])
+4-element CategoricalArrays.CategoricalArray{String,1,UInt32}:
+ "c"
+ "b"
+ "c"
+ "a"
+
+julia> levels(v)
+3-element Vector{String}:
+ "a"
+ "b"
+ "c"
+
+julia> int(v)
+4-element Vector{UInt32}:
+ 0x00000003
+ 0x00000002
+ 0x00000003
+ 0x00000001See also: decoder.
MLJModelInterface.UnivariateFinite — FunctionUnivariateFinite(
+ support,
+ probs;
+ pool=nothing,
+ augmented=false,
+ ordered=false
+)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. Alternatively, construct an abstract array of UnivariateFinite distributions by choosing probs to be an array of one higher dimension than the array generated.
Here the word "probabilities" is an abuse of terminology as there is no requirement that probabilities actually sum to one, only that they be non-negative. So UnivariateFinite objects actually implement arbitrary non-negative measures over finite sets of labelled points. A UnivariateDistribution will be a bona fide probability measure when constructed using the augment=true option (see below) or when fit to data.
Unless pool is specified, support should have type AbstractVector{<:CategoricalValue} and all elements are assumed to share the same categorical pool, which may be larger than support.
Important. All levels of the common pool have associated 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(support) (or one less, if augment=true) and the constructor then returns an array of UnivariateFinite distributions of size (n1, n2, ..., nk).
Examples
julia> v = categorical(["x", "x", "y", "x", "z"])
+5-element CategoricalArrays.CategoricalArray{String,1,UInt32}:
+ "x"
+ "x"
+ "y"
+ "x"
+ "z"
+
+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])
+UnivariateFinite{Multiclass{3}}(x=>0.1, z=>0.9)
+
+julia> rand(d, 3)
+3-element CategoricalArrays.CategoricalArray{String,1,UInt32}:
+ "x"
+ "z"
+ "x"
+
+julia> levels(d)
+3-element Vector{String}:
+ "x"
+ "y"
+ "z"
+
+julia> pdf(d, "y")
+0.0
+Specifying a pool
Alternatively, support may be a list of raw (non-categorical) elements if pool is:
some
CategoricalArray,CategoricalValueorCategoricalPool, such thatsupportis a subset oflevels(pool)missing, in which case a new categorical pool is created which hassupportas its only levels.
In the last case, specify ordered=true if the pool is to be considered ordered.
julia> UnivariateFinite(["x", "z"], [0.1, 0.9], pool=missing, ordered=true)
+UnivariateFinite{OrderedFactor{2}}(x=>0.1, z=>0.9)
+
+julia> d = UnivariateFinite(["x", "z"], [0.1, 0.9], pool=v) # v defined above
+UnivariateFinite{Multiclass{3}}(x=>0.1, z=>0.9)
+
+julia> pdf(d, "y") # allowed as `"y" in levels(v)`
+0.0
+
+julia> v = categorical(["x", "x", "y", "x", "z", "w"])
+6-element CategoricalArrays.CategoricalArray{String,1,UInt32}:
+ "x"
+ "x"
+ "y"
+ "x"
+ "z"
+ "w"
+
+julia> probs = rand(100, 3); probs = probs ./ sum(probs, dims=2);
+
+julia> UnivariateFinite(["x", "y", "z"], probs, pool=v)
+100-element UnivariateFiniteVector{Multiclass{4}, String, UInt32, Float64}:
+ UnivariateFinite{Multiclass{4}}(x=>0.194, y=>0.3, z=>0.505)
+ UnivariateFinite{Multiclass{4}}(x=>0.727, y=>0.234, z=>0.0391)
+ UnivariateFinite{Multiclass{4}}(x=>0.674, y=>0.00535, z=>0.321)
+ ⋮
+ UnivariateFinite{Multiclass{4}}(x=>0.292, y=>0.339, z=>0.369)Probability augmentation
If augment=true the provided array is augmented by inserting appropriate elements ahead of those provided, along the last dimension of the array. This means the user only provides probabilities for the classes c2, c3, ..., cn. The class c1 probabilities are chosen so that each UnivariateFinite distribution in the returned array is a bona fide probability distribution.
UnivariateFinite(prob_given_class; pool=nothing, ordered=false)Construct a discrete univariate distribution whose finite support is 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 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.
MLJModelInterface.classes — Functionclasses(x)All the categorical elements with the same pool as x (including x), returned as a list, with an ordering consistent with the pool. Here x has CategoricalValue type, and classes(x) is a vector of the same eltype. Note that x in classes(x) is always true.
Not to be confused with levels(x.pool). See the example below.
julia> v = categorical(["c", "b", "c", "a"])
+4-element CategoricalArrays.CategoricalArray{String,1,UInt32}:
+ "c"
+ "b"
+ "c"
+ "a"
+
+julia> levels(v)
+3-element Vector{String}:
+ "a"
+ "b"
+ "c"
+
+julia> x = v[4]
+CategoricalArrays.CategoricalValue{String, UInt32} "a"
+
+julia> classes(x)
+3-element CategoricalArrays.CategoricalArray{String,1,UInt32}:
+ "a"
+ "b"
+ "c"
+
+julia> levels(x.pool)
+3-element Vector{String}:
+ "a"
+ "b"
+ "c"MLJModelInterface.decoder — Functiondecoder(x)Return a callable object for decoding the integer representation of a CategoricalValue sharing the same pool the CategoricalValue x. Specifically, one has decoder(x)(int(y)) == y for all CategoricalValues y having the same pool as x. One can also call decoder(x) on integer arrays, in which case decoder(x) is broadcast over all elements.
Examples
julia> v = categorical(["c", "b", "c", "a"])
+4-element CategoricalArrays.CategoricalArray{String,1,UInt32}:
+ "c"
+ "b"
+ "c"
+ "a"
+
+julia> int(v)
+4-element Vector{UInt32}:
+ 0x00000003
+ 0x00000002
+ 0x00000003
+ 0x00000001
+
+julia> d = decoder(v[3]);
+
+julia> d(int(v)) == v
+trueWarning:
It is not true that int(d(u)) == u always holds.
See also: int.
MLJModelInterface.select — Functionselect(X, r, c)Select element(s) of a table or matrix at row(s) r and column(s) c. An object of the sink type of X (or a matrix) is returned unless c is a single integer or symbol. In that case a vector is returned, unless r is a single integer, in which case a single element is returned.
See also: selectrows, selectcols.
MLJModelInterface.selectrows — Functionselectrows(X, r)Select single or multiple rows from a table, abstract vector or matrix X. If X is tabular, the object returned is a table of the preferred sink type of typeof(X), even if only a single row is selected.
If the object is neither a table, abstract vector or matrix, X is returned and r is ignored.
MLJModelInterface.selectcols — Functionselectcols(X, c)Select single or multiple columns from a matrix or table X. If c is an abstract vector of integers or symbols, then the object returned is a table of the preferred sink type of typeof(X). If c is a single integer or column, then an AbstractVector is returned.
MLJModelInterface.UnivariateFinite — FunctionUnivariateFinite(
+ support,
+ probs;
+ pool=nothing,
+ augmented=false,
+ ordered=false
+)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. Alternatively, construct an abstract array of UnivariateFinite distributions by choosing probs to be an array of one higher dimension than the array generated.
Here the word "probabilities" is an abuse of terminology as there is no requirement that probabilities actually sum to one, only that they be non-negative. So UnivariateFinite objects actually implement arbitrary non-negative measures over finite sets of labelled points. A UnivariateDistribution will be a bona fide probability measure when constructed using the augment=true option (see below) or when fit to data.
Unless pool is specified, support should have type AbstractVector{<:CategoricalValue} and all elements are assumed to share the same categorical pool, which may be larger than support.
Important. All levels of the common pool have associated 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(support) (or one less, if augment=true) and the constructor then returns an array of UnivariateFinite distributions of size (n1, n2, ..., nk).
Examples
julia> v = categorical(["x", "x", "y", "x", "z"])
+5-element CategoricalArrays.CategoricalArray{String,1,UInt32}:
+ "x"
+ "x"
+ "y"
+ "x"
+ "z"
+
+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])
+UnivariateFinite{Multiclass{3}}(x=>0.1, z=>0.9)
+
+julia> rand(d, 3)
+3-element CategoricalArrays.CategoricalArray{String,1,UInt32}:
+ "x"
+ "z"
+ "x"
+
+julia> levels(d)
+3-element Vector{String}:
+ "x"
+ "y"
+ "z"
+
+julia> pdf(d, "y")
+0.0
+Specifying a pool
Alternatively, support may be a list of raw (non-categorical) elements if pool is:
some
CategoricalArray,CategoricalValueorCategoricalPool, such thatsupportis a subset oflevels(pool)missing, in which case a new categorical pool is created which hassupportas its only levels.
In the last case, specify ordered=true if the pool is to be considered ordered.
julia> UnivariateFinite(["x", "z"], [0.1, 0.9], pool=missing, ordered=true)
+UnivariateFinite{OrderedFactor{2}}(x=>0.1, z=>0.9)
+
+julia> d = UnivariateFinite(["x", "z"], [0.1, 0.9], pool=v) # v defined above
+UnivariateFinite{Multiclass{3}}(x=>0.1, z=>0.9)
+
+julia> pdf(d, "y") # allowed as `"y" in levels(v)`
+0.0
+
+julia> v = categorical(["x", "x", "y", "x", "z", "w"])
+6-element CategoricalArrays.CategoricalArray{String,1,UInt32}:
+ "x"
+ "x"
+ "y"
+ "x"
+ "z"
+ "w"
+
+julia> probs = rand(100, 3); probs = probs ./ sum(probs, dims=2);
+
+julia> UnivariateFinite(["x", "y", "z"], probs, pool=v)
+100-element UnivariateFiniteVector{Multiclass{4}, String, UInt32, Float64}:
+ UnivariateFinite{Multiclass{4}}(x=>0.194, y=>0.3, z=>0.505)
+ UnivariateFinite{Multiclass{4}}(x=>0.727, y=>0.234, z=>0.0391)
+ UnivariateFinite{Multiclass{4}}(x=>0.674, y=>0.00535, z=>0.321)
+ ⋮
+ UnivariateFinite{Multiclass{4}}(x=>0.292, y=>0.339, z=>0.369)Probability augmentation
If augment=true the provided array is augmented by inserting appropriate elements ahead of those provided, along the last dimension of the array. This means the user only provides probabilities for the classes c2, c3, ..., cn. The class c1 probabilities are chosen so that each UnivariateFinite distribution in the returned array is a bona fide probability distribution.
UnivariateFinite(prob_given_class; pool=nothing, ordered=false)Construct a discrete univariate distribution whose finite support is 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 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.