An API for dispatching on the "scientific" type of data instead of the machine type
Author alan-turing-institute
37 Stars
Updated Last
2 Years Ago
Started In
August 2019


[Linux] Coverage
Build status

A light-weight, dependency-free, Julia interface defining a collection of types (without instances) for implementing conventions about the scientific interpretation of data.

This package makes a distinction between the machine type and scientific type of a Julia object:

  • The machine type refers to the Julia type being used to represent the object (for instance, Float64).

  • The scientific type is one of the types defined in this package reflecting how the object should be interpreted (for instance, Continuous or Multiclass).

The distinction is useful because the same machine type is often used to represent data with differing scientific interpretations - Int is used for product numbers (a factor) but also for a person's weight (a continuous variable) - while the same scientific type is frequently represented by different machine types - both Int and Float64 are used to represent weights, for example.

For implementation of a concrete convention assigning specific scientific types (interpretations) to julia objects, see instead the MLJScientificTypes package.

├─ Multiclass{N}
└─ OrderedFactor{N}

├─ Continuous
└─ Count

├─ ColorImage{W,H}
└─ GrayImage{W,H}

├─ ScientificDate
├─ ScientificTime
└─ ScientificDateTime





Figure 1. The type hierarchy defined in ScientificTypes.jl (The Julia native Missing type is also regarded as a scientific type).


Who is this repository for?

This package should only be used by developers who intend to define their own scientific type convention. The MLJScientificTypes.jl package implements such a convention, first adopted in the MLJ universe, but which can be adopted by other statistical and scientific software.

The purpose of this package is to provide a mechanism for articulating conventions around the scientific interpretation of data. With such a convention in place, a numerical algorithm declares its data requirements in terms of scientific types, the user has a convenient way to check compliance of his data with that requirement, and the developer understands precisely the constraints his data specification places on the actual machine type of the data supplied.

What is provided here?

1. Scientific types

ScientificTypes provides the new julia types appearing in Figure 1 above, signifying "scientific type" for use in method dispatch (e.g., for trait values). Instances of the types play no role.

The types Finite{N}, Multiclass{N} and OrderedFactor{N} are all parametrised by the number of levels N, while Image{W,H}, GrayImage{W,H} and ColorImage{W,H} are all parametrised by the image width and height dimensions, (W, H). The type ManifoldPoint{MT}, intended for points lying on a manifold, is parameterized by the type MT of the manifold to which the points belong.

The scientific type ScientificDate is for representing dates (for example, the 23rd of April, 2029), ScientificTime represents time within a 24-hour day, while ScientificDateTime represents both a time of day and date. These types mirror the types Date, Time and DateTime from the Julia standard library Dates (and indeed, in the MLJ convention the difference is only a formal one).

The type parameter K in Table{K} is for conveying the scientific type(s) of a table's columns. See More on the Table type.

The julia native types Missing and Nothing are also regarded as scientific types.

2. The scitype and Scitype methods

ScientificTypes provides a method scitype for articulating a particular convention: scitype(X) is the scientific type of object X. For example, in the MLJ convention, implemented by MLJScientificTypes, one has scitype(3.14) = Continuous and scitype(42) = Count.

Aside. scitype is not a mapping of types to types but from instances to types. This is because one may want to distinguish the scientific type of objects having the same machine type. For example, in the MLJ convention, some CategoricalArrays.CategoricalValue objects have the scitype OrderedFactor but others are Multiclass. In CategoricalArrays.jl the ordered attribute is not a type parameter and so it can only be extracted from instances.

The developer implementing a particular scientific type convention overloads the scitype method appropriately. However, this package provides certain rudimentary fallback behaviour; only Property 1 below should be altered by the developer:

Property 0. scitype(missing) = Missing and scitype(nothing) = Nothing (regarding Missing and Nothing as native scientific types).

Property 1. scitype(X) = Unknown, unless X is a tuple, an abstract array, or missing.

Property 2. The scitype of a k-tuple is Tuple{S1, S2, ..., Sk} where Sj is the scitype of the jth element.

For example, in the MLJ convention:

julia> scitype((1, 4.5))
Tuple{Count, Continuous}

Property 3. The scitype of an AbstractArray, A, is alwaysAbstractArray{U} where U is the union of the scitypes of the elements of A, with one exception: If typeof(A) <: AbstractArray{Union{Missing,T}} for some T different from Any, then the scitype of A is AbstractArray{Union{Missing, U}}, where U is the union over all non-missing elements, even if A has no missing elements.

The exception is made for performance reasons. In MLJ:

julia> v = [1.3, 4.5, missing]
julia> scitype(v)
AbstractArray{Union{Missing, Continuous},1}
julia> scitype(v[1:2])
AbstractArray{Union{Missing, Continuous},1}

Performance note. Computing type unions over large arrays is expensive and, depending on the convention's implementation and the array eltype, computing the scitype can be slow. In the common case that the scitype of an array can be determined from the machine type of the object alone, the implementer of a new connvention can speed up compututations by implementing a Scitype method. Do ?ScientificTypes.Scitype for details.

3. Trait dictionary

Scientific types provides a dictionary TRAIT_FUNCTION_GIVEN_NAME for registering names (symbols) for boolean-value trait functions used to dispatch scitype in cases that direct type-dispatch is inadequate. See below for details.

4. Convenience methods

Scientific provides the following convenience functions:

  • trait(X) - return the trait name associated with the trait holding for X

  • set_convention(C) - activate the convention named C

  • set_convention() - inspect the active convention

  • scitype_union(A) - return the union of the scitypes of all elements of iterable A

  • elscitype(A) - return the "element scitype" of array A

Query the doc-strings for details.

More on the Table type

An object of scitype Table{K} is expected to have a notion of "columns", which are AbstractVectors, and the intention of the type parameter K is to encode the scientific type(s) of its columns. Specifically, developers are requested to adhere to the following:

Tabular data convention. If scitype(X) <: Table, then in fact

scitype(X) == Table{Union{scitype(c1), ..., scitype(cn)}}

where c1, c2, ..., cn are the columns of X. With this definition, common type checks can be performed with tables. For instance, you could check that each column of X has an element scitype that is either Continuous or Finite:

scitype(X) <: Table{<:Union{AbstractVector{<:Continuous}, AbstractVector{<:Finite}}}

A built-in Table constructor provides a shorthand for the right-hand side:

scitype(X) <: Table(Continuous, Finite)

Note that Table(Continuous,Finite) is a type union and not a Table instance.

Defining a new convention

If you want to implement your own convention, you can consider the MLJScientificTypes.jl as a blueprint.

The steps below summarise the possible steps in defining such a convention:

  • declare a new convention,
  • add explicit scitype (and Scitype) definitions,
  • register any traits that were needed to define scitypes,
  • optionally define coerce methods for your convention

Each step is explained below, taking the MLJ convention as an example.

Naming the convention

In the module, define a

struct MyConvention <: ScientificTypes.Convention end

and add an init function with:

function __init__()

Adding explicit scitype declarations.

When overloading scitype one needs to dipatch over the convention, as in this example:

ScientificTypes.scitype(::Integer, ::MLJ) = Count

In some cases, however, the scientific type to be attributed to an object might depend on the evaluation of a boolean-valued trait function. There is a mechanism for "registering" such traits to streamline trait-based dispatch of the scitype method. This is best illustrated with an example.

In the MLJ convention, all containers that meet the Tables.jl interface are deemed to have scitype Table. These are detected using the Tables.jl trait istable. Our first step is to choose a name for the trait, in this case :table. Our scitype declaration then reads:

function ScientificTypes.scitype(X, ::MLJ, ::Val{:table})
   K = <some type depending on columns of X>
   return Table{K}

For this to work we now need to register the trait, which means adding to the TRAIT_FUNCTION_GIVEN_NAME dictionary, which should be performed within the init function of the defining package:

function __init__()
    ScientificTypes.TRAIT_FUNCTION_GIVEN_NAME[:table] = Tables.istable

Important limitation. One may not add a trait function to the TRAIT_FUNCTION_GIVEN_NAME dictionary if it holds true on some object X for which an existing trait already holds true.

Defining a coerce function

It may be very useful to define a function to coerce machine types so as to correct an unintended scientific interpretation, according to a given convention. In the MLJ convention, this is implemented by defining coerce methods (no stub provided by ScientificTypes)

For instance consider the simplified:

function coerce(y::AbstractArray{T}, T2::Type{<:Union{Missing,Continuous}}
                ) where T <: Union{Missing,Real}
    return float(y)

Under this definition, coerce([1, 2, 4], Continuous) is mapped to [1.0, 2.0, 4.0], which has scitype AbstractVector{Continuous}.

In the case of tabular data, one might additionally define coerce methods to selectively coerce data in specified columns. See MLJScientificType for examples.