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StreamModels

Stream yer models

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StreamModels

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The idea

This is a prototype of a different approach to generating numerical model matrices based on tabular data and a formula. It works with any data source that produces a Data.Schema and an row iterator of NamedTuples. In principle, that's anything that satisfies the DataStreams Data.Source interface, including in-memory stores like DataFrames/DataTables, as well as databases and files on disk like CSV and Feather.

Current status

At the moment this is a proof of concept/sandbox for prototyping ideas.

  • Restricted to an in-memory store (a Data.Table, which is just a NamedTuple of AbstractVector columns).
  • It relies on the NamedTuples PR to Base JuliaLang/julia#22194. It could in principle be made to work with NamedTuples.jl but I'm lazy.

Strategy

The strategy deviates substantially from the original implementation (in StatsModels.jl). The reason for this is that the original implementation is based on how R does this, and R mixes metaprogramming and normal execution of code more freely than Julia does. This makes it tricky to implement some of the features that we want in a formula domain specific language:

  1. Concise domain-specific expressions for interactions (e.g., a*b means main effects for a and b, and a term for the a&b interaction), random effects ((1+a | subject) means random intercept and slope for a for each subject), etc. That is, some symbols have different interpretations than in base julia.
  2. The interpretation of symbols referring to variables depends on their types (e.g. continuous vs. categorical), and for some symbols on their values (e.g., the levels of a categorical variable).
  3. Calls that don't have special DSL interpretations should be treated as normal Julia code (e.g., if you want to lazily log-transform a predictor with log(a)).

In order to generate a model matrix from streaming data, we'd ideally like a fast, specialized inner loop that generates one row a time. But we can't do code generate with just a macro, because the results depend on run time information (whether a variable is categorical or not, etc.). So we need a three-stage approach:

  1. Parse formula expression into a lowered form, and create Terms representations for each term. This includes turning function calls that don't have a special formula DSL interpretation into anonymous functions that consume a single row of data. Terms that need run time information are converted into Eval terms.
  2. Consume the data source once to determine the types and any necessary invariants of the data to realize Eval terms (unique elements for categorical variables), and convert Eval terms into concrete Continuous or Categorical terms. For categorical variables, contrasts matrices are computed and stored on the term.
  3. A generated function that creates one row at a time based on the tuple of terms and a NamedTuple of data for one row. The loop over terms can be unrolled since each term type is annotated with the number of columns that it will generate.

Planned API

My long-term hope is that this is a second-generation version of StatsModels with a more general and extensible API, including both streaming data, generic tabular data, and "multipart formulas" (as discussed here). The high-level entry point will be the @model macro, which captures formulas in a model constructor arguments and creates a ModelBuilder struct which is parametrized by the type of model constructed:

julia> @model df GLM(y ~ 1 + x*condition, family=Binomial)

ModelBuilder{GLM}:
    data:
        df
    args:
        Formula(y~1+x+condition+x&conditino),
    kw args:
        family=Binomial

This type will have a default in this package that immediately applies the data to the formulae and calls the model constructor with the resulting numerical matrices/vectors replacing the corresponding formulae. The result will be stored in a ModelFrame{M} struct, parametrized by the model type.

Packages that want to do non-traditional things with the formulae (like using nested formulae to denote the relationship between endogenous and instrumental variables in FixedEffectsModels.jl) can add ModelBuilder{M} constructor methods for their own model types, allowing them to manipulate the extracted formulae as necessary.

Extending

This package is designed to make the formula DSL extendable if you need to add special syntax or terms. Essentially any julia operator or function can be given special meaning. The points to extend are:

  • A subtype of Terms.Term to represent your special terms.
  • Method(s) for evaluating your term given a row of data: (t::MyTerm)(data::NamedTuple). This should return a numeric vector
  • A method of Terms.ex_from_formula to convert the abbreviated formula syntax into a constructor for your term type. Terms.ex_from_formula(::Val{head}, ex::Expr) where head is the symbol that is the head of a call ex. For instance, if you wanted to add support for random effects of the form (1+x | subject), you'd need to add a method like ex_from_formula(::Val{:|}, ex::Expr) = :(ReTerm($(ex.args[2]), $(ex.args[3]))) (in actual fact you'd probably need to parse the sub-expressions as well, constructing the necessary terms for them)
  • A method nc(::Type{MyTerm}) to determine the number of columns that your term will generate based only on the type.

First Commit

04/19/2017

Last Touched

12 months ago

Commits

104 commits

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