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ROADMAP.md

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Road map

  • Mock up a challenging update use-case: controlling an iterative algorithm that wants, for efficiency, to internally compute the out-of-sample predictions that will be for used to externally determined early stopping cc: @jeremiedb

  • Get code coverage to 100% (see next item)

  • Add to this repo or a utility repo methods to test a valid implementation of LearnAPI.jl

  • Flush out "Common Implementation Patterns". The current plan is to mock up example implementations, and add them as LearnAPI.jl tests, with links to the test file from "Common Implementation Patterns". As real-world implementations roll out, we could increasingly point to those instead, to conserve effort

    • regression
    • classification
    • clustering
    • gradient descent
    • iterative algorithms
    • incremental algorithms
    • dimension reduction
    • feature engineering
    • static algorithms
    • missing value imputation
    • transformers
    • ensemble algorithms
    • time series forecasting
    • time series classification
    • survival analysis
    • density estimation
    • Bayesian algorithms
    • outlier detection
    • collaborative filtering
    • text analysis
    • audio analysis
    • natural language processing
    • image processing
    • meta-algorithms

  • In a utility package provide:

    • Method to clone an algorithm with user-specified property (hyperparameter) replacement in LearnAPI.clone(algorithm, p1=value1, p22=value2, ...) (since algorithm can have any type, can't really overload Base.replace without piracy). This will be needed in tuning meta-algorithms. Or should this be in LearnAPI.jl proper, to expose it to all users?
    • Methods to facilitate common-use case data interfaces: support simultaneously fit data of the form data = (X, y) where X is table or matrix, and data a table with target specified by hyperparameter; here obs will return a thin wrapping of the matrix of X, the target y, and the names of all fields. We can have options to make X a concrete array or an adjoint, depending on what is more efficient for the algorithm.