A generic and modular framework for building custom iterative algorithms in Julia
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February 2017


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LearningStrategies is a modular framework for building iterative algorithms in Julia.

Below, some of the key concepts are briefly explained, and a few examples are made. A more in-depth notebook can be found here


Many algorithms can be generalized to the following pseudocode:

while not finished:
    (update model)
    (iteration logic)


The core function of LearningStrategies is a straightforward abstract implementation of the above loop. A model can be learned by an LearningStrategy or a collection of strategies in a MetaStrategy.

function learn!(model, strat::LearningStrategy, data)
    setup!(strat, model[, data])
    for (i, item) in enumerate(data)
        update!(model, strat[, i], item)
        hook(strat, model[, data], i)
        finished(strat, model[, data], i) && break
    cleanup!(strat, model)
  • For a MetaStrategy, each function (setup!, update!, hook, finished, cleanup!) is mapped to the contained strategies.
  • To let item == data, pass the argument Iterators.repeated(data).

Built In Strategies

See help (i.e. ?MaxIter) for more info.

  • MetaStrategy
  • MaxIter
  • TimeLimit
  • Converged
  • ConvergedTo
  • IterFunction
  • Tracer
  • Breaker
  • Verbose


Learning with a single LearningStrategy

julia> using LearningStrategies

julia> s = Verbose(TimeLimit(2))
Verbose TimeLimit(2.0)

julia> @elapsed learn!(nothing, s)  # data == InfiniteNothing()
INFO: TimeLimit(2.0) finished

Learning with a MetaLearner

julia> using LearningStrategies

julia> s = strategy(Verbose(MaxIter(5)), TimeLimit(10))
  > Verbose MaxIter(5)
  > TimeLimit(10.0)

julia> learn!(nothing, s, 1:100)
INFO: MaxIter: 1/5
INFO: MaxIter: 2/5
INFO: MaxIter: 3/5
INFO: MaxIter: 4/5
INFO: MaxIter: 5/5
INFO: MaxIter(5) finished

Linear Regression Solver

using LearningStrategies
import LearningStrategies: update!, finished
import Base.Iterators: repeated

struct MyLinearModel

struct MyLinearModelSolver <: LearningStrategy end

update!(model, s::MyLinearModelSolver, xy) = (model.coef[:] = xy[1] \ xy[2])

finished(s::MyLinearModelSolver, model) = true

# generate some fake data
x = randn(100, 5)
y = x * range(-1, stop=1, length=5) + randn(100)

data = (x, y)

# Create the model
model = MyLinearModel(zeros(5))

# learn! the model with data (x, y)
learn!(model, MyLinearModelSolver(), repeated(data))

# check that it works
model.coef == x \ y

More Examples

There are some user contributed snippets in the examples dir.

  • dftracer.jl shows a tracer with DataFrame as underlying storage.


LearningStrategies is partially inspired by IterationManagers and (Tom Breloff's) conversations with Spencer Lyon. This functionality was previously part of the StochasticOptimization package, but was split off as a dependency.

Complex LearningStrategy examples (using previous LearningStrategies versions) can be found in StochasticOptimization and from Tom Breloff's blog posts.

Examples using the current version can be found in SparseRegression.

Primary author: Tom Breloff

Required Packages