A package for controlling iterative algorithms
Author JuliaAI
13 Stars
Updated Last
1 Year Ago
Started In
March 2021


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A lightweight package for controlling iterative algorithms, with a view to training and optimizing machine learning models.

Builds on EarlyStopping.jl and inspired by LearningStrategies.jl.

Other related software: DynamicIterators.jl.


using Pkg

Basic idea

Suppose you have some kind of object, SquareRooter(x), for iteratively computing approximations to the square root of x:

model = SquareRooter(9)

julia> model.root

train!(model, 2) # train for 2 iterations

julia> model.root

train!(model, 1) # train for 1 more iteration

julia> model.root

Then we can replace the integer argument n in train!(model, n) with a number of more sophisticated controls by "lifting" the method train! to the IterationControl.train! method defined in this package:

using IterationControl
IterationControl.train!(model::SquareRooter, n) =  train!(model, n) # lifting

By definitiion, the lifted train! has the same functionality as the original one:

model = SquareRooter(9)
IterationControl.train!(model, 2)

julia> model.root

But now we can also do this:

julia> IterationControl.train!(model, Step(2), NumberLimit(3), Info(m->m.root));
[ Info: 3.4
[ Info: 3.00009155413138
[ Info: 3.0
[ Info: Stop triggered by NumberLimit(3) stopping criterion.

Here each control is repeatedly applied in sequence until one of them triggers a stop. The first control Step(2) says, "Train the model two more iterations"; the second asks, "Have I been applied 3 times yet?", signalling a stop (at the end of the current control cycle) if so; and the third logs the value of the function m -> m.root, evaluated on model, to Info. In this example only the second control can terminate model iteration.

If model admits a method returning a loss (in this case the difference between x and the square of root) then we can lift that method to IterationControl.loss to enable control using loss-based stopping criteria, such as a loss threshold. In the demonstation below, we also include a callback:

model = SquareRooter(4)
train!(model, 1)

julia> loss(model)

IterationControl.loss(model::SquareRooter) = loss(model) # lifting

losses = Float64[]
callback(model) = push!(losses, loss(model))

julia> IterationControl.train!(model,
[ Info: Stop triggered by Threshold(0.0001) stopping criterion.

julia> losses
2-element Array{Float64,1}:

In many appliations to machine learning, "loss" will be an out-of-sample loss, computed after some iterations. If model additionally generates user-inspectable "training losses" (one per iteration) then similarly lifting the appropriate access function to IterationControl.training_losses enables Prechelt's progress-modified generalization loss stopping criterion, PQ (see Table 1 below).

PQ is the only criterion from the EarlyStopping.jl package not otherwise enabled when IterationControl.loss is overloaded as above.

Reference. Prechelt, Lutz (1998): "Early Stopping - But When?", in Neural Networks: Tricks of the Trade, ed. G. Orr, Springer.

The interface just described is sufficient for controlling conventional machine learning models with an iteration parameter, as this tree boosting example shows.

Online and incremental training

For online or incremental training, lift the method for ingesting data into the model to IterationControl.ingest!(model, datum) and use the control Data(data). Here data is any iterator generating the datum items to be ingested (one per application of the control). By default, the Data control becomes passive after data is exhausted. Do ?Data for details. (See Access to model through a wrapper below on dealing with any model wrapping necessary to implement data ingestion.)

A simple particle tracking example is given here.

Verbose logging and inspecting control reports

The IterationControl.train! method can be given the keyword argument verbosity=..., defaulting to 1. The larger verbosity, the noisier.

The return value of IterationControl.train! is a tuple of (control, report) tuples, where report is generated by control at the end of training. For example, the final loss can be accessed from the report of the WithLossDo() control:

model = SquareRooter(9)
reports = IterationControl.train!(model, Step(1), WithLossDo(println), NumberLimit(3));

julia> last(reports[2])
(loss = 0.1417301038062284, done = false, log = "")

julia> last(reports[2]).loss

Controls provided

Controls are repeatedly applied in sequence until a control triggers a stop. Each control type has a detailed doc-string. sBelow is a short summary, with some advanced options omitted.

control description enabled if these are overloaded can trigger a stop notation in Prechelt
Step(n=1) Train model for n iterations train! no
Info(f=identity) Log to Info the value of f(model) train! no
Warn(predicate, f="") Log to Warn the value of f or f(model) if predicate(model) holds train! no
Error(predicate, f="") Log to Error the value of f or f(model) if predicate(model) holds and then stop train! yes
Callback(f=_->nothing) Call f(model) train! yes
TimeLimit(t=0.5) Stop after t hours train! yes
NumberLimit(n=100) Stop after n applications of the control train! yes
NumberSinceBest(n=6) Stop when best loss occurred n control applications ago train! yes
WithNumberDo(f=n->@info(n)) Call f(n + 1) where n is the number of complete control cycles so far train! yes
WithLossDo(f=x->@info("loss: $x")) Call f(loss) where loss is the current loss train!, loss yes
WithTrainingLossesDo(f=v->@info(v)) Call f(v) where v is the current batch of training losses train!, training_loss yes
InvalidValue() Stop when NaN, Inf or -Inf loss/training loss encountered train! yes
Threshold(value=0.0) Stop when loss < value train!, loss yes
GL(alpha=2.0) Stop after "Generalization Loss" exceeds alpha train!, loss yes GL_α
Patience(n=5) Stop after n consecutive loss increases train!, loss yes UP_s
PQ(alpha=0.75, k=5) Stop after "Progress-modified GL" exceeds alpha train!, loss, training_losses yes PQ_α
Data(data) Call ingest!(model, item) on the next item in the iterable data. train!, ingest! yes

Table 1. Atomic controls

Stopping option. All the following controls trigger a stop if the provided function f returns true and stop_if_true=true is specified in the constructor: Callback, WithNumberDo, WithLossDo, WithTrainingLossesDo.

There are also three control wrappers to modify a control's behavior:

wrapper description
IterationControl.skip(control, predicate=1) Apply control every predicate applications of the control wrapper (can also be a function; see doc-string)
IterationControl.louder(control, by=1) Increase the verbosity level of control by the specified value (negative values lower verbosity)
IterationControl.debug(control) Apply control but also log its state to Info (at any verbosity level)
IterationControl.composite(controls...) Apply each control in controls in sequence; mostly for under-the-hood use

Table 2. Wrapped controls

Access to model through a wrapper

Note that functions ordinarily applied to model by some control (e.g., a Callback) will instead be applied to IterationControl.expose(model) if IterationControl.expose is appropriately overloaded.

Implementing new controls

There is no abstract control type; any object can be a control. Behaviour is implemented using a functional style interface with four methods. Only the first two are compulsory (the done and takedown fallbacks always return false and NamedTuple() respectively.):

update!(control, model, verbosity, n) -> state  # initialization
update!(control, model, verbosity, n, state) -> state
done(control, state)::Bool
takedown(control, verbosity, state) -> human_readable_named_tuple

Here n is the control cycle count, i.e., one more than the the number of completed control cylcles.

Here's how IterationControl.train! calls these methods:

function train!(model, controls...; verbosity::Int=1)

    control = composite(controls...)

    # before training:
    verbosity > 1 && @info "Using these controls: $(flat(control)). "

    # first training event:
    n = 1 # counts control cycles
    state = update!(control, model, verbosity, n)
    finished = done(control, state)

    # subsequent training events:
    while !finished
        n += 1
        state = update!(control, model, verbosity, n, state)
        finished = done(control, state)

    # finalization:
    return takedown(control, verbosity, state)

Required Packages