Early stopping criteria for loss-generating iterative algorithms
Author JuliaAI
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Updated Last
1 Year Ago
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February 2021


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A small package for applying early stopping criteria to loss-generating iterative algorithms, with a view to training and optimizing machine learning models.

The basis of IterationControl.jl, a package externally controlling iterative algorithms.

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


using Pkg

Sample usage

The EarlyStopper objects defined in this package consume a sequence of numbers called losses generated by some external algorithm - generally the training loss or out-of-sample loss of some iterative statistical model - and decide when those losses have dropped sufficiently to warrant terminating the algorithm. A number of commonly applied stopping criteria, listed under Criteria below, are provided out-of-the-box.

Here's an example of using an EarlyStopper object to check against two of these criteria (either triggering the stop):

using EarlyStopping

stopper = EarlyStopper(Patience(2), InvalidValue()) # multiple criteria
done!(stopper, 0.123) # false
done!(stopper, 0.234) # false
done!(stopper, 0.345) # true

julia> message(stopper)
"Early stop triggered by Patience(2) stopping criterion. "

One may force an EarlyStopper to report its evolving state:

losses = [10.0, 11.0, 10.0, 11.0, 12.0, 10.0];
stopper = EarlyStopper(Patience(2), verbosity=1);

for loss in losses
    done!(stopper, loss) && break
[ Info: loss: 10.0       state: (loss = 10.0, n_increases = 0)
[ Info: loss: 11.0       state: (loss = 11.0, n_increases = 1)
[ Info: loss: 10.0       state: (loss = 10.0, n_increases = 0)
[ Info: loss: 11.0       state: (loss = 11.0, n_increases = 1)
[ Info: loss: 12.0       state: (loss = 12.0, n_increases = 2)

The "object-oriented" interface demonstrated here is not code-optimized but will suffice for the majority of use-cases. For performant code, use the functional interface described under Implementing new criteria below.


To list all stopping criterion, do subtypes(StoppingCriterion). Each subtype T has a detailed doc-string queried with ?T at the REPL. Here is a short summary:

criterion description notation in Prechelt
Never() Never stop
InvalidValue() Stop when NaN, Inf or -Inf encountered
TimeLimit(t=0.5) Stop after t hours
NumberLimit(n=100) Stop after n loss updates (excl. "training losses")
NumberSinceBest(n=6) Stop after n loss updates (excl. "training losses")
Threshold(value=0.0) Stop when loss < value
GL(alpha=2.0) Stop after "Generalization Loss" exceeds alpha GL_α
PQ(alpha=0.75, k=5) Stop after "Progress-modified GL" exceeds alpha PQ_α
Patience(n=5) Stop after n consecutive loss increases UP_s
Disjunction(c...) Stop when any of the criteria c apply

Criteria tracking both training and out-of-sample losses

For criteria tracking both an "out-of-sample" loss and a "training" loss (eg, stopping criterion of type PQ), specify training=true if the update is for training, as in

done!(stopper, 0.123, training=true)

In these cases, the out-of-sample update must always come after the corresponding training update. Multiple training updates may precede the out-of-sample update, as in the following example:

criterion = PQ(alpha=2.0, k=2)
needs_training_losses(criterion) # true

stopper = EarlyStopper(criterion)

done!(stopper, 9.5, training=true) # false
done!(stopper, 9.3, training=true) # false
done!(stopper, 10.0) # false

done!(stopper, 9.3, training=true) # false
done!(stopper, 9.1, training=true) # false
done!(stopper, 8.9, training=true) # false
done!(stopper, 8.0) # false

done!(stopper, 8.3, training=true) # false
done!(stopper, 8.4, training=true) # false
done!(stopper, 9.0) # true

Important. If there is no distinction between in and out-of-sample losses, then any criterion can be applied, and in that case training=true is never specified (regardless of the actual interpretation of the losses being tracked).

Stopping times

To determine the stopping time for an iterator losses, use stopping_time(criterion, losses). This is useful for debugging new criteria (see below). If the iterator terminates without a stop, 0 is returned.

julia> stopping_time(InvalidValue(), [10.0, 3.0, Inf, 4.0])

julia> stopping_time(Patience(3), [10.0, 3.0, 4.0, 5.0], verbosity=1)
[ Info: loss updates: 1
[ Info: state: (loss = 10.0, n_increases = 0)
[ Info: loss updates: 2
[ Info: state: (loss = 3.0, n_increases = 0)
[ Info: loss updates: 3
[ Info: state: (loss = 4.0, n_increases = 1)
[ Info: loss updates: 4
[ Info: state: (loss = 5.0, n_increases = 2)

If the losses include both training and out-of-sample losses as described above, pass an extra Bool vector marking the training losses with true, as in

              [0.123, 0.321, 0.52, 0.55, 0.56, 0.58],
              [true, true, false, true, true, false])

Implementing new criteria

To implement a new stopping criterion, one must:

  • Define a new struct for the criterion, which must subtype StoppingCriterion.

  • Overload methods update and done for the new type.

  • Optionally overload methods message.

  • Optionally overload update_training and the trait needs_training_losses.

We demonstrate this with a simplified version of the code for Patience:

Defining the new type

using EarlyStopping

mutable struct Patience <: StoppingCriterion
Patience(; n=5) = Patience(n)

Overloading update and done

All information to be "remembered" must passed around in an object called state below, which is the return value of update (and update_training). The update function has two methods - one for initialization, without a state argument, and one for all subsequent loss updates, which requires the state returned by the preceding update (or update_training) call:

import EarlyStopping: update, done

update(criterion::Patience, loss) = (loss=loss, n_increases=0) # state

function update(criterion::Patience, loss, state)
    old_loss, n = state
    if loss > old_loss
        n += 1
        n = 0
    return (loss=loss, n_increases=n) # state

The done method returns true or false depending on the state:

done(criterion::Patience, state) = state.n_increases == criterion.n

Optional methods

The final message of an EarlyStopper is generated by a message method for StoppingCriterion. Here is the fallback (which does not use state):

EarlyStopping.message(criteria::StoppingCriterion, state)
    = "Early stop triggered by $criterion stopping criterion. "

The optional update_training methods (two for each criterion) have the same signature as the update methods above. Refer to the PQ code for an example.

If a stopping criterion requires one or more update_training calls per update call to work, you should overload the trait needs_training_losses for that type, as in this example from the source code:

EarlyStopping.needs_training_losses(::Type{<:PQ}) = true

Required Packages

No packages found.