Hyperparameter optimization algorithms for use in the MLJ machine learning framework
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Hyperparameter optimization for MLJ machine learning models.

See Tuning Models · MLJ for usage examples.

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Note: This component of the MLJ stack applies to MLJ versions 0.8.0 and higher. Prior to 0.8.0, tuning algorithms resided in MLJ.

Who is this repo for?

This repository is not intended to be directly imported by the general MLJ user. Rather, MLJTuning is a dependency of the MLJ machine learning platform, which allows MLJ users to perform a variety of hyperparameter optimization tasks from there.

MLJTuning is the place for developers to integrate hyperparameter optimization algorithms (here called tuning strategies) into MLJ, either by adding code to /src/strategies, or by importing MLJTuning into a third-party package and implementing MLJTuning's tuning strategy interface.

MLJTuning is a component of the MLJ stack which does not have MLJModels as a dependency (no ability to search and load registered MLJ models). It does however depend on MLJBase and, in particular, on the resampling functionality currently residing there.

What is provided here?

This repository contains:

  • a tuning wrapper called TunedModel for transforming arbitrary MLJ models into "self-tuning" ones - that is, into models which, when fit, automatically optimize a specified subset of the original hyperparameters (using cross-validation or other resampling strategy) before training the optimal model on all supplied data

  • an abstract tuning strategy interface to allow developers to conveniently implement common hyperparameter optimization strategies, such as:

    • search models generated by an arbitrary iterator, eg models = [model1, model2, ...] (built-in Explicit strategy)

    • grid search (built-in Grid strategy)

    • Latin hypercubes (built-in LatinHypercube strategy, interfacing the LatinHypercubeSampling package)

    • random search (built-in RandomSearch strategy)

    • particle swarm optimization (MLJParticleOptimization.jl)

    • bandit

    • simulated annealing

    • Bayesian optimization using Gaussian processes

    • structured tree Parzen estimators (MLJTreeParzenTuning from TreeParzen.jl)

    • multi-objective (Pareto) optimization

    • genetic algorithms

    • AD-powered gradient descent methods

  • a selection of implementations of the tuning strategy interface, currently all those accessible from MLJ itself.

  • the code defining the MLJ functions learning_curves! and learning_curve as these are essentially one-dimensional grid searches

How do I implement a new tuning strategy?

This document assumes familiarity with the Evaluating Model Performance and Performance Measures sections of the MLJ manual. Tuning itself, from the user's perspective, is described in Tuning Models.


What follows is an overview of tuning in MLJ. After the overview is an elaboration on those terms given in italics.

All tuning in MLJ is conceptualized as an iterative procedure, each iteration corresponding to a performance evaluation of a single model. Each such model is a mutated clone of a fixed prototype. In the general case, this prototype is a composite model, i.e., a model with other models as hyperparameters, and while the type of the prototype mutations is fixed, the types of the sub-models are allowed to vary.

When all iterations of the algorithm are complete, the optimal model is selected by applying a selection heuristic to a history generated according to the specified tuning strategy. Iterations are generally performed in batches, which are evaluated in parallel (sequential tuning strategies degenerating into semi-sequential strategies, unless the batch size is one). At the beginning of each batch, both the history and an internal state object are consulted, and, on the basis of the tuning strategy, a new batch of models to be evaluated is generated. On the basis of these evaluations, and the strategy, the history and internal state are updated.

The tuning algorithm initializes the state object before iterations begin, on the basis of the specific strategy and a user-specified range object.

  • Recall that in MLJ a model is an object storing the hyperparameters of some learning algorithm indicated by the name of the model type (e.g., DecisionTreeRegressor). Models do not store learned parameters.

  • An evaluation is an object E returned by some call to the evaluate! method, when passed the resampling strategy (e.g., resampling=CV(nfolds=9)) and a battery of user-specified performance measures (e.g., measures=[cross_entropy, accuracy]). An evaluation object E contains a list of measures E.measure and a list of corresponding measurements E.measurement, each of which is the aggregrate of measurements for each resampling of the data, which are stored in E.per_fold (a vector of vectors). In the case of a measure that reports a value for each individual observation (to obtain the per-fold measurement, by aggregation) the per-observation values can be retrieved from E.per_observation. This last object includes missing entries for measures that do not report per-observation values (reports_per_observation(measure) = false) such as auc. See Evaluating Model Performance for details. There is a trait for measures called orientation which is :loss for measures you ordinarily want to minimize, and :score for those you want to maximize. See Performance measures for further details.

  • A tuning strategy is an instance of some subtype S <: TuningStrategy, the name S (e.g., Grid) indicating the tuning (optimization) algorithm to be applied. The fields of the tuning strategy - called tuning hyperparameters - are those tuning parameters specific to the strategy that do not refer to specific models or specific model hyperparameters. So, for example, a default resolution to be used in a grid search is a hyperparameter of Grid, but the resolution to be applied to a specific hyperparameter (such as the maximum depth of a decision tree) is not. This latter parameter would be part of the user-specified range object. A multi-objective tuning strategy is one that consults the measurements of all measures specified by the user; otherwise only the first measure is consulted, although measurements for all measures are nevertheless reported.

  • A selection heuristic is a rule describing how the outcomes of the model evaluations will be used to select the best (optimal) model, after all iterations of the optimizer have concluded. For example, the default NaiveSelection() heuristic simply selects the model whose evaluation E has the smallest or largest E.measurement[1] value, according to whether the metric E.measure[1] is a :loss or :score. Most heuristics are generic in the sense they will apply no matter what tuning strategy is applied. A selection heuristic supported by a multi-objective tuning strategy must select some "best" model (e.g., a random Pareto optimal solution).

  • The history is a vector of identically-keyed named tuples, one tuple per iteration. The tuple keys include:

    • model: for the MLJ model instance that has been evaluated

    • measure, measurement, per_fold: for storing the values of E.measure, E.measurement and E.per_fold, where E is the corresponding evaluation object.

    • metadata: for any tuning strategy-specific information required to be recorded in the history but not intended to be reported to the user (for example an implementation-specific representation of model).

    There may be additional keys for tuning-specific information that is to be reported to the user (such as temperature in simulated annhealing).

  • A range is any object whose specification completes the specification of the tuning task, after the prototype, tuning strategy, resampling strategy, performance measure(s), selection heuristic, and total iteration count are given. This definition is intentionally broad and the interface places no restriction on the allowed types of this object, although all strategies should support the one-dimensional range objects defined in MLJBase (see below). Generally, a range may be understood as the "space" of models being searched plus strategy-specific data explaining how models from that space are actually to be generated (e.g., grid resolutions or probability distributions specific to particular hyper-parameters). For more on range types see Range types below.

Interface points for user input

Recall, for context, that in MLJ tuning is implemented as a model wrapper. A model is tuned by fitting the wrapped model to data (which also trains the optimal model on all available data). This process determines the optimal model, as defined by the selection heuristic (see above). To use the optimal model one predicts using the wrapped model. For more detail, see the Tuning Models section of the MLJ manual.

In setting up a tuning task, the user constructs an instance of the TunedModel wrapper type, which has these principal fields:

  • model: the prototype model instance mutated during tuning (the model being wrapped)

  • tuning: the tuning strategy, an instance of a concrete TuningStrategy subtype, such as Grid

  • resampling: the resampling strategy used for performance evaluations, which must be an instance of a concrete ResamplingStrategy subtype, such as Holdout or CV

  • measure: a measure (loss or score) or vector of measures available to the tuning algorithm, the first of which is optimized in the common case of single-objective tuning strategies

  • selection_heuristic: some instance of SelectionHeuristic, such as NaiveSelection() (default)

  • range: as defined above - roughly, the space of models to be searched

  • n: the number of iterations, which is the number of distinct model evaluations that will be added to the history, unless the tuning strategy's supply of models is exhausted (e.g., Grid). This is not to be confused with an iteration count specific to the tuning strategy (e.g., Particle Swarm Optimization).

  • acceleration: the computational resources to be applied (e.g., CPUProcesses() for distributed computing and CPUThreads() for multi-threaded processing)

  • acceleration_resampling: the computational resources to be applied at the level of resampling (e.g., in cross-validation)

Implementation requirements for new tuning strategies

As sample implementations, see /src/strategies/

Summary of functions

Several functions are part of the tuning strategy API:

  • clean!: for validating and resetting invalid fields in tuning strategy (optional)

  • setup: for initialization of state (compulsory)

  • extras: for declaring and formatting additional user-inspectable information going into the history

  • tuning_report: for declaring any other strategy-specific information to report to the user (optional)

  • models: for generating batches of new models and updating the state (compulsory)

  • default_n: to specify the total number of models to be evaluated when n is not specified by the user

  • supports_heuristic: a trait used to encode which selection heuristics are supported by the tuning strategy (only needed if you define a strategy-specific heuristic)

Important note on the history. The initialization and update of the history is carried out internally, i.e., is not the responsibility of the tuning strategy implementation. The history is always initialized to nothing, rather than an empty vector.

The above functions are discussed further below, after discussing types.

The tuning strategy type

Each tuning algorithm must define a subtype of TuningStrategy whose fields are the hyperparameters controlling the strategy that do not directly refer to models or model hyperparameters. These would include, for example, the default resolution of a grid search, or the initial temperature in simulated annealing.

The algorithm implementation must include a keyword constructor with defaults. Here's an example:

mutable struct Grid <: TuningStrategy

# Constructor with keywords
Grid(; goal=nothing, resolution=10, shuffle=true,
	 rng=Random.GLOBAL_RNG) =
	Grid(goal, resolution, MLJBase.shuffle_and_rng(shuffle, rng)...)

Range types

Generally new types are defined for each class of range object a tuning strategy should like to handle, and the tuning strategy functions to be implemented are dispatched on these types. It is recommended that every tuning strategy support at least these types:

  • one-dimensional ranges r, where r is a MLJBase.ParamRange instance

  • (optional) pairs of the form (r, data), where data is extra hyper-parameter-specific information, such as a resolution in a grid search, or a distribution in a random search

  • abstract vectors whose elements are of the above form

Recall that ParamRange has two concrete subtypes NumericRange and NominalRange, whose instances are constructed with the MLJBase extension to the range function.

Note in particular that a NominalRange has a values field, while NumericRange has the fields upper, lower, scale, unit and origin. The unit field specifies a preferred length scale, while origin a preferred "central value". These default to (upper - lower)/2 and (upper + lower)/2, respectively, in the bounded case (neither upper = Inf nor lower = -Inf). The fields origin and unit are used in generating grids or fitting probability distributions to unbounded ranges.

A ParamRange object is always associated with the name of a hyperparameter (a field of the prototype in the context of tuning) which is recorded in its field attribute, a Symbol, but for composite models this might be a be an Expr, such as :(atom.max_depth).

Use the iterator and sampler methods to convert ranges into one-dimensional grids or for random sampling, respectively. See the tuning section of the MLJ manual or doc-strings for more on these methods and the Grid and RandomSearch implementations.

The clean! method: For validating tuning strategy


Some tuning strategies have mutable fields that only support specific set of values: a particle swarm strategy, for instance, should have at least three agents for the algorithm to work. As such, it is recommended to implement the clean! method to warn the user and correct invalid tuning hyperparameters. The method should return a string message if some fields have been reset or an empty string otherwise, and will be called internally whenever a TunedModel machine is fit!.

The default fallback for clean! returns an empty string.

The setup method: To initialize state

state = setup(tuning::MyTuningStrategy, model, range, n, verbosity)

The setup function is for initializing the state of the tuning algorithm (available to the models method). Be sure to make this object mutable if it needs to be updated by the models method. The arguments model and n are what the user has specified in their TunedModel instance; recall model is the prototype to be cloned and mutated, and n the total number of mutations to be generated.

The state is a place to record the outcomes of any necessary intialization of the tuning algorithm (performed by setup) and a place for the models method to save and read transient information that does not need to be recorded in the history.

The setup function is called once only, when a TunedModel machine is fit! the first time, and not on subsequent calls (unless force=true). (Specifically,, ...) calls setup but MLJBase.update(::TunedModel, ...) does not.)

The verbosity is an integer indicating the level of logging: 0 means logging should be restricted to warnings, -1, means completely silent.

The fallback for setup is:

setup(tuning::TuningStrategy, model, range, n, verbosity) = range

However, a tuning strategy will generally want to implement a setup method for each range type it is going to support:

MLJTuning.setup(tuning::MyTuningStrategy, model, range::RangeType1, n, verbosity) = ...
MLJTuning.setup(tuning::MyTuningStrategy, model, range::RangeType2, n, verbosity) = ...

The extras method: For adding user-inspectable data to the history

MLJTuning.extras(tuning::MyTuningStrategy, history, state, E) -> named_tuple

This method should return any user-inspectable information to be included in a new history entry, that is in addition to the model, measures, measurement and per_fold data. This method must return a named tuple, human readable if possible. Each key of the returned named tuple becomes a key of the new history entry.

Here E is the full evalutation object for model and history the current history (before adding the new entry).

The fallback for extras returns an empty named tuple.

The models method: For generating model batches to evaluate

MLJTuning.models(tuning::MyTuningStrategy, model, history, state, n_remaining, verbosity)
	-> vector_of_models, new_state

This is the core method of a new implementation. Given the existing history and state, it must return a vector ("batch") of new model instances vector_of_models to be evaluated, and the updated state. Any number of models may be returned (and this includes an empty vector or nothing) and the evaluations will be performed in parallel (using the mode of parallelization defined by the acceleration field of the TunedModel instance).

Important note. Parallelization means the order in which the history gets extended after models(...) returns its list of new candidates is generally not the same order in which the candidates are returned. Some implementations may therefore need to attach extra "labeling" metadata to each model, as explained below, so that the existing history can be suitably interpreted.

If more models are returned than needed (because including them would create a history whose length exceeds the user-specified number of iterations tuned_model.n) then the surplus models are saved, for use in a "warm restart" of tuning, when the user increases tuned_model.n. The remaining models are then evaluated and these evaluations are added to the history. In any warm restart, no new call to models will be made until all saved models have been evaluated, and these evaluations added to the history.

If the tuning algorithm exhausts it's supply of new models (because, for example, there is only a finite supply, as in a Grid search) then vector_of_models should be an empty vector or nothing. The interface has no fixed "batch-size" parameter, and the tuning algorithm is happy to receive any number of models; a surplus is handled as explained above, a shortfall will trigger an early stop (so that the final history has length less than tuned_model.n).

If needed, extra metadata may be attached to each model returned; see below.

Sequential tuning strategies generating models non-deterministically (e.g., simulated annealing) might choose to include a batch size hyperparameter, and arrange that models returns batches of the specified size (to be evaluated in parallel when acceleration is set appropriately). However, the evaluations and history updates do not occur until after the models call, so it may be complicated or impossible to preserve the original (strictly) sequential algorithm in that case, which should be clearly documented.

Some simple tuning strategies, such as RandomSearch, will want to return as many models as possible in one hit. To this end, the variable n_remaining is passed to the models call; this is the difference between the current length of the history and tuned_model.n.

Including model metadata

If a tuning strategy implementation needs to record additional metadata in the history, for each model generated, then instead of model instances, vector_of_models should be vector of tuples of the form (m, metadata), where m is a model instance, and metadata the associated data. To access the metadata for the jth element of the existing history, use history[j].metadata.

The tuning_report method: To add to the user-inspectable report

As with any model, fitting a TunedModel instance generates a user-accessible report. Note that the fallback report already includes additions to the history created by the extras method mentioned above. To add more strategy-specific information to the report, one overloads tuning_report.

Specically, the report generated by fitting a TunedModel is constructed with this code:

report1 = (best_model         = best_model,
		   best_history_entry = best_user_history_entry,
		   history            = user_history,
		   best_report        = best_report)

report = merge(report1, tuning_report(tuning, history, state))


  • best_model is the best model instance (as selected according to the user-specified selection heuristic).

  • best_user_history is the corresponding entry in the history with metadata removed.

  • best_report is the report generated when fitting the best_model on all available data.

  • user_history is the full history with metadata entries removed.

  • tuning_report(::MyTuningStrategy, ...) is a method the implementer may overload that ***must return a named tuple, preferably human readable

The fallback for tuning_report returns an empty named-tuple.

The default_n method: For declaring the default number of iterations

MLJTuning.default_n(tuning::MyTuningStrategy, range)

The models method, which is allowed to return multiple models in it's first return value vector_of_models, is called until one of the following occurs:

  • The length of the history matches the number of iterations specified by the user, namely tuned_model.n where tuned_model is the user's TunedModel instance. If tuned_model.n is nothing (because the user has not specified a value) then default_n(tuning, range) is used instead.

  • vector_of_models is empty or nothing.

The fallback is

default_n(tuning::TuningStrategy, range) = DEFAULT_N

where DEFAULT_N is a global constant. Do using MLJTuning; MLJTuning.DEFAULT_N to see check the current value.

The supports_heuristic trait

If you define a selection heuristic SpecialHeuristic (see below) and that heuristic is specific to a tuning strategy TuningStrategy then you must define

MLJTuning.supports_heuristic(::TuningStrategy, ::SpecialHeuristic) = true

Sample implementations

A number of built-in tuning strategy implementations of the MLJTuning API can be found at /src/strategies.

The simplest Explicit strategy is the simplest but is also an odd case as the range is just an iterator of MLJ models. These models need not share a common type and model is never cloned.

For slightly less trivial example, see the Grid search code

How do I implement a new selection heuristic?

Recall that a selection heuristic is a rule which decides on the "best model" given the model evaluations in the tuning history. New heuristics are introduced by defining a new struct SomeHeuristic subtyping SelectionHeuristic and implementing a method, history) -> history_entry

where history_entry is the entry in the history corresponding to the model deemed "best".

Below is a simplified version of code defining the default heuristic NaiveSelection() which simply chooses the model with the lowest (or highest) aggregated performance estimate, based on the first measure specified by the user in his TunedModel construction (she may specify more than one).

struct NaiveSelection <: MLJTuning.SelectionHeuristic end

function best(heuristic::NaiveSelection, history)
	measurements = [h.measurement[1] for h in history]
	measure = first(history).measure[1]
	if orientation(measure) == :score
		measurements = -measurements
	best_index = argmin(measurements)
	return history[best_index]

Because this selection heuristic is generic (applies to all tuning strategies) we additionally define

MLJTuning.supports_heuristic(strategy, heuristic::NaiveSelection) = true

For strategy-specific selection heuristics, see above on how to set this trait.