Hyperparameter optimization in Julia.
Author baggepinnen
88 Stars
Updated Last
2 Years Ago
Started In
August 2018


Build Status codecov.io

A package to perform hyperparameter optimization. Currently supports random search, latin hypercube sampling and Bayesian optimization.


This package was designed to facilitate the addition of optimization logic to already existing code. I usually write some code and try a few hyper parameters by hand before I realize I have to take a more structured approach to finding good hyper parameters. I therefore designed this package such that the optimization logic is wrapped around existing code, and the user only has to specify which variables to optimize and candidate values (ranges) for these variables.

High-level example

In order to add hyper-parameter optimization to the existing pseudo code

a = manually_selected_value
b = other_value
cost = train_model(a,b)

we wrap it in @hyperopt like this

ho = @hyperopt for i = number_of_samples,
                   a = candidate_values,
                   b = other_candidate_values,
cost = train_model(a,b)


  1. The macro @hyperopt takes a for-loop with an initial argument determining the number of samples to draw (i below).
  2. The sample strategy can be specified by specifying the special keyword sampler = Sampler(opts...). Available options are RandomSampler(), LHSampler(), CLHSampler(dims=[Continuous(), Categorical(2), Continuous(), ...]), Hyperband(R=50, η=3, inner=RandomSampler()) and GPSampler(Min)/GPSampler(Max).
  3. The subsequent arguments to the for-loop specifies names and candidate values for different hyper parameters (a = LinRange(1,2,1000), b = [true, false], c = exp10.(LinRange(-1,3,1000)) below).
  4. A useful strategy to achieve log-uniform sampling is logarithmically spaced vector, e.g. c = exp10.(LinRange(-1,3,1000)).
  5. In the example below, the parameters i,a,b,c can be used within the expression sent to the macro and they will hold a new value sampled from the corresponding candidate vector each iteration.

The resulting object ho::Hyperoptimizer holds all the sampled parameters and function values and has minimum/minimizer and maximum/maximizer properties (e.g., ho.minimizer). It can also be plotted using plot(ho) (uses Plots.jl). The exact syntax to use for various samplers is shown in the testfile, which should be fairly readable.

Full example

using Hyperopt

f(x,a,b=true;c=10) = sum(@. x + (a-3)^2 + (b ? 10 : 20) + (c-100)^2) # Function to minimize

# Main macro. The first argument to the for loop is always interpreted as the number of iterations
ho = @hyperopt for i=50,
            sampler = RandomSampler(), # This is default if none provided
            a = LinRange(1,5,1000),
            b = [true, false],
            c = exp10.(LinRange(-1,3,1000))
   print(i, "\t", a, "\t", b, "\t", c, "   \t")
   x = 100
   @show f(x,a,b,c=c)
1   3.910910910910911   false   0.15282140360258697     f(x, a, b, c=c) = 10090.288832348499
2   3.930930930930931   true    6.1629662551329405      f(x, a, b, c=c) = 8916.255534433481
3   2.7617617617617616  true    146.94918006248173      f(x, a, b, c=c) = 2314.282265997491
4   3.6666666666666665  false   0.3165924111983522      f(x, a, b, c=c) = 10057.226192959602
5   4.783783783783784   true    34.55719936762139       f(x, a, b, c=c) = 4395.942039196544
6   2.5895895895895897  true    4.985373463873895       f(x, a, b, c=c) = 9137.947692504491
7   1.6206206206206206  false   301.6334347259197       f(x, a, b, c=c) = 40777.94468684398
8   1.012012012012012   true    33.00034791125285       f(x, a, b, c=c) = 4602.905476253546
9   3.3583583583583585  true    193.7703337477989       f(x, a, b, c=c) = 8903.003911886599
10  4.903903903903904   true    144.26439512181574      f(x, a, b, c=c) = 2072.9615255755252
11  2.2332332332332334  false   119.97177354358843      f(x, a, b, c=c) = 519.4596697509966
12  2.369369369369369   false   117.77987011971193      f(x, a, b, c=c) = 436.52147646611473
13  3.2182182182182184  false   105.44427935261685      f(x, a, b, c=c) = 149.68779686009242

  iterations: Int64 50
  params: Tuple{Symbol,Symbol,Symbol}
  candidates: Array{AbstractArray{T,1} where T}((3,))
  history: Array{Any}((50,))
  results: Array{Any}((50,))
  sampler: Hyperopt.RandomSampler

julia> best_params, min_f = ho.minimizer, ho.minimum
(Real[1.62062, true, 100.694], 112.38413353985818)

julia> printmin(ho)
a = 1.62062
b = true
c = 100.694

We can also visualize the result by plotting the hyperoptimizer



This may allow us to determine which parameters are most important for the performance etc.

The type Hyperoptimizer is iterable, it iterates for the specified number of iterations, each iteration providing a sample of the parameter vector, e.g.

ho = Hyperoptimizer(10, a = LinRange(1,2,50), b = [true, false], c = randn(100))
for (i,a,b,c) in ho
    println(i, "\t", a, "\t", b, "\t", c)

1   1.2244897959183674  false   0.8179751164732062
2   1.7142857142857142  true    0.6536272580487854
3   1.4285714285714286  true    -0.2737451706680355
4   1.6734693877551021  false   -0.12313108128547606
5   1.9795918367346939  false   -0.4350837079334295
6   1.0612244897959184  true    -0.2025613848798039
7   1.469387755102041   false   0.7464858339748051
8   1.8571428571428572  true    -0.9269021128132274
9   1.163265306122449   true    2.6554272337516966
10  1.4081632653061225  true    1.112896676939024

If used in this way, the hyperoptimizer can not keep track of the function values like it did when @hyperopt was used. To manually store the same data, consider a pattern like

ho = Hyperoptimizer(10, a = LinRange(1,2), b = [true, false], c = randn(100))
for (i,a,b,c) in ho
    res = computations(a,b,c)
    push!(ho.history, [a,b,c])

Categorical variables

RandomSampler and CLHSampler support categorical variables which do not have a natural floating point representation, such as functions:

@hyperopt for i=20, fun = [tanh, σ, relu]
# or
@hyperopt for i=20, sampler=CLHSampler(dims=[Categorical(3), Continuous()]),
                    fun   = [tanh, σ, relu],
                    param = LinRange(0,1,20)
    train_network(fun, param)

Which sampler to use?

RandomSampler is a good baseline and the default if none is chosen. GPSampler fits a Gaussian process to the data and tries to use this model to figure out where the best point to sample next is (using expected improvement). This is somewhat expensive and pays off when the function to optimize is expensive. Hyperband(R=50, η=3, inner=RandomSampler()) runs the expression with varying amount of resources, allocating more resources to promising hyperparameters. See below for more info on Hyperband.

If number of iterations is small, LHSampler work better than random search. Caveat: LHSampler needs all candidate vectors to be of equal length, i.e.,

hob = @hyperopt for i=100, sampler = LHSampler(),
                            a = LinRange(1,5,100),
                            b = repeat([true, false],50),
                            c = exp10.(LinRange(-1,3,100))

where all candidate vectors are of length 100. The candidates for b thus had to be repeated 50 times.

The categorical CLHSampler circumvents this

hob = @hyperopt for i=100,
                    sampler=CLHSampler(dims=[Continuous(), Categorical(2), Continuous()]),
                    a = LinRange(1,5,100),
                    b = [true, false],
                    c = exp10.(LinRange(-1,3,100))


Hyperband(R=50, η=3, inner=RandomSampler()) Implements Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. The maximum amount of resources is given by R and the parameter η roughly determines the proportion of trials discarded between each round of successive halving. When using Hyperband the expression inside the @hyperopt macro takes the following form

ho = @hyperopt for i=18, sampler=Hyperband(R=50, η=3, inner=RandomSampler()), a = LinRange(1,5,1800), c = exp10.(LinRange(-1,3,1800))
    if state === nothing # Query if state is initialized
        res = optimize(resources=i, a, b) # if state is uninitialized, start a new optimization using the selected hyper parameters
        res = optimize(resources=i, state=state) # If state has a value, continue the optimization from the state
    minimum(res), get_state(res) # return the minimum value and a state from which to continue the optimization

a (simple) working example using Hyperband and Optim is

using Optim
f(a;c=10) = sum(@. 100 + (a-3)^2 + (c-100)^2)
hohb = @hyperopt for i=18, sampler=Hyperband(R=50, η=3, inner=RandomSampler()), a = LinRange(1,5,1800), c = exp10.(LinRange(-1,3,1800))
    if !(state === nothing)
        a,c = state
    res = Optim.optimize(x->f(x[1],c=x[2]), [a,c], SimulatedAnnealing(), Optim.Options(f_calls_limit=i))
    Optim.minimum(res), Optim.minimizer(res)

and a more complicated example that also explores different Optim optimizers as the inner optimizer is

hohb = @hyperopt for i=18, sampler=Hyperband(R=50, η=3, inner=RandomSampler()),
    algorithm = [SimulatedAnnealing(), ParticleSwarm(), NelderMead(), BFGS(), NewtonTrustRegion()],
    a = LinRange(1,5,1800),
    c = exp10.(LinRange(-1,3,1800))
    if !(state === nothing)
        x0,algorithm = state
        x0 = [a,c]
    println(i, " algorithm: ", typeof(algorithm).name.name)
    res = Optim.optimize(x->f(x[1],c=x[2]), x0, algorithm, Optim.Options(time_limit=i+1, show_trace=false))
    Optim.minimum(res), (Optim.minimizer(res), algorithm)

Parallel execution

  • The macro @phyperopt works in the same way as @hyperopt but distributes all computation on available workers. The usual caveats apply, code must be loaded on all workers etc.
  • The macro @thyperopt uses ThreadPools.tmap to evaluate the objective on all available threads. Beware of high memory consumption if your objective allocates a lot of memory.

Used By Packages

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