1. DeepHyper 101

In this tutorial, we present the basics of DeepHyper.

Let us start with installing DeepHyper!

try:
    import deephyper
    print(deephyper.__version__)
except (ImportError, ModuleNotFoundError):
    !pip install deephyper

0.6.0

1.1. Optimization Problem

In the definition of our optimization problem we have two components:

1. black-box function that we want to optimize

2. the search space of input variables

1.1.1. Black-Box Function

DeepHyper is developed to optimize black-box functions. Here, we define the function \(f(x) = - x ^ 2\) that we want to maximise (the maximum being \(f(x=0) = 0\) on \(I_x = [-10;10]\)). The black-box function f takes as input a config dictionary from which we retrieve the variables of interest.

def f(job):
    return -job.parameters["x"] ** 2

1.1.2. Search Space of Input Variables

In this example, we have only one variable \(x\) for the black-box functin \(f\). We empirically decide to optimize this variable \(x\) on the interval \(I_x = [-10;10]\). To do so we use the HpProblem from DeepHyper and add a real hyperparameter by using a tuple of two floats.

from deephyper.problem import HpProblem


problem = HpProblem()

# Define the variable you want to optimize
problem.add_hyperparameter((-10.0, 10.0), "x")

problem

Configuration space object:
  Hyperparameters:
    x, Type: UniformFloat, Range: [-10.0, 10.0], Default: 0.0

1.2. Evaluator Interface

DeepHyper uses an API called Evaluator to distribute the computation of black-box functions and adapt to different backends (e.g., threads, processes, MPI, Ray). An Evaluator object wraps the black-box function f that we want to optimize. Then a method parameter is used to select the backend and method_kwargs defines some available options of this backend.

Tip

The method="thread" provides parallel computation only if the black-box is releasing the global interpretor lock (GIL). Therefore, if you want parallelism in Jupyter notebooks you should use the Ray evaluator (method="ray") after installing Ray with pip install ray.

It is possible to define callbacks to extend the behaviour of Evaluator each time a function-evaluation is launched or completed. In this example we use the TqdmCallback to follow the completed evaluations and the evolution of the objective with a progress-bar.

from deephyper.evaluator import Evaluator
from deephyper.evaluator.callback import TqdmCallback


# define the evaluator to distribute the computation
evaluator = Evaluator.create(
    f,
    method="thread",
    method_kwargs={
        "num_workers": 4,
        "callbacks": [TqdmCallback()]
    },
)

print(f"Evaluator has {evaluator.num_workers} available worker{'' if evaluator.num_workers == 1 else 's'}")

Evaluator has 4 available workers

1.3. Search Algorithm

The next step is to define the search algorithm that we want to use. Here, we choose CBO (Centralized Bayesian Optimization) which is a sampling based Bayesian optimization strategy. This algorithm has the advantage of being asynchronous thanks to a constant liar strategy which is crutial to keep a good utilization of the resources when the number of available workers increases.

from deephyper.search.hps import CBO

# define your search
search = CBO(
    problem,
    evaluator,
    acq_func="UCB",  # Acquisition function to Upper Confidence Bound
    multi_point_strategy="qUCB",  # Fast Multi-point strategy with q-Upper Confidence Bound
    n_jobs=2,  # Number of threads to fit surrogate models in parallel
)

Then, we can execute the search for a given number of iterations by using the search.search(max_evals=...). It is also possible to use the timeout parameter if one needs a specific time budget (e.g., restricted computational time in machine learning competitions, allocation time in HPC).

results = search.search(max_evals=100)

Finally, let us visualize the results. The search(...) returns a DataFrame also saved locally under results.csv (in case of crash we don’t want to lose the possibly expensive evaluations already performed).

The DataFrame contains as columns: 1. the optimized hyperparameters: such as x in our case. 2. the objective maximised which directly match the results of the \(f\)-function in our example. 3. the job_id of each evaluated function (increased incrementally following the order of created evaluations). 4. the time of creation/collection of each task timestamp_submit and timestamp_gather respectively (in secondes, since the creation of the Evaluator).

results

	p:x	objective	job_id	m:timestamp_submit	m:timestamp_gather
0	5.158482	-2.660994e+01	1	5.408207	5.410271
1	6.138799	-3.768485e+01	3	5.408318	5.449186
2	3.312879	-1.097517e+01	2	5.408243	5.450632
3	2.589174	-6.703821e+00	0	5.408132	5.452201
4	-1.399978	-1.959937e+00	4	5.590530	5.593016
...	...	...	...	...	...
95	-0.000188	-3.515852e-08	94	12.333142	12.336986
96	0.000442	-1.957323e-07	96	12.601563	12.602302
97	-0.001094	-1.196771e-06	97	12.601587	12.603551
98	-0.001094	-1.196771e-06	98	12.601603	12.604581
99	-0.001094	-1.196771e-06	99	12.601617	12.605465

100 rows × 5 columns

We can also plot the evolution of the objective to verify that we converge correctly toward \(0\).

from deephyper.analysis.hpo import plot_search_trajectory_single_objective_hpo
import matplotlib.pyplot as plt

fig, ax = plot_search_trajectory_single_objective_hpo(results)
plt.title("Search Trajectory")
plt.show()