hypertunity

Quick start

Define the objective function to optimise. For example, it can take the hyperparameters params as input and return a raw value score as output:

import hypertunity as ht

def foo(**params) -> float:
    # do some very costly computations
    ...
    return score

To define the valid ranges for the values of params we create a Domain object:

domain = ht.Domain({
    "x": [-10., 10.],         # continuous variable within the interval [-10., 10.]
    "y": {"opt1", "opt2"},    # categorical variable from the set {"opt1", "opt2"}
    "z": set(range(4))        # discrete variable from the set {0, 1, 2, 3}
})

Then we set up the optimiser:

bo = ht.BayesianOptimisation(domain=domain)

And we run the optimisation for 10 steps. Each result is used to update the optimiser so that informed domain samples are drawn:

n_steps = 10
for i in range(n_steps):
    samples = bo.run_step(batch_size=2, minimise=True)      # suggest next samples
    evaluations = [foo(**s.as_dict()) for s in samples]     # evaluate foo
    bo.update(samples, evaluations)                         # update the optimiser

Finally, we visualise the results in Tensorboard:

import hypertunity.reports.tensorboard as tb

results = tb.Tensorboard(domain=domain, metrics=["score"], logdir="path/to/logdir")
results.from_history(bo.history)