syne_tune.optimizer.schedulers.neuralbands package

class syne_tune.optimizer.schedulers.neuralbands.NeuralbandScheduler(config_space, gamma=0.01, nu=0.01, step_size=30, max_while_loop=100, **kwargs)

Bases: NeuralbandSchedulerBase

NeuralBand is a neural-bandit based HPO algorithm for the multi-fidelity setting. It uses a budget-aware neural network together with a feedback perturbation to efficiently explore the input space across fidelities. NeuralBand uses a novel configuration selection criterion to actively choose the configuration in each trial and incrementally exploits the knowledge of every past trial.

Parameters:

• config_space (Dict) –

• gamma (float) – Control aggressiveness of configuration selection criterion

• nu (float) – Control aggressiveness of perturbing feedback for exploration

• step_size (int) – How many trials we train network once

• max_while_loop (int) – Maximal number of times we can draw a configuration from configuration space

• kwargs –

on_trial_result(trial, result)

We simply relay result to the searcher. Other decisions are done in on_trial_complete.

Return type:

str

Submodules

• syne_tune.optimizer.schedulers.neuralbands.networks module
  • NetworkExploitation
    • NetworkExploitation.forward()
    • NetworkExploitation.training
  • Exploitation
    • Exploitation.add_data()
    • Exploitation.predict()
    • Exploitation.train()
• syne_tune.optimizer.schedulers.neuralbands.neuralband module
  • is_continue_decision()
  • NeuralbandScheduler
    • NeuralbandScheduler.on_trial_result()
• syne_tune.optimizer.schedulers.neuralbands.neuralband_supplement module
  • is_continue_decision()
  • NeuralbandSchedulerBase
    • NeuralbandSchedulerBase.on_trial_result()
  • NeuralbandEGreedyScheduler
  • NeuralbandTSScheduler
  • NeuralbandUCBScheduler