deepchem.hyper package

Submodules

deepchem.hyper.gaussian_process module

Contains class for gaussian process hyperparameter optimizations.

class deepchem.hyper.gaussian_process.GaussianProcessHyperparamOpt(model_class, verbose=True)[source]

Bases: deepchem.hyper.grid_search.HyperparamOpt

Gaussian Process Global Optimization(GPGO)

Perform hyperparams search using a gaussian process assumption

params_dict include single-valued parameters being optimized, which should only contain int, float and list of int(float)

parameters with names in hp_invalid_list will not be changed.

For Molnet models, self.model_class is model name in string, params_dict = dc.molnet.preset_hyper_parameters.hps[self.model_class]

Parameters:
  • params_dict (dict) – dict including parameters and their initial values parameters not suitable for optimization can be added to hp_invalid_list
  • train_dataset (dc.data.Dataset struct) – dataset used for training
  • valid_dataset (dc.data.Dataset struct) – dataset used for validation(optimization on valid scores)
  • output_transformers (list of dc.trans.Transformer) – transformers for evaluation
  • metric (list of dc.metrics.Metric) – metric used for evaluation
  • direction (bool) – maximization(True) or minimization(False)
  • n_features (int) – number of input features
  • n_tasks (int) – number of tasks
  • max_iter (int) – number of optimization trials
  • search_range (int(float)) – optimization on [initial values / search_range, initial values * search_range]
  • hp_invalid_list (list) – names of parameters that should not be optimized
  • logfile (string) – name of log file, hyperparameters and results for each trial will be recorded
Returns:

  • hyper_parameters (dict) – params_dict with all optimized values
  • valid_performance_opt (float) – best performance on valid dataset

Module contents