Changes

    if len(params_dict) > 20:

      raise ValueError(

          "This class can only search over 20 parameters in one invocation.")

    data_dir = deepchem.utils.get_data_dir()

    # Specify logfile

    if logfile:

      log_file = logfile

# Fit models

regression_metric = dc.metrics.Metric(dc.metrics.pearson_r2_score)

# TODO(rbharath): I don't like this awkward string/class divide. Maybe clean up?

optimizer = dc.hyper.GaussianProcessHyperparamOpt('tf_regression')

def rf_model_builder(**model_params):

  rf_params = {k:v for (k,v) in model_params.items() if k != 'model_dir'}

  model_dir = model_params['model_dir']

  sklearn_model = sklearn.ensemble.RandomForestRegressor(**rf_params)

  return dc.models.SklearnModel(sklearn_model, model_dir)

optimizer = dc.hyper.GaussianProcessHyperparamOpt(rf_model_builder)

best_hyper_params, best_performance = optimizer.hyperparam_search(

    dc.molnet.preset_hyper_parameters.hps['tf_regression'], train, valid,

    transformers, [regression_metric])

    params_dict,

    train_dataset,

    valid_dataset,

    transformers,

    metric)