Added example file

import numpy as np

np.random.seed(123)

import tensorflow as tf

tf.random.set_seed(123)

import deepchem as dc

# Load delaney dataset

delaney_tasks, delaney_datasets, transformers = dc.molnet.load_delaney()

train, valid, test= delaney_datasets

# 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')

best_hyper_params, best_performance = optimizer.hyperparam_search(

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

  train,

  valid,

  transformers,

  [regression_metric]

)