benchmark_seed_change

import deepchem as dc

import argparse

np.random.seed(123)

parser = argparse.ArgumentParser(

    description='Deepchem benchmark: ' +

    'giving performances of different learning models on datasets')

    dest='test',

    default=False,

    help='Evalute performance on test set')

parser.add_argument(

    '--seed',

    action='append',

    dest='seed_args',

    default=[],

    help='Choice of random seed')

args = parser.parse_args()

#Datasets and models used in the benchmark test

splitters = args.splitter_args

models = args.model_args

datasets = args.dataset_args

test = args.test

if len(args.seed_args) > 0:

  seed = args.seed_args[0]

else:

  seed = 123

if len(splitters) == 0:

  splitters = ['index', 'random', 'scaffold']

for dataset in datasets:

  for split in splitters:

    for model in models:

      dc.molnet.run_benchmark([dataset], str(model), split=split, test=test)

      np.random.seed(seed)

      dc.molnet.run_benchmark(

          [dataset], str(model), split=split, test=test, seed=seed)