In this function, although there is a seed=777 argument in the signature like this.
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def get_molnet_dataset(dataset_name, preprocessor=None, labels=None, |
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split=None, frac_train=.8, frac_valid=.1, |
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frac_test=.1, seed=777, return_smiles=False, |
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return_pdb_id=False, target_index=None, task_index=0, |
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**kwargs): |
But it's never passed to any splitter, in the same function, the splitter is called here without seed argument:
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if dataset_config['dataset_type'] == 'one_file_csv': |
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split = dataset_config['split'] if split is None else split |
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if isinstance(split, str): |
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splitter = split_method_dict[split]() |
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elif isinstance(split, BaseSplitter): |
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splitter = split |
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else: |
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raise TypeError("split must be None, str or instance of" |
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" BaseSplitter, but got {}".format(type(split))) |
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if isinstance(splitter, ScaffoldSplitter): |
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get_smiles = True |
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else: |
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get_smiles = return_smiles |
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result = parser.parse(get_molnet_filepath(dataset_name), |
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return_smiles=get_smiles, |
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target_index=target_index, **kwargs) |
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dataset = result['dataset'] |
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smiles = result['smiles'] |
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train_ind, valid_ind, test_ind = \ |
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splitter.train_valid_test_split(dataset, smiles_list=smiles, |
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task_index=task_index, |
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frac_train=frac_train, |
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frac_valid=frac_valid, |
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frac_test=frac_test, **kwargs) |
Then, in the splitter (here the ScaffoldSplitter), the seed argument is still None:
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def train_valid_test_split(self, dataset, smiles_list, frac_train=0.8, |
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frac_valid=0.1, frac_test=0.1, converter=None, |
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return_index=True, seed=None, |
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include_chirality=False, **kwargs): |
According to the implementation, the seed=None means it be initialized by reading data from /dev/urandom according to the numpy docs.
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def _split(self, dataset, frac_train=0.8, frac_valid=0.1, frac_test=0.1, |
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**kwargs): |
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numpy.testing.assert_almost_equal(frac_train + frac_valid + frac_test, |
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1.) |
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seed = kwargs.get('seed', None) |
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smiles_list = kwargs.get('smiles_list') |
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include_chirality = kwargs.get('include_chirality') |
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if len(dataset) != len(smiles_list): |
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raise ValueError("The lengths of dataset and smiles_list are " |
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"different") |
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rng = numpy.random.RandomState(seed) |
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This bug will cause data split inconsistent across different models and different run, even if we explicitly specify the same seed, and the default seed 777 here is useless.
PS: I use Pycharm debug tool to validate above procedure.
