Initial MW splitter framework

    """

    raise NotImplementedError

class MolecularWeightSplitter(Splitter):

  """

  Class for doing data splits by molecular weight.

  """

  def split(self, samples, seed=None, frac_train=.8, frac_valid=.1,

            frac_test=.1, log_every_n=None):

    """

    Splits internal compounds into train/validation/test using the MW calculated

    by SMILES string.

    """

    np.testing.assert_almost_equal(frac_train + frac_valid + frac_test, 1.)

    np.random.seed(seed)

    smiles_df = samples.compounds_df['smiles']

    mw = []

    for idx, row in smiles_df.iterrows():

        smiles = Chem.Mol

    # create new array that contains IDs sorted by MW

    # split based on frac_train/frac_valid+frac_test

    # random split frac_valid/frac_test

    train_cutoff = frac_train * len(samples.compounds_df)

    valid_cutoff = (frac_train+frac_valid) * len(samples.compounds_df)

    shuffled = np.random.permutation(range(len(samples.compounds_df)))

    # log stats on MW in each dataset

    return (shuffled[:train_cutoff], shuffled[train_cutoff:valid_cutoff],

            shuffled[valid_cutoff:])

class RandomSplitter(Splitter):

  """

  Class for doing random data splits.