Minor changes for CANVAS loading.

  train_test_cmd.set_defaults(func=train_test_input)

  # TRAIN FLAGS

  train_cmd = subparsers.add_parser("train",

                  help="Train a model on train data processed by transform.")

  train_cmd = subparsers.add_parser("fit",

                  help="Fit a model to training data.")

  group = train_cmd.add_argument_group("load-and-transform")

  group.add_argument("--task-type", default="classification",

                      choices=["classification", "regression"],

  sorted_targets = sorted(labels.keys())

  singletask = {target: {} for target in sorted_targets}

  # Populate the singletask datastructures

  sorted_smiles = sorted(dataset.keys())

  for index, smiles in enumerate(sorted_smiles):

    datapoint = dataset[smiles]

  sorted_ids = sorted(dataset.keys())

  for index, id in enumerate(sorted_ids):

    datapoint = dataset[id]

    labels = datapoint["labels"]

    for t_ind, target in enumerate(sorted_targets):

      if labels[target] == -1:

      else:

        datapoint_copy = datapoint.copy()

        datapoint_copy["labels"] = {target: labels[target]}

        singletask[target][smiles] = datapoint_copy 

        singletask[target][id] = datapoint_copy 

  return singletask

def split_dataset(dataset, splittype, seed=None):

def train_test_specified_split(dataset):

  """Split provided data due to splits in origin data."""

  train, test = {}, {}

  for smiles, datapoint in dataset.iteritems():

  for id, datapoint in dataset.iteritems():

    if "split" not in datapoint:

      raise ValueError("Missing required split information.")

    if datapoint["split"].lower() == "train" or datapoint["split"].lower() == "valid":

      train[smiles] = datapoint

    # TODO(rbharath): Add support for validation sets.

    if datapoint["split"].lower() == "train":

      train[id] = datapoint

    elif datapoint["split"].lower() == "test":

      test[smiles] = datapoint

    else:

      raise ValueError("Improper split specified.")

      test[id] = datapoint

  return train, test

def train_test_random_split(dataset, frac_train=.8, seed=None):

    A dictionary of type produced by load_datasets. 

  """

  scaffolds = {}

  for smiles in dataset:

    datapoint = dataset[smiles]

  for id in dataset:

    datapoint = dataset[id]

    scaffold = datapoint["scaffold"]

    if scaffold not in scaffolds:

      scaffolds[scaffold] = [smiles]

      scaffolds[scaffold] = [id]

    else:

      scaffolds[scaffold].append(smiles)

      scaffolds[scaffold].append(id)

  # Sort from largest to smallest scaffold sets 

  return sorted(scaffolds.items(), key=lambda x: -len(x[1]))