Add defaults to loader template

import logging

import deepchem

from deepchem.feat import Featurizer

from deepchem.trans import Transformer

from deepchem.split.splitters import Splitter

from typing import Iterable, List

from typing import List, Tuple, Optional

logger = logging.getLogger(__name__)

MYDATASET_URL = 'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/mydataset.tar.gz'

MYDATASET_CSV_URL = 'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/mydataset.csv'

# dict of accepted featurizers for this dataset

DEFAULT_FEATURIZERS = {

    'Raw': deepchem.feat.RawFeaturizer(),

    'ECFP': deepchem.feat.CircularFingerprint(size=1024),

}

# dict of accepted transformers

DEFAULT_TRANSFORMERS = {

    'Power': deepchem.trans.PowerTransformer(),

}

def load_mydataset(featurizer: str = None,

                   split: str = 'random',

# dict of accepted splitters

DEFAULT_SPLITTERS = {

    'Index': deepchem.splits.IndexSplitter(),

    'Random': deepchem.splits.RandomSplitter(),

}

def load_mydataset(

    featurizer: Featurizer = DEFAULT_FEATURIZERS['Raw'],

    transformers: Tuple[Transformer] = (DEFAULT_TRANSFORMERS['Power']),

    splitter: Splitter = DEFAULT_SPLITTERS['Random'],

    reload: bool = True,

                   move_mean: bool = True,

                   data_dir: str = None,

                   save_dir: str = None,

                   **kwargs) -> Iterable:

    data_dir: Optional[str] = None,

    save_dir: Optional[str] = None,

    **kwargs) -> Tuple[List, Tuple, List]:

  """Load mydataset.

  This is a template for adding a function to load a dataset from

  MoleculeNet. Adjust the global variable URL strings, default parameters,

  and variable names as needed. The function will need to be modified

  to handle the allowed featurizers for your dataset. 

  default featurizers, transformers, and splitters, and variable names as

  needed.

  If `reload = True` and `data_dir` (`save_dir`) is specified, the loader

  will attempt to load the raw dataset (featurized dataset) from disk.

  Otherwise, the dataset will be downloaded from the DeepChem AWS bucket.

  The dataset will be featurized with `featurizer` and separated into

  train/val/test sets according to `split`. Additional kwargs may

  be given for specific featurizers and splitters.

  train/val/test sets according to `splitter`. Additional kwargs may

  be given for specific featurizers, transformers, and splitters.

  Please refer to the MoleculeNet documentation for further information

  https://deepchem.readthedocs.io/en/latest/moleculenet.html.

  ----------

  featurizer: {List of allowed featurizers for this dataset}

    A featurizer that inherits from deepchem.feat.Featurizer.

  split: {'random', 'stratified', 'index', 'scaffold'}

  transformers: Tuple{List of allowed transformers for this dataset}

    A transformer that inherits from deepchem.trans.Transformer.

  splitter: {List of allowed splitters for this dataset}

    A splitter that inherits from deepchem.splits.splitters.Splitter.

  reload: bool (default True)

    Try to reload dataset from disk if already downloaded. Save to disk

    after featurizing.

  move_mean: bool (default True)

    Center data to have 0 mean after transform.

  data_dir: str, optional

    Path to datasets.

  save_dir: str, optional

    Path to featurized datasets.

  **kwargs: optional arguments to featurizers and splitters.

  **kwargs: optional arguments to methods of featurizers, transformers, and

  splitters.

  Returns

  -------

  tasks, datasets, transformers : iterable

  tasks, datasets, transformers : tuple

    tasks : list

      Column names corresponding to machine learning target variables.

    datasets : tuple

  Examples

  --------

  >>> import deepchem as dc

  >>> tasks, datasets, transformers = dc.molnet.load_tox21(reload=False)

  >>> train_dataset, val_dataset, test_dataset = datasets

  >>> n_tasks = len(tasks)

  >>> n_features = train_dataset.get_data_shape()[0]

  >>> model = dc.models.MultitaskClassifier(n_tasks, n_features)

  >> import deepchem as dc

  >> tasks, datasets, transformers = dc.molnet.load_tox21(reload=False)

  >> train_dataset, val_dataset, test_dataset = datasets

  >> n_tasks = len(tasks)

  >> n_features = train_dataset.get_data_shape()[0]

  >> model = dc.models.MultitaskClassifier(n_tasks, n_features)

  """

  if save_dir is None:

    save_dir = DEFAULT_DIR

  # Check for str args to featurizer, splitter, and transformers

  if isinstance(featurizer, str):

    featurizer = DEFAULT_FEATURIZERS[featurizer]

  if isinstance(splitter, str):

    splitter = DEFAULT_SPLITTERS[splitter]

  transformers = [

      DEFAULT_TRANSFORMERS[t] if isinstance(t, str) else t for t in transformers

  ]

  # Reload from disk

  if reload:

    save_folder = os.path.join(save_dir, "mydataset-featurized")

    if not move_mean:

      save_folder = os.path.join(save_folder, str(featurizer) + "_mean_unmoved")

    else:

      save_folder = os.path.join(save_folder, str(featurizer))

    featurizer_name = str(featurizer.__class__.__name__)

    splitter_name = str(splitter.__class__.__name__)

    save_folder = os.path.join(save_dir, "mydataset-featurized",

                               featurizer_name, splitter_name)

    loaded, all_dataset, transformers = deepchem.utils.save.load_dataset_from_disk(

        save_folder)

      deepchem.utils.download_url(url=MYDATASET_URL, dest_dir=data_dir)

      deepchem.utils.untargz_file(

          os.path.join(data_dir, 'mydataset.tar.gz'), data_dir)

  else:  # only load CSV file

    dataset_file = os.path.join(data_dir, "mydataset.csv")

    if not os.path.exists(dataset_file):

      deepchem.utils.download_url(url=MYDATASET_CSV_URL, dest_dir=data_dir)

  # Handle all allowed SDF featurizers

  if featurizer in sdf_featurizers:

    if featurizer == 'Featurizer1':

      featurizer = deepchem.feat.Featurizer1()

    elif featurizer == 'Featurizer2':

      featurizer = deepchem.feat.Featurizer2()

    loader = deepchem.data.SDFLoader(

        tasks=my_tasks,

        smiles_field="smiles",  # column name holding SMILES strings

        mol_field="mol",  # field where RKit mol objects are stored

        featurizer=featurizer)

  else:  # Handle allowed CSV featurizers

    if featurizer == 'Featurizer3':

      featurizer = deepchem.feat.Featurizer3()

    elif featurizer == 'Featurizer4':

      featurizer = deepchem.feat.Featurizer4()

  else:  # only load CSV file

    dataset_file = os.path.join(data_dir, "mydataset.csv")

    if not os.path.exists(dataset_file):

      deepchem.utils.download_url(url=MYDATASET_CSV_URL, dest_dir=data_dir)

    loader = deepchem.data.CSVLoader(

        tasks=my_tasks, smiles_field="smiles", featurizer=featurizer)

  # Featurize dataset

  dataset = loader.featurize(dataset_file)

  if split is None:  # Must give a recommended split for data

    raise ValueError()

  # Generate Splitter

  splitters = {

      'index':

      deepchem.splits.IndexSplitter(),

      'random':

      deepchem.splits.RandomSplitter(),

      'stratified':

      deepchem.splits.SingletaskStratifiedSplitter(task_number=len(my_tasks)),

      'scaffold':

      deepchem.splits.ScaffoldSplitter()

  }

  splitter = splitters[split]

  # 80/10/10 train/val/test split is default

  frac_train = kwargs.get("frac_train", 0.8)

      frac_valid=frac_valid,

      frac_test=frac_test)

  transformers = [

      deepchem.trans.NormalizationTransformer(

          transform_y=True, dataset=train_dataset, move_mean=move_mean)

  ]

  for transformer in transformers:

    train_dataset = transformer.transform(train_dataset)

    valid_dataset = transformer.transform(valid_dataset)

5. Ask a member of the technical steering committee to add your .tar.gz or .zip file to the DeepChem AWS bucket. Modify your load function to pull down the dataset from AWS.

6. Submit a [WIR] PR (Work in progress pull request) following the PR `template <https://github.com/deepchem/deepchem/blob/master/docs/molnet_pr_template.md>`_.  

6. Submit a [WIP] PR (Work in progress pull request) following the PR `template <https://github.com/deepchem/deepchem/blob/master/docs/molnet_pr_template.md>`_.  

Load Dataset Template

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