Delete material_datasets.py

"""

Datasets for inorganic crystal structures.

"""

import os

import logging

import deepchem

from deepchem.feat import Featurizer, MaterialStructureFeaturizer, MaterialCompositionFeaturizer

from deepchem.trans import Transformer

from deepchem.splits.splitters import Splitter

from deepchem.molnet.defaults import get_defaults

from typing import List, Tuple, Dict, Optional, Union

logger = logging.getLogger(__name__)

# TODO: Change URLs

DEFAULT_DIR = deepchem.utils.get_data_dir()

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

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

# dict of accepted featurizers for this dataset

# modify the returned dicts for your dataset

DEFAULT_FEATURIZERS = get_defaults("feat")

# Names of supported featurizers

featurizers = [

    'ElementPropertyFingerprint', 'SineCoulombMatrix',

    'StructureGraphFeaturizer'

]

DEFAULT_FEATURIZERS = {k: DEFAULT_FEATURIZERS[k] for k in featurizers}

# dict of accepted transformers

DEFAULT_TRANSFORMERS = get_defaults("trans")

# dict of accepted splitters

DEFAULT_SPLITTERS = get_defaults("splits")

# names of supported splitters

splitters = ['RandomSplitter']

DEFAULT_SPLITTERS = {k: DEFAULT_SPLITTERS[k] for k in splitters}

def load_bandgap(

    featurizer: MaterialCompositionFeaturizer = DEFAULT_FEATURIZERS['ElementPropertyFingerprint'],

    transformers: List[Transformer] = [

        DEFAULT_TRANSFORMERS['NormalizationTransformer']

    ],

    splitter: Splitter = DEFAULT_SPLITTERS['RandomSplitter'],

    reload: bool = True,

    data_dir: Optional[str] = None,

    save_dir: Optional[str] = None,

    featurizer_kwargs: Dict[str, object] = {'data_source': 'matminer'},

    splitter_kwargs: Dict[str, object] = {

        'frac_train': 0.8,

        'frac_valid': 0.1,

        'frac_test': 0.1

    },

    transformer_kwargs: Dict[str, Dict[str, object]] = {

        'NormalizationTransformer': {

            'transform_X': True

        }

    },

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

  """Load band gap dataset.

  Contains 4604 experimentally measured band gaps for inorganic

  crystal structure compositions. In benchmark studies, random forest 

  models achieved a mean average error of 0.45 eV during five-fold 

  nested cross validation on this dataset. 

  For more details on the dataset see [1]_. For more details

  on previous benchmarks for this dataset, see [2]_.

  Parameters

  ----------

  featurizer : MaterialCompositionFeaturizer 

    (default ElementPropertyFingerprint)

    A featurizer that inherits from deepchem.feat.Featurizer.

  transformers : List[Transformer]

    A transformer that inherits from deepchem.trans.Transformer.

  splitter : Splitter (default RandomSplitter)

    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.

  data_dir : str, optional

    Path to datasets.

  save_dir : str, optional

    Path to featurized datasets.

  featurizer_kwargs : dict

    Specify parameters to featurizer, e.g. {"size": 1024}

  splitter_kwargs : dict

    Specify parameters to splitter, e.g. {"seed": 42}

  transformer_kwargs : dict

    Maps transformer names to constructor arguments, e.g.

    {"BalancingTransformer": {"transform_x":True, "transform_y":False}}

  **kwargs : additional optional arguments.

  Returns

  -------

  tasks, datasets, transformers : tuple

    tasks : list

      Column names corresponding to machine learning target variables.

    datasets : tuple

      train, validation, test splits of data as

      ``deepchem.data.datasets.Dataset`` instances.

    transformers : list

      ``deepchem.trans.transformers.Transformer`` instances applied

      to dataset.

  References

  ----------

  .. [1] Zhuo, Y. et al. "Predicting the Band Gaps of Inorganic Solids by Machine Learning." J. Phys. Chem. Lett. (2018) DOI: 10.1021/acs.jpclett.8b00124.

  .. [2] Dunn, A. et al. "Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm." https://arxiv.org/abs/2005.00707 (2020)

  Examples

  --------

  >> import deepchem as dc

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

  >> train_dataset, val_dataset, test_dataset = datasets

  >> n_tasks = len(tasks)

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

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

  """

  # Featurize

  logger.info("About to featurize band gap dataset.")

  my_tasks = ['gap expt']  # machine learning targets

  # Get DeepChem data directory if needed

  if data_dir is None:

    data_dir = DEFAULT_DIR

  if save_dir is None:

    save_dir = DEFAULT_DIR

  # Check for str args to featurizer and splitter

  if isinstance(featurizer, str):

    featurizer = DEFAULT_FEATURIZERS[featurizer](**featurizer_kwargs)

  elif issubclass(featurizer, Featurizer):

    featurizer = featurizer(**featurizer_kwargs)

  if isinstance(splitter, str):

    splitter = DEFAULT_SPLITTERS[splitter]()

  elif issubclass(splitter, Splitter):

    splitter = splitter()

  # Reload from disk

  if reload:

    featurizer_name = str(featurizer.__class__.__name__)

    splitter_name = str(splitter.__class__.__name__)

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

                               splitter_name)

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

        save_folder)

    if loaded:

      return my_tasks, all_dataset, transformers

  # First type of supported featurizers

  supported_featurizers = ['ElementPropertyFingerprint'

                          ]  # type: List[Featurizer]

  # Load .tar.gz file

  if featurizer.__class__.__name__ in supported_featurizers:

    dataset_file = os.path.join(data_dir, 'expt_gap.tar.gz')

    deepchem.utils.untargz_file(dataset_file, dest_dir=data_dir)

    dataset_file = os.path.join(data_dir, 'expt_gap.json')

    if not os.path.exists(dataset_file):

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

      deepchem.utils.untargz_file(

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

    # Changer loader to match featurizer and data file type

    loader = deepchem.data.JsonLoader(

        tasks=my_tasks,

        feature_field="composition",

        label_field="gap expt",

        featurizer=featurizer)

  # Featurize dataset

  dataset = loader.create_dataset(dataset_file)

  train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(

      dataset, **splitter_kwargs)

  # Initialize transformers

  transformers = [

      DEFAULT_TRANSFORMERS[t](dataset=dataset, **transformer_kwargs[t])

      if isinstance(t, str) else t(

          dataset=dataset, **transformer_kwargs[str(t.__name__)])

      for t in transformers

  ]

  for transformer in transformers:

    train_dataset = transformer.transform(train_dataset)

    valid_dataset = transformer.transform(valid_dataset)

    test_dataset = transformer.transform(test_dataset)

  if reload:  # save to disk

    deepchem.utils.save.save_dataset_to_disk(

        save_folder, train_dataset, valid_dataset, test_dataset, transformers)

  return my_tasks, (train_dataset, valid_dataset, test_dataset), transformers

def load_perovskite(

    featurizer: Featurizer = DEFAULT_FEATURIZERS['SineCoulombMatrix'],

    transformers: List[Transformer] = [

        DEFAULT_TRANSFORMERS['NormalizationTransformer']

    ],

    splitter: Splitter = DEFAULT_SPLITTERS['RandomSplitter'],

    reload: bool = True,

    data_dir: Optional[str] = None,

    save_dir: Optional[str] = None,

    featurizer_kwargs: Dict[str, object] = None,

    splitter_kwargs: Dict[str, object] = {

        'frac_train': 0.6,

        'frac_valid': 0.2,

        'frac_test': 0.2

    },

    transformer_kwargs: Dict[str, Dict[str, object]] = {

        'NormalizationTransformer': {

            'transform_X': True

        }

    },

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

  """Load perovskite dataset.

  Contains 18928 perovskite structures and their formation energies.

  In benchmark studies, random forest models and crystal graph

  neural networks achieved mean average error of 0.23 and 0.05 eV/atom,

  respectively, during five-fold nested cross validation on this

  dataset. 

  For more details on the dataset see [1]_. For more details

  on previous benchmarks for this dataset, see [2]_.

  Parameters

  ----------

  featurizer : StructureGraphFeaturizer

    A featurizer that inherits from deepchem.feat.Featurizer.

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

    A transformer that inherits from deepchem.trans.Transformer.

  splitter : RandomSplitter

    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.

  data_dir : str, optional

    Path to datasets.

  save_dir : str, optional

    Path to featurized datasets.

  featurizer_kwargs : dict

    Specify parameters to featurizer, e.g. {"size": 1024}

  splitter_kwargs : dict

    Specify parameters to splitter, e.g. {"seed": 42}

  transformer_kwargs : dict

    Maps transformer names to constructor arguments, e.g.

    {"BalancingTransformer": {"transform_x":True, "transform_y":False}}

  **kwargs : additional optional arguments.

  Returns

  -------

  tasks, datasets, transformers : tuple

    tasks : list

      Column names corresponding to machine learning target variables.

    datasets : tuple

      train, validation, test splits of data as

      ``deepchem.data.datasets.Dataset`` instances.

    transformers : list

      ``deepchem.trans.transformers.Transformer`` instances applied

      to dataset.

  References

  ----------

  .. [1] Castelli, I. et al. "New cubic perovskites for one- and two-photon water splitting using the computational materials repository." Energy Environ. Sci., (2012), 5, 9034-9043 DOI: 10.1039/C2EE22341D.

  .. [2] Dunn, A. et al. "Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm." https://arxiv.org/abs/2005.00707 (2020)

  Examples

  --------

  >> import deepchem as dc

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

  >> train_dataset, val_dataset, test_dataset = datasets

  >> n_tasks = len(tasks)

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

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

  """

  # Featurize

  logger.info("About to featurize perovskite dataset.")

  my_tasks = ['e_form']  # machine learning targets

  # Get DeepChem data directory if needed

  if data_dir is None:

    data_dir = DEFAULT_DIR

  if save_dir is None:

    save_dir = DEFAULT_DIR

  # Check for str args to featurizer and splitter

  if isinstance(featurizer, str):

    featurizer = DEFAULT_FEATURIZERS[featurizer](**featurizer_kwargs)

  elif issubclass(featurizer, Featurizer):

    featurizer = featurizer(**featurizer_kwargs)

  if isinstance(splitter, str):

    splitter = DEFAULT_SPLITTERS[splitter]()

  elif issubclass(splitter, Splitter):

    splitter = splitter()

  # Reload from disk

  if reload:

    featurizer_name = str(featurizer.__class__.__name__)

    splitter_name = str(splitter.__class__.__name__)

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

                               featurizer_name, splitter_name)

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

        save_folder)

    if loaded:

      return my_tasks, all_dataset, transformers

  # First type of supported featurizers

  supported_featurizers = ['StructureGraphFeaturizer',

                           'SineCoulombMatrix']  # type: List[Featurizer]

  # Load .tar.gz file

  if featurizer.__class__.__name__ in supported_featurizers:

    dataset_file = os.path.join(data_dir, 'perovskite.tar.gz')

    deepchem.utils.untargz_file(dataset_file, dest_dir=data_dir)

    dataset_file = os.path.join(data_dir, 'perovskite.json')

    if not os.path.exists(dataset_file):

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

      deepchem.utils.untargz_file(

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

    # Changer loader to match featurizer and data file type

    loader = deepchem.data.JsonLoader(

        tasks=my_tasks,

        feature_field="structure",

        label_field="e_form",

        featurizer=featurizer)

  # Featurize dataset

  dataset = loader.create_dataset(dataset_file)

  train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(

      dataset, **splitter_kwargs)

  # Initialize transformers

  transformers = [

      DEFAULT_TRANSFORMERS[t](dataset=dataset, **transformer_kwargs[t])

      if isinstance(t, str) else t(

          dataset=dataset, **transformer_kwargs[str(t.__name__)])

      for t in transformers

  ]

  for transformer in transformers:

    train_dataset = transformer.transform(train_dataset)

    valid_dataset = transformer.transform(valid_dataset)

    test_dataset = transformer.transform(test_dataset)

  if reload:  # save to disk

    deepchem.utils.save.save_dataset_to_disk(

        save_folder, train_dataset, valid_dataset, test_dataset, transformers)

  return my_tasks, (train_dataset, valid_dataset, test_dataset), transformers