🚨 fix flake8 error and update docstrings in materials molnet

  Parameters

  ----------

  featurizer : MaterialCompositionFeaturizer

    (default ElementPropertyFingerprint)

  featurizer : MaterialCompositionFeaturizer, default ElementPropertyFingerprint

    A featurizer that inherits from deepchem.feat.Featurizer.

  transformers : List[Transformer]

    A transformer that inherits from deepchem.trans.Transformer.

  reload : bool (default True)

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

    after featurizing.

  data_dir : str, optional

  data_dir : str, optional (default None)

    Path to datasets.

  save_dir : str, optional

  save_dir : str, optional (default None)

    Path to featurized datasets.

  featurizer_kwargs : Dict[str, Any]

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

import os

import logging

import deepchem

from deepchem.feat import Featurizer, MaterialStructureFeaturizer, MaterialCompositionFeaturizer

from deepchem.trans import Transformer

from deepchem.feat import MaterialStructureFeaturizer

from deepchem.splits.splitters import Splitter

from deepchem.molnet.defaults import get_defaults

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

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

logger = logging.getLogger(__name__)

  Parameters

  ----------

  featurizer : MaterialCompositionFeaturizer 

    (default CGCNNFeaturizer)

  featurizer : MaterialStructureFeaturizer, default SineCoulombMatrix

    A featurizer that inherits from deepchem.feat.Featurizer.

  transformers : List[Transformer]

    A transformer that inherits from deepchem.trans.Transformer.

  reload : bool (default True)

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

    after featurizing.

  data_dir : str, optional

  data_dir : str, optional (default None)

    Path to datasets.

  save_dir : str, optional

  save_dir : str, optional (default None)

    Path to featurized datasets.

  featurizer_kwargs : Dict[str, Any]

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

    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}}

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

  **kwargs : additional optional arguments.

  Returns

  References

  ----------

  .. [1] A. Jain*, S.P. Ong*, et al. (*=equal contributions) The Materials Project: A materials genome approach to accelerating materials innovation APL Materials, 2013, 1(1), 011002. doi:10.1063/1.4812323 (2013).

  .. [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)

  .. [1] A. Jain*, S.P. Ong*, et al. (*=equal contributions) The Materials Project:

     A materials genome approach to accelerating materials innovation APL Materials,

     2013, 1(1), 011002. doi:10.1063/1.4812323 (2013).

  .. [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 os

import logging

import deepchem

from deepchem.feat import Featurizer, MaterialStructureFeaturizer, MaterialCompositionFeaturizer

from deepchem.trans import Transformer

from deepchem.feat import MaterialStructureFeaturizer

from deepchem.splits.splitters import Splitter

from deepchem.molnet.defaults import get_defaults

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

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

logger = logging.getLogger(__name__)

  Parameters

  ----------

  featurizer : MaterialCompositionFeaturizer 

    (default CGCNNFeaturizer)

  featurizer : MaterialStructureFeaturizer, default SineCoulombMatrix

    A featurizer that inherits from deepchem.feat.Featurizer.

  transformers : List[Transformer]

  transformers : List[Transformer], , default NormalizationTransformer

    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

  data_dir : str, optional (default None)

    Path to datasets.

  save_dir : str, optional

  save_dir : str, optional (default None)

    Path to featurized datasets.

  featurizer_kwargs : Dict[str, Any]

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

  References

  ----------

  .. [1] A. Jain*, S.P. Ong*, et al. (*=equal contributions) The Materials Project: A materials genome approach to accelerating materials innovation APL Materials, 2013, 1(1), 011002. doi:10.1063/1.4812323 (2013).

  .. [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)

  .. [1] A. Jain*, S.P. Ong*, et al. (*=equal contributions) The Materials Project:

     A materials genome approach to accelerating materials innovation APL Materials,

     2013, 1(1), 011002. doi:10.1063/1.4812323 (2013).

  .. [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

  --------

  Parameters

  ----------

  featurizer : MaterialStructureFeaturizer

  featurizer : MaterialStructureFeaturizer, default SineCoulombMatrix

    A featurizer that inherits from deepchem.feat.Featurizer.

  transformers : List[Transformer]

    A transformer that inherits from deepchem.trans.Transformer.

  reload : bool (default True)

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

    after featurizing.

  data_dir : str, optional

  data_dir : str, optional (default None)

    Path to datasets.

  save_dir : str, optional

  save_dir : str, optional (default None)

    Path to featurized datasets.

  featurizer_kwargs : Dict[str, Any]

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