Merge pull request #2083 from nd-02110114/fix-mat-dataset

  a = [4625.32086965, 6585.20209678, 61.00680193, 48.72230922, 48.72230922]

  assert dataset.X.shape == (5, 1, 5)

  assert np.allclose(dataset.X[0][0], a, atol=.5)

  assert dataset.X.shape == (5, 5)

  assert np.allclose(dataset.X[0], a, atol=.5)

  dataset = loader.create_dataset(input_file, shard_size=None)

  assert dataset.X.shape == (5, 1, 5)

  assert dataset.X.shape == (5, 5)

  dataset = loader.create_dataset([input_file, input_file], shard_size=5)

  assert dataset.X.shape == (10, 1, 5)

  assert dataset.X.shape == (10, 5)

  This class requires matminer and Pymatgen to be installed.

  """

  def __init__(self, max_atoms: int, flatten: bool = True):

  def __init__(self, max_atoms: int = 100, flatten: bool = True):

    """

    Parameters

    ----------

    max_atoms: int

    max_atoms: int (default 100)

      Maximum number of atoms for any crystal in the dataset. Used to

      pad the Coulomb matrix.

    flatten: bool (default True)

    if self.flatten:

      eigs, _ = np.linalg.eig(sine_mat)

      zeros = np.zeros((1, self.max_atoms))

      zeros[:len(eigs)] = eigs

      zeros = np.zeros(self.max_atoms)

      zeros[:len(eigs[0])] = eigs[0]

      features = zeros

    else:

      features = pad_array(sine_mat, self.max_atoms)

    Test SCM featurizer.

    """

    featurizer = SineCoulombMatrix(max_atoms=1)

    featurizer = SineCoulombMatrix(max_atoms=3)

    features = featurizer.featurize([self.struct_dict])

    assert len(features) == 1

    assert np.isclose(features[0], 1244, atol=.5)

    assert features.shape == (1, 3)

    assert np.isclose(features[0][0], 1244, atol=.5)

  def test_cgcnn_featurizer(self):

    """

"""

import os

import logging

import deepchem

from deepchem.feat import Featurizer, MaterialStructureFeaturizer, MaterialCompositionFeaturizer

from deepchem.trans import Transformer

from deepchem.feat import MaterialCompositionFeaturizer

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__)

# 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'

BANDGAP_URL = 'https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/expt_gap.tar.gz'

# dict of accepted featurizers for this dataset

# modify the returned dicts for your dataset

    reload: bool = True,

    data_dir: Optional[str] = None,

    save_dir: Optional[str] = None,

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

    featurizer_kwargs: Dict[str, Any] = {},

    splitter_kwargs: Dict[str, Any] = {

        'frac_train': 0.8,

        'frac_valid': 0.1,

  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)

  .. [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

  --------

  # 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):

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

      if not os.path.exists(targz_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)

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, Callable

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

logger = logging.getLogger(__name__)

# TODO: Change URLs

DEFAULT_DIR = deepchem.utils.get_data_dir()

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

PEROVSKITE_URL = 'https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/perovskite.tar.gz'

# dict of accepted featurizers for this dataset

# modify the returned dicts for your 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)

  .. [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

  --------

  # 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):

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

      if not os.path.exists(targz_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)