Changes

    Parameters

    ----------

    inputs: Sequence[Any]

      List of inputs to process. Entries can be filenames or arbitrary objects.

      List of inputs to process. Entries can be arbitrary objects so long as

      they are understood by `self.featurizer`

    data_dir: str, optional

      Directory to store featurized dataset.

    shard_size: int, optional

    self._X_shape = self._find_array_shape(X)

    self._y_shape = self._find_array_shape(y)

    if w is None:

      if len(self._y_shape) == 1:

      if len(self._y_shape) == 0:

        # Case n_samples should be 1

        if n_samples != 1:

          raise ValueError("y can only be a scalar if n_samples == 1")

        w = np.ones_like(y)

      elif len(self._y_shape) == 1:

        w = np.ones(self._y_shape[0], np.float32)

      else:

        w = np.ones((self._y_shape[0], 1), np.float32)

import deepchem

import warnings

import logging

from typing import List, Optional, Iterator

from typing import List, Optional, Iterator, Any

from deepchem.utils.genomics import encode_bio_sequence as encode_sequence, encode_fasta_sequence as fasta_sequence, seq_one_hot_encode as seq_one_hotencode

logger = logging.getLogger(__name__)

    raise ValueError("Unrecognized extension %s" % file_extension)

def load_data(input_files, shard_size=None):

def load_data(input_files: List[str],

              shard_size: Optional[int] = None) -> Iterator[Any]:

  """Loads data from disk.

  For CSV files, supports sharded loading for large files.

      yield load_pickle_from_disk(input_file)

def load_sdf_files(input_files, clean_mols, tasks=[]):

def load_sdf_files(input_files: List[str],

                   clean_mols: bool = True,

                   tasks: List[str] = []) -> List[pd.DataFrame]:

  """Load SDF file into dataframe.

  Parameters

  -------

  dataframes: list

    This function returns a list of pandas dataframes. Each dataframe will

    columns `('mol_id', 'smiles', 'mol')`.

    contain columns `('mol_id', 'smiles', 'mol')`.

  """

  from rdkit import Chem

  dataframes = []

  return dataframes

def load_csv_files(filenames, shard_size=None):

def load_csv_files(filenames: List[str],

                   shard_size: Optional[int] = None) -> Iterator[pd.DataFrame]:

  """Load data as pandas dataframe.

  Parameters

  ----------

  input_files: list[str]

  filenames: list[str]

    List of filenames

  shard_size: int, optional (default None) 

    The shard size to yield at one time.