Commit 4c2e1a01 authored by alat-rights's avatar alat-rights
Browse files

yapf / mypy / flake8

parent 5242fa59

deepchem/data/data_loader.py

+27 −20
Original line number Diff line number Diff line
@@ -488,8 +488,8 @@ class UserCSVLoader(CSVLoader): 
    shard[feature_fields] = shard[feature_fields].apply(pd.to_numeric)

    X_shard = shard[feature_fields].to_numpy()

    time2 = time.time()

    logger.info(

        "TIMING: user specified processing took %0.3f s" % (time2 - time1))

    logger.info("TIMING: user specified processing took %0.3f s" %

                (time2 - time1))

    return (X_shard, np.ones(len(X_shard), dtype=bool))
@@ -831,8 +831,7 @@ class SDFLoader(DataLoader): 
    Iterator[pd.DataFrame]

      Iterator over shards

    """

    return load_sdf_files(

        input_files=input_files,

    return load_sdf_files(input_files=input_files,

                          clean_mols=self.sanitize,

                          tasks=self.tasks,

                          shard_size=shard_size)
@@ -875,7 +874,11 @@ class FASTALoader(DataLoader): 
  learning tasks.

  """



  def __init__(self, featurizer = OneHotFeaturizer, charset: Union[str, tuple, None] = "ATCGN", max_length: Optional[int] = None):

  def __init__(self,

               featurizer=OneHotFeaturizer,

               charset: Union[str, tuple, None] = "ATCGN",

               max_length: Optional[int] = None,

               auto_add_annotations: bool = False):

    """Initialize FASTALoader.



    Parameters
@@ -904,18 +907,24 @@ class FASTALoader(DataLoader): 
      string that is being featurized. max_length is passed to the featurizer.



      OneHotFeaturizer pads all strings to max_length with spaces.



    auto_add_annotations: bool (default False)

      Whether create_dataset will automatically add [CLS] and [SEP] annotations

      to the sequences it reads in order to assist tokenization.

      Keep False if your FASTA file already includes [CLS] and [SEP] annotations.

   """

    charsets = {

        "protein": ('A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L',

                    'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X',

                    'Y', 'Z', '*', '-'),

      "nucleic": ('A', 'C', 'G', 'T', 'U', '(i)', 'R', 'Y', 'K', 'M', 'S', 'W',

                  'B', 'D', 'H', 'V', 'N', '-'),

        "nucleic": ('A', 'C', 'G', 'T', 'U', '(i)', 'R', 'Y', 'K', 'M', 'S',

                    'W', 'B', 'D', 'H', 'V', 'N', '-'),

        "ATCGN": ('A', 'T', 'C', 'G')

    }



    # Initialize self.max_length and self.charset

    # Initialize instance variables

    self.max_length = max_length

    self.auto_add_annotations = auto_add_annotations

    if isinstance(charset, str):

      try:

        self.charset = charsets[charset]
@@ -942,7 +951,6 @@ class FASTALoader(DataLoader): 


  def create_dataset(self,

                     input_files: OneOrMany[str],

                     auto_add_annotations: bool = False,

                     data_dir: Optional[str] = None,

                     shard_size: Optional[int] = None) -> DiskDataset:

    """Creates a `Dataset` from input FASTA files.
@@ -954,10 +962,6 @@ class FASTALoader(DataLoader): 
    ----------

    input_files: List[str]

      List of fasta files.

    auto_add_annotations: bool (default False)

      Whether create_dataset will automatically add [CLS] and [SEP] annotations

      to the sequences it reads in order to assist tokenization.

      Keep False if your FASTA file already includes [CLS] and [SEP] annotations.

    data_dir: str, optional (default None)

      Name of directory where featurized data is stored.

    shard_size: int, optional (default None)
@@ -987,6 +991,7 @@ class FASTALoader(DataLoader): 
      """

      Convert the FASTA file to a numpy array of FASTA-format strings.

      """



      def _generate_sequences(fasta_file, header_mark=">") -> np.array:

        """

        Uses a fasta_file to create a numpy array of annotated FASTA-format strings
@@ -1010,7 +1015,8 @@ class FASTALoader(DataLoader): 
      def _add_sequence(sequences: np.array, sequence: list) -> np.array:

        # Handle empty sequence

        if sequence is None or len(sequence) <= 0:

          logger.warning("Attempting to add empty sequence, returning empty array...")

          logger.warning(

              "Attempting to add empty sequence, returning empty array...")

          return np.array([])

        # Annotate start/stop of sequence

        if auto_add_annotations:
@@ -1140,11 +1146,12 @@ class ImageLoader(DataLoader): 


    if in_memory:

      if data_dir is None:

        return NumpyDataset(

            load_image_files(image_files), y=labels, w=weights, ids=image_files)

        return NumpyDataset(load_image_files(image_files),

                            y=labels,

                            w=weights,

                            ids=image_files)

      else:

        dataset = DiskDataset.from_numpy(

            load_image_files(image_files),

        dataset = DiskDataset.from_numpy(load_image_files(image_files),

                                         y=labels,

                                         w=weights,

                                         ids=image_files,
@@ -1293,8 +1300,8 @@ class InMemoryLoader(DataLoader): 


  # FIXME: Signature of "_featurize_shard" incompatible with supertype "DataLoader"

  def _featurize_shard(  # type: ignore[override]

      self, shard: List, global_index: int

  ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:

      self, shard: List, global_index: int) -> Tuple[np.ndarray, np.ndarray,

                                                     np.ndarray, np.ndarray]:

    """Featurizes a shard of an input data.



    Parameters

deepchem/data/tests/test_data_loader.py

+19 −14
Original line number Diff line number Diff line
@@ -12,8 +12,9 @@ def test_unlabelled(): 
  current_dir = os.path.dirname(os.path.abspath(__file__))

  input_file = os.path.join(current_dir, "../../data/tests/no_labels.csv")

  featurizer = dc.feat.CircularFingerprint(size=1024)

  loader = dc.data.CSVLoader(

      tasks=[], feature_field="smiles", featurizer=featurizer)

  loader = dc.data.CSVLoader(tasks=[],

                             feature_field="smiles",

                             featurizer=featurizer)

  dataset = loader.create_dataset(input_file)

  assert len(dataset.X)
@@ -26,8 +27,9 @@ def test_scaffold_test_train_valid_test_split(): 
  featurizer = dc.feat.CircularFingerprint(size=1024)



  input_file = os.path.join(current_dir, input_file)

  loader = dc.data.CSVLoader(

      tasks=tasks, feature_field="smiles", featurizer=featurizer)

  loader = dc.data.CSVLoader(tasks=tasks,

                             feature_field="smiles",

                             featurizer=featurizer)



  dataset = loader.create_dataset(input_file)
@@ -48,8 +50,9 @@ def test_scaffold_test_train_test_split(): 
  featurizer = dc.feat.CircularFingerprint(size=1024)



  input_file = os.path.join(current_dir, input_file)

  loader = dc.data.CSVLoader(

      tasks=tasks, feature_field="smiles", featurizer=featurizer)

  loader = dc.data.CSVLoader(tasks=tasks,

                             feature_field="smiles",

                             featurizer=featurizer)



  dataset = loader.create_dataset(input_file)
@@ -68,8 +71,9 @@ def test_random_test_train_valid_test_split(): 
  featurizer = dc.feat.CircularFingerprint(size=1024)



  input_file = os.path.join(current_dir, input_file)

  loader = dc.data.CSVLoader(

      tasks=tasks, feature_field="smiles", featurizer=featurizer)

  loader = dc.data.CSVLoader(tasks=tasks,

                             feature_field="smiles",

                             featurizer=featurizer)



  dataset = loader.create_dataset(input_file)
@@ -88,8 +92,9 @@ def test_random_test_train_test_split(): 
  tasks = ["log-solubility"]

  input_file = os.path.join(current_dir, "../../models/tests/example.csv")

  featurizer = dc.feat.CircularFingerprint(size=1024)

  loader = dc.data.CSVLoader(

      tasks=tasks, feature_field="smiles", featurizer=featurizer)

  loader = dc.data.CSVLoader(tasks=tasks,

                             feature_field="smiles",

                             featurizer=featurizer)



  dataset = loader.create_dataset(input_file)
@@ -107,8 +112,7 @@ def test_log_solubility_dataset(): 
  input_file = os.path.join(current_dir, input_file)



  tasks = ["log-solubility"]

  loader = dc.data.CSVLoader(

      tasks=tasks,

  loader = dc.data.CSVLoader(tasks=tasks,

                             feature_field="smiles",

                             featurizer=dc.feat.CircularFingerprint(size=1024))

  dataset = loader.create_dataset(input_file)
@@ -126,8 +130,9 @@ def test_dataset_move(): 


  featurizer = dc.feat.CircularFingerprint(size=1024)

  tasks = ["log-solubility"]

  loader = dc.data.CSVLoader(

      tasks=tasks, feature_field="smiles", featurizer=featurizer)

  loader = dc.data.CSVLoader(tasks=tasks,

                             feature_field="smiles",

                             featurizer=featurizer)

  featurized_dataset = loader.create_dataset(dataset_file, data_dir)

  n_dataset = len(featurized_dataset)

deepchem/feat/molecule_featurizers/one_hot_featurizer.py

+3 −1
Original line number Diff line number Diff line
@@ -32,7 +32,9 @@ class OneHotFeaturizer(Featurizer): 
  It does not need RDKit to be installed to work with arbitrary strings.

  """



  def __init__(self, charset: List[str] = ZINC_CHARSET, max_length: Optional[int] = 100):

  def __init__(self,

               charset: List[str] = ZINC_CHARSET,

               max_length: Optional[int] = 100):

    """Initialize featurizer.



    Parameters

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