Data Loader fixes

import time

import sys

import logging

import warnings

from deepchem.utils.save import load_csv_files

from deepchem.utils.save import load_sdf_files

from deepchem.utils.genomics import encode_fasta_sequence

def _convert_df_to_numpy(df, tasks):

  """Transforms a dataframe containing deepchem input into numpy arrays"""

  """Transforms a dataframe containing deepchem input into numpy arrays

  This is a private helper method intended to help parse labels and

  weights arrays from a pandas dataframe. Here `df` is a dataframe

  which has columns for each task in `tasks`. These labels are

  extracted into a labels array `y`. Weights `w` are initialized to

  all ones, but weights for any missing labels are set to 0.

  Parameters

  ----------

  df: pd.DataFrame

    Pandas dataframe with columns for all tasks

  tasks: list

    List of tasks

  """

  n_samples = df.shape[0]

  n_tasks = len(tasks)

def _featurize_smiles_df(df, featurizer, field, log_every_n=1000):

  """Featurize individual compounds in dataframe.

  Given a featurizer that operates on individual chemical

  compounds or macromolecules, compute & add features for that

  compound to the features dataframe

  Private helper that given a featurizer that operates on individual

  chemical compounds or macromolecules, compute & add features for

  that compound to the features dataframe

  Parameters

  ----------

  df: pd.DataFrame

    DataFrame that holds SMILES strings

  featurizer: Featurizer

    A featurizer object

  field: str

    The name of a column in `df` that holds SMILES strings

  log_every_n: int, optional (default 1000)

    Emit a logging statement every `log_every_n` rows.

  """

  sample_elems = df[field].tolist()

def _get_user_specified_features(df, featurizer):

  """Extract and merge user specified features.

  Merge features included in dataset provided by user

  into final features dataframe

  Private helper methods that merges features included in dataset

  provided by user into final features dataframe

  Three types of featurization here:

      -) PDB files for interacting molecules.

    3) User specified featurizations.

  Parameters

  ----------

  df: pd.DataFrame

    DataFrame that holds SMILES strings

  featurizer: Featurizer

    A featurizer object

  """

  time1 = time.time()

  df[featurizer.feature_fields] = df[featurizer.feature_fields].apply(

  general framework for loading data into DeepChem. This class should

  never be instantiated directly.  To load your own type of data, make

  a subclass of `DataLoader` and provide your own implementation for

  the `featurize`.

  the `create_dataset()` method.

  To construct a `Dataset` from input data, first instantiate a

  concrete data loader (that is, an object which is an instance of a

  subclass of `DataLoader`) with a given `Featurizer` object. Then

  call the data loader's `featurize()` method on a list of input files

  that hold the source data to process. Note that each subclass of

  `DataLoader` is specialized to handle one type of input data so you

  will have to pick the loader class suitable for your input data

  call the data loader's `create_dataset()` method on a list of input

  files that hold the source data to process. Note that each subclass

  of `DataLoader` is specialized to handle one type of input data so

  you will have to pick the loader class suitable for your input data

  type.

  Note that it isn't necessary to use a data loader to process input

  def featurize(self, input_files, data_dir=None, shard_size=8192):

    """Featurize provided files and write to specified location.

    DEPRECATED: This method is now a wrapper for `create_dataset()`

    and calls that method under the hood.

    For large datasets, automatically shards into smaller chunks

    for convenience. This implementation assumes that the helper

    methods `_get_shards` and `_featurize_shard` are implemented and

      Directory to store featurized dataset.

    shard_size: int, optional

      Number of examples stored in each shard.

    Returns

    -------

    A `Dataset` object containing a featurized representation of data

    from `input_files`.

    """

    warnings.warn(

        "featurize() is deprecated and has been renamed to create_dataset(). featurize() will be removed in DeepChem 3.0",

        FutureWarning)

    return self.create_dataset(input_files, data_dir, shard_size)

  def create_dataset(self, input_files, data_dir=None, shard_size=8192):

    """Creates and returns a `Dataset` object by featurizing provided files.

    Reads in `input_files` and uses `self.featurizer` to featurize the

    data in these input files.  For large files, automatically shards

    into smaller chunks of `shard_size` datapoints for convenience.

    Returns a `Dataset` object that contains the featurized dataset.

    This implementation assumes that the helper methods `_get_shards`

    and `_featurize_shard` are implemented and that each shard

    returned by `_get_shards` is a pandas dataframe.  You may choose

    to reuse or override this method in your subclass implementations.

    Parameters

    ----------

    input_files: list

      List of input filenames.

    data_dir: str, optional

      Directory to store featurized dataset.

    shard_size: int, optional

      Number of examples stored in each shard.

    Returns

    -------

    A `Dataset` object containing a featurized representation of data

    from `input_files`.

    """

    logger.info("Loading raw samples now.")

    logger.info("shard_size: %d" % shard_size)

    If you chose to override `featurize()` directly you don't need to

    override this helper method.

    Parameters

    ----------

    input_files: list

      List of input filenames.

    shard_size: int, optional

      Number of examples stored in each shard.

    """

    raise NotImplementedError