code: """
  FACTOR dataset loader
  """
  import os
  import logging
  import time
  
  import numpy as np
  import deepchem
  from deepchem.molnet.load_function.kaggle_features import merck_descriptors
  
  logger = logging.getLogger(__name__)
  
  TRAIN_URL = "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/FACTORS_training_disguised_combined_full.csv.gz"
  VALID_URL = "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/FACTORS_test1_disguised_combined_full.csv.gz"
  TEST_URL = "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/FACTORS_test2_disguised_combined_full.csv.gz"
  
  TRAIN_FILENAME = "FACTORS_training_disguised_combined_full.csv.gz"
  VALID_FILENAME = "FACTORS_test1_disguised_combined_full.csv.gz"
  TEST_FILENAME = "FACTORS_test2_disguised_combined_full.csv.gz"
  
  
  def remove_missing_entries(dataset):
    """Remove missing entries.
  
    Some of the datasets have missing entries that sneak in as zero'd out
    feature vectors. Get rid of them.
    """
    for i, (X, y, w, ids) in enumerate(dataset.itershards()):
      available_rows = X.any(axis=1)
      logger.info("Shard %d has %d missing entries." %
                  (i, np.count_nonzero(~available_rows)))
      X = X[available_rows]
      y = y[available_rows]
      w = w[available_rows]
      ids = ids[available_rows]
      dataset.set_shard(i, X, y, w, ids)
  
  
  def get_transformers(train_dataset):
    """Gets transformers applied to the dataset"""
  
    transformers = list()
    # TODO: Check if anything needs to be added
  
    return transformers
  
  
  def gen_factors(FACTORS_tasks,
                  data_dir,
                  train_dir,
                  valid_dir,
                  test_dir,
                  shard_size=2000):
    """Loads the FACTORS dataset; does not do train/test split"""
  
    time1 = time.time()
  
    train_files = os.path.join(data_dir, TRAIN_FILENAME)
    valid_files = os.path.join(data_dir, VALID_FILENAME)
    test_files = os.path.join(data_dir, TEST_FILENAME)
  
    if not os.path.exists(train_files):
      logger.info("Downloading train file...")
      deepchem.utils.data_utils.download_url(url=TRAIN_URL, dest_dir=data_dir)
      logger.info("Training file download complete.")
  
      logger.info("Downloading validation file...")
      deepchem.utils.data_utils.download_url(url=VALID_URL, dest_dir=data_dir)
      logger.info("Validation file download complete.")
  
      logger.info("Downloading test file...")
      deepchem.utils.data_utils.download_url(url=TEST_URL, dest_dir=data_dir)
      logger.info("Test file download complete")
  
    # Featurize the FACTORS dataset
    logger.info("About to featurize the FACTORS dataset")
    featurizer = deepchem.feat.UserDefinedFeaturizer(merck_descriptors)
    loader = deepchem.data.UserCSVLoader(
        tasks=FACTORS_tasks, id_field="Molecule", featurizer=featurizer)
  
    logger.info("Featurizing the train dataset...")
    train_dataset = loader.featurize(train_files, shard_size=shard_size)
  
    logger.info("Featurizing the validation dataset...")
    valid_dataset = loader.featurize(valid_files, shard_size=shard_size)
  
    logger.info("Featurizing the test dataset...")
    test_dataset = loader.featurize(test_files, shard_size=shard_size)
  
    logger.info("Remove missing entries from dataset")
    remove_missing_entries(train_dataset)
    remove_missing_entries(valid_dataset)
    remove_missing_entries(test_dataset)
  
    # Shuffle the training data
    logger.info("Shuffling the training dataset")
    train_dataset.sparse_shuffle()
  
    # Apply transformations
    logger.info("Transforming datasets with transformers")
    transformers = get_transformers(train_dataset)
  
    for transformer in transformers:
      logger.info("Performing transformations with {}".format(
          transformer.__class__.__name__))
  
      logger.info("Transforming the training dataset...")
      train_dataset = transformer.transform(train_dataset)
  
      logger.info("Transforming the validation dataset...")
      valid_dataset = transformer.transform(valid_dataset)
  
      logger.info("Transforming the test dataset...")
      test_dataset = transformer.transform(test_dataset)
  
    logger.info("Transformations complete.")
    logger.info("Moving datasets to corresponding directories")
  
    train_dataset.move(train_dir)
    logger.info("Train dataset moved.")
  
    valid_dataset.move(valid_dir)
    logger.info("Validation dataset moved.")
  
    test_dataset.move(test_dir)
    logger.info("Test dataset moved.")
  
    time2 = time.time()
  
    # TIMING
    logger.info("TIMING: FACTORS fitting took %0.3f s" % (time2 - time1))
  
    return train_dataset, valid_dataset, test_dataset
  
  
  def load_factors(shard_size=2000, featurizer=None, split=None, reload=True):
    """Loads FACTOR dataset; does not do train/test split
  
    The Factors dataset is an in-house dataset from Merck that was first introduced in the following paper:
    Ramsundar, Bharath, et al. "Is multitask deep learning practical for pharma?." Journal of chemical information and modeling 57.8 (2017): 2068-2076.
  
    It contains 1500 Merck in-house compounds that were measured
    for IC50 of inhibition on 12 serine proteases. Unlike most of
    the other datasets featured in MoleculeNet, the Factors
    collection does not have structures for the compounds tested
    since they were proprietary Merck compounds. However, the
    collection does feature pre-computed descriptors for these
    compounds.
  
    Note that the original train/valid/test split from the source
    data was preserved here, so this function doesn't allow for
    alternate modes of splitting. Similarly, since the source data
    came pre-featurized, it is not possible to apply alternative
    featurizations.
  
    Parameters
    ----------
    shard_size: int, optional
      Size of the DiskDataset shards to write on disk
    featurizer: optional
      Ignored since featurization pre-computed
    split: optional
      Ignored since split pre-computed
    reload: bool, optional
      Whether to automatically re-load from disk
  
    """
  
    FACTORS_tasks = [
        'T_00001', 'T_00002', 'T_00003', 'T_00004', 'T_00005', 'T_00006',
        'T_00007', 'T_00008', 'T_00009', 'T_00010', 'T_00011', 'T_00012'
    ]
  
    data_dir = deepchem.utils.data_utils.get_data_dir()
    data_dir = os.path.join(data_dir, "factors")
  
    if not os.path.exists(data_dir):
      os.mkdir(data_dir)
  
    train_dir = os.path.join(data_dir, "train_dir")
    valid_dir = os.path.join(data_dir, "valid_dir")
    test_dir = os.path.join(data_dir, "test_dir")
  
    if (os.path.exists(train_dir) and os.path.exists(valid_dir) and
        os.path.exists(test_dir)):
  
      logger.info("Reloading existing datasets")
      train_dataset = deepchem.data.DiskDataset(train_dir)
      valid_dataset = deepchem.data.DiskDataset(valid_dir)
      test_dataset = deepchem.data.DiskDataset(test_dir)
  
    else:
      logger.info("Featurizing datasets")
      train_dataset, valid_dataset, test_dataset = gen_factors(
          FACTORS_tasks=FACTORS_tasks,
          data_dir=data_dir,
          train_dir=train_dir,
          valid_dir=valid_dir,
          test_dir=test_dir,
          shard_size=shard_size)
  
    transformers = get_transformers(train_dataset)
  
    return FACTORS_tasks, (train_dataset, valid_dataset,
                           test_dataset), transformers
  
apis: numpy.count_nonzero