Unverified Commit 7ca3a111 authored by Bharath Ramsundar's avatar Bharath Ramsundar Committed by GitHub
Browse files

Merge pull request #1621 from VIGS25/split-transform-order 

Swapping Split-Transform order
parents 4e382ee8 2a33962f

deepchem/molnet/load_function/bace_datasets.py

+42 −17
Original line number Diff line number Diff line
@@ -57,17 +57,17 @@ def load_bace_regression(featurizer='ECFP', 
      tasks=bace_tasks, smiles_field="mol", featurizer=featurizer)



  dataset = loader.featurize(dataset_file, shard_size=8192)

  if split is None:

    # Initialize transformers

    transformers = [

        deepchem.trans.NormalizationTransformer(

            transform_y=True, dataset=dataset, move_mean=move_mean)

    ]



  logger.info("About to transform data")

    logger.info("Split is None, about to transform data")

    for transformer in transformers:

      dataset = transformer.transform(dataset)



  if split == None:

    return bace_tasks, (dataset, None, None), transformers



  splitters = {
@@ -76,8 +76,20 @@ def load_bace_regression(featurizer='ECFP', 
      'scaffold': deepchem.splits.ScaffoldSplitter()

  }

  splitter = splitters[split]

  logger.info("About to split data using {} splitter".format(split))

  train, valid, test = splitter.train_valid_test_split(dataset)



  transformers = [

      deepchem.trans.NormalizationTransformer(

          transform_y=True, dataset=train, move_mean=move_mean)

  ]



  logger.info("About to transform data.")

  for transformer in transformers:

    train = transformer.transform(train)

    valid = transformer.transform(valid)

    test = transformer.transform(test)



  if reload:

    deepchem.utils.save.save_dataset_to_disk(save_dir, train, valid, test,

                                             transformers)
@@ -122,16 +134,17 @@ def load_bace_classification(featurizer='ECFP', split='random', reload=True): 
      tasks=bace_tasks, smiles_field="mol", featurizer=featurizer)



  dataset = loader.featurize(dataset_file, shard_size=8192)



  if split is None:

    # Initialize transformers

    transformers = [

        deepchem.trans.BalancingTransformer(transform_w=True, dataset=dataset)

    ]



  logger.info("About to transform data")

    logger.info("Split is None, about to transform data")

    for transformer in transformers:

      dataset = transformer.transform(dataset)



  if split == None:

    return bace_tasks, (dataset, None, None), transformers



  splitters = {
@@ -139,9 +152,21 @@ def load_bace_classification(featurizer='ECFP', split='random', reload=True): 
      'random': deepchem.splits.RandomSplitter(),

      'scaffold': deepchem.splits.ScaffoldSplitter()

  }



  splitter = splitters[split]

  logger.info("About to split data using {} splitter".format(split))

  train, valid, test = splitter.train_valid_test_split(dataset)



  transformers = [

      deepchem.trans.BalancingTransformer(transform_w=True, dataset=train)

  ]



  logger.info("About to transform data.")

  for transformer in transformers:

    train = transformer.transform(train)

    valid = transformer.transform(valid)

    test = transformer.transform(test)



  if reload:

    deepchem.utils.save.save_dataset_to_disk(save_dir, train, valid, test,

                                             transformers)

deepchem/molnet/load_function/bbbc_datasets.py

+10 −2
Original line number Diff line number Diff line
@@ -57,6 +57,8 @@ def load_bbbc001(split='index', reload=True): 
  dataset = deepchem.data.DiskDataset.from_numpy(dataset.X, y)



  if split == None:

    transformers = []

    logger.info("Split is None, no transformers used for the dataset.")

    return bbbc001_tasks, (dataset, None, None), transformers



  splitters = {
@@ -67,7 +69,9 @@ def load_bbbc001(split='index', reload=True): 
    raise ValueError("Only index and random splits supported.")

  splitter = splitters[split]



  logger.info("About to split dataset with {} splitter.".format(split))

  train, valid, test = splitter.train_valid_test_split(dataset)

  transformers = []

  all_dataset = (train, valid, test)

  if reload:

    deepchem.utils.save.save_dataset_to_disk(save_dir, train, valid, test,
@@ -121,6 +125,8 @@ def load_bbbc002(split='index', reload=True): 
  dataset = deepchem.data.DiskDataset.from_numpy(dataset.X, y, ids=ids)



  if split == None:

    transformers = []

    logger.info("Split is None, no transformers used for the dataset.")

    return bbbc002_tasks, (dataset, None, None), transformers



  splitters = {
@@ -131,8 +137,10 @@ def load_bbbc002(split='index', reload=True): 
    raise ValueError("Only index and random splits supported.")

  splitter = splitters[split]



  logger.info("About to split dataset with {} splitter.".format(split))

  train, valid, test = splitter.train_valid_test_split(dataset)

  all_dataset = (train, valid, test)

  transformers = []

  if reload:

    deepchem.utils.save.save_dataset_to_disk(save_dir, train, valid, test,

                                             transformers)

deepchem/molnet/load_function/bbbp_datasets.py

+20 −8
Original line number Diff line number Diff line
@@ -45,16 +45,17 @@ def load_bbbp(featurizer='ECFP', split='random', reload=True): 
  loader = deepchem.data.CSVLoader(

      tasks=bbbp_tasks, smiles_field="smiles", featurizer=featurizer)

  dataset = loader.featurize(dataset_file, shard_size=8192)



  if split is None:

    # Initialize transformers

    transformers = [

        deepchem.trans.BalancingTransformer(transform_w=True, dataset=dataset)

    ]



  logger.info("About to transform data")

    logger.info("Split is None, about to transform data")

    for transformer in transformers:

      dataset = transformer.transform(dataset)



  if split == None:

    return bbbp_tasks, (dataset, None, None), transformers



  splitters = {
@@ -63,8 +64,19 @@ def load_bbbp(featurizer='ECFP', split='random', reload=True): 
      'scaffold': deepchem.splits.ScaffoldSplitter()

  }

  splitter = splitters[split]

  logger.info("About to split data with {} splitter.".format(split))

  train, valid, test = splitter.train_valid_test_split(dataset)



  # Initialize transformers

  transformers = [

      deepchem.trans.BalancingTransformer(transform_w=True, dataset=train)

  ]



  for transformer in transformers:

    train = transformer.transform(train)

    valid = transformer.transform(valid)

    test = transformer.transform(test)



  if reload:

    deepchem.utils.save.save_dataset_to_disk(save_dir, train, valid, test,

                                             transformers)

deepchem/molnet/load_function/cell_counting_datasets.py

+4 −1
Original line number Diff line number Diff line
@@ -43,6 +43,7 @@ def load_cell_counting(split=None, reload=True): 
  transformers = []



  if split == None:

    logger.info("Split is None, no transformers used.")

    return cell_counting_tasks, (dataset, None, None), transformers



  splitters = {
@@ -53,7 +54,9 @@ def load_cell_counting(split=None, reload=True): 
    raise ValueError("Only index and random splits supported.")

  splitter = splitters[split]



  logger.info("About to split dataset with {} splitter.".format(split))

  train, valid, test = splitter.train_valid_test_split(dataset)

  transformers = []

  all_dataset = (train, valid, test)

  if reload:

    deepchem.utils.save.save_dataset_to_disk(save_dir, train, valid, test,

deepchem/molnet/load_function/chembl_datasets.py

+26 −24
Original line number Diff line number Diff line
@@ -80,35 +80,27 @@ def load_chembl(shard_size=2000, 


  if split == "year":

    logger.info("Featurizing train datasets")

    train_dataset = loader.featurize(train_files, shard_size=shard_size)

    train = loader.featurize(train_files, shard_size=shard_size)

    logger.info("Featurizing valid datasets")

    valid_dataset = loader.featurize(valid_files, shard_size=shard_size)

    valid = loader.featurize(valid_files, shard_size=shard_size)

    logger.info("Featurizing test datasets")

    test_dataset = loader.featurize(test_files, shard_size=shard_size)

    test = loader.featurize(test_files, shard_size=shard_size)

  else:

    dataset = loader.featurize(dataset_path, shard_size=shard_size)

  # Initialize transformers

  logger.info("About to transform data")

  if split == "year":

    transformers = [

        deepchem.trans.NormalizationTransformer(

            transform_y=True, dataset=train_dataset)

    ]

    for transformer in transformers:

      train = transformer.transform(train_dataset)

      valid = transformer.transform(valid_dataset)

      test = transformer.transform(test_dataset)

  else:



  if split is None:

    transformers = [

        deepchem.trans.NormalizationTransformer(

            transform_y=True, dataset=dataset)

    ]



    logger.info("Split is None, about to transform data.")

    for transformer in transformers:

      dataset = transformer.transform(dataset)



  if split == None:

    return chembl_tasks, (dataset, None, None), transformers



  if split != "year":

    splitters = {

        'index': deepchem.splits.IndexSplitter(),

        'random': deepchem.splits.RandomSplitter(),
@@ -119,6 +111,16 @@ def load_chembl(shard_size=2000, 
    logger.info("Performing new split.")

    train, valid, test = splitter.train_valid_test_split(dataset)



  transformers = [

      deepchem.trans.NormalizationTransformer(transform_y=True, dataset=train)

  ]



  logger.info("About to transform data.")

  for transformer in transformers:

    train = transformer.transform(train)

    valid = transformer.transform(valid)

    test = transformer.transform(test)



  if reload:

    deepchem.utils.save.save_dataset_to_disk(save_dir, train, valid, test,

                                             transformers)

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