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kinase_datasets.py
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kinase_datasets.py
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"""
KINASE dataset loader
"""
import os
import logging
import time
import numpy as np
import deepchem
from deepchem.molnet.load_function.kaggle_features import merck_descriptors
TRAIN_URL = 'https://s3-us-west-1.amazonaws.com/deepchem.io/datasets/KINASE_training_disguised_combined_full.csv.gz'
VALID_URL = 'https://s3-us-west-1.amazonaws.com/deepchem.io/datasets/KINASE_test1_disguised_combined_full.csv.gz'
TEST_URL = 'https://s3-us-west-1.amazonaws.com/deepchem.io/datasets/KINASE_test2_disguised_combined_full.csv.gz'
TRAIN_FILENAME = "KINASE_training_disguised_combined_full.csv.gz"
VALID_FILENAME = "KINASE_test1_disguised_combined_full.csv.gz"
TEST_FILENAME = "KINASE_test2_disguised_combined_full.csv.gz"
logger = logging.getLogger(__name__)
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"""
#TODO: Check for this
transformers = list()
return transformers
def gen_kinase(KINASE_tasks,
train_dir,
valid_dir,
test_dir,
data_dir,
shard_size=2000):
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)
# Download files if they don't exist
if not os.path.exists(train_files):
logger.info("Downloading training file...")
deepchem.utils.download_url(url=TRAIN_URL, dest_dir=data_dir)
logger.info("Training file download complete.")
logger.info("Downloading validation file...")
deepchem.utils.download_url(url=VALID_URL, dest_dir=data_dir)
logger.info("Validation file download complete.")
logger.info("Downloading test file...")
deepchem.utils.download_url(url=TEST_URL, dest_dir=data_dir)
logger.info("Test file download complete")
# Featurize the KINASE dataset
logger.info("About to featurize KINASE dataset.")
featurizer = deepchem.feat.UserDefinedFeaturizer(merck_descriptors)
loader = deepchem.data.UserCSVLoader(
tasks=KINASE_tasks, id_field="Molecule", featurizer=featurizer)
logger.info("Featurizing train datasets...")
train_dataset = loader.featurize(
input_files=train_files, shard_size=shard_size)
logger.info("Featurizing validation datasets...")
valid_dataset = loader.featurize(
input_files=valid_files, shard_size=shard_size)
logger.info("Featurizing test datasets....")
test_dataset = loader.featurize(input_files=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("Transformating 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: KINASE fitting took %0.3f s" % (time2 - time1))
return train_dataset, valid_dataset, test_dataset
def load_kinase(shard_size=2000, featurizer=None, split=None, reload=True):
"""Loads Kinase datasets, does not do train/test split
The Kinase 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 2500 Merck in-house compounds that were measured
for IC50 of inhibition on 99 protein kinases. Unlike most of
the other datasets featured in MoleculeNet, the Kinase
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
"""
KINASE_tasks = [
'T_00013', 'T_00014', 'T_00015', 'T_00016', 'T_00017', 'T_00018',
'T_00019', 'T_00020', 'T_00021', 'T_00022', 'T_00023', 'T_00024',
'T_00025', 'T_00026', 'T_00027', 'T_00028', 'T_00029', 'T_00030',
'T_00031', 'T_00032', 'T_00033', 'T_00034', 'T_00035', 'T_00036',
'T_00037', 'T_00038', 'T_00039', 'T_00040', 'T_00041', 'T_00042',
'T_00043', 'T_00044', 'T_00045', 'T_00046', 'T_00047', 'T_00048',
'T_00049', 'T_00050', 'T_00051', 'T_00052', 'T_00053', 'T_00054',
'T_00055', 'T_00056', 'T_00057', 'T_00058', 'T_00059', 'T_00060',
'T_00061', 'T_00062', 'T_00063', 'T_00064', 'T_00065', 'T_00066',
'T_00067', 'T_00068', 'T_00069', 'T_00070', 'T_00071', 'T_00072',
'T_00073', 'T_00074', 'T_00075', 'T_00076', 'T_00077', 'T_00078',
'T_00079', 'T_00080', 'T_00081', 'T_00082', 'T_00083', 'T_00084',
'T_00085', 'T_00086', 'T_00087', 'T_00088', 'T_00089', 'T_00090',
'T_00091', 'T_00092', 'T_00093', 'T_00094', 'T_00095', 'T_00096',
'T_00097', 'T_00098', 'T_00099', 'T_00100', 'T_00101', 'T_00102',
'T_00103', 'T_00104', 'T_00105', 'T_00106', 'T_00107', 'T_00108',
'T_00109', 'T_00110', 'T_00111'
]
data_dir = deepchem.utils.get_data_dir()
data_dir = os.path.join(data_dir, "kinase")
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_kinase(KINASE_tasks=KINASE_tasks, train_dir=train_dir,
valid_dir=valid_dir, test_dir=test_dir, data_dir=data_dir,
shard_size=shard_size)
transformers = get_transformers(train_dataset)
return KINASE_tasks, (train_dataset, valid_dataset,
test_dataset), transformers