def main(active_filename, decoy_filename, assay_id, target, with_assay_id=True,
with_target=True, phenotype=None, output_filename=None,
mol_id_prefix=None, output_format='.pkl.gz'):
rows = []
for outcome, filename in zip(['active', 'inactive'],
[active_filename, decoy_filename]):
this_phenotype = phenotype
if outcome == 'inactive' and phenotype is not None:
this_phenotype = 'inactive'
with serial.MolReader().open(filename) as reader:
this_rows = get_rows(reader, outcome, this_phenotype, mol_id_prefix)
rows.extend(this_rows)
# create dataframe
df = pd.DataFrame(rows)
# sanity check for duplicate mol_ids
assert len(np.unique(df['mol_id'])) == len(df)
# add assay_id and target columns
if with_assay_id:
df.loc[:, 'assay_id'] = assay_id
if with_target:
df.loc[:, 'target'] = target
if output_filename is None:
output_filename = '{}.{}'.format(assay_id, output_format)
print '{}\t{}\t{}\t{}'.format(assay_id, target, output_filename, len(df))
write_dataframe(df, output_filename)
def write_output_file(data, filename, compression_level=3):
"""
Pickle output data, possibly to a compressed file.
Parameters
----------
data : object
Object to pickle in output file.
filename : str
Output filename. Should end with .joblib, .pkl, or .pkl.gz.
compression_level : int, optional (default 3)
Compression level (0-9) to use with joblib.dump.
"""
if filename.endswith('.joblib'):
joblib.dump(data, filename, compress=compression_level)
else:
write_dataframe(data, filename)
def write_output_file(data, filename, compression_level=3):
"""
Pickle output data, possibly to a compressed file.
Parameters
----------
data : object
Object to pickle in output file.
filename : str
Output filename. Should end with .joblib, .pkl, or .pkl.gz.
compression_level : int, optional (default 3)
Compression level (0-9) to use with joblib.dump.
"""
if filename.endswith('.joblib'):
joblib.dump(data, filename, compress=compression_level)
else:
write_dataframe(data, filename)
def main(active_filename,
decoy_filename,
assay_id,
target,
with_assay_id=True,
with_target=True,
phenotype=None,
output_filename=None,
mol_id_prefix=None,
output_format='.pkl.gz'):
rows = []
for outcome, filename in zip(['active', 'inactive'],
[active_filename, decoy_filename]):
this_phenotype = phenotype
if outcome == 'inactive' and phenotype is not None:
this_phenotype = 'inactive'
with serial.MolReader().open(filename) as reader:
this_rows = get_rows(reader, outcome, this_phenotype,
mol_id_prefix)
rows.extend(this_rows)
# create dataframe
df = pd.DataFrame(rows)
# sanity check for duplicate mol_ids
assert len(np.unique(df['mol_id'])) == len(df)
# add assay_id and target columns
if with_assay_id:
df.loc[:, 'assay_id'] = assay_id
if with_target:
df.loc[:, 'target'] = target
if output_filename is None:
output_filename = '{}.{}'.format(assay_id, output_format)
print '{}\t{}\t{}\t{}'.format(assay_id, target, output_filename, len(df))
write_dataframe(df, output_filename)
def main(dirs, config_filename, map_filename=None, summary_filename=None,
with_aid=True, with_target=True, phenotype=False, id_prefix='CID',
output_format='.pkl.gz'):
aids = set()
targets = set()
total = 0
config = pd.read_csv(config_filename)
summary = []
sid_cid = None
if map_filename is not None:
sid_cid = read_sid_cid_map(map_filename)
if 'aid' not in config.columns:
raise ValueError('Configuration file must contain "aid" column.')
assert len(config) == len(pd.unique(config['aid']))
for this_dir in dirs:
for filename in glob.glob(os.path.join(this_dir, '*.json.gz')):
# get AID from filename so we only have to load relevant assays
aid = int(os.path.basename(filename).split('.')[0])
if aid not in config['aid'].values:
continue
# get configuration for this AID
this_config = config[config['aid'] == aid].iloc[0]
if not with_aid and 'aid' in this_config:
del this_config['aid']
if not with_target and 'target' in this_config:
del this_config['target']
# get data
try:
extractor = PcbaDataExtractor(filename, this_config, with_aid=with_aid)
except NotImplementedError as e:
warnings.warn(e.message)
continue
if phenotype and 'phenotype' not in extractor.config:
warnings.warn('{} has no phenotype'.format(aid))
continue
assert aid == extractor.parser.get_aid() # sanity check for AID match
aids.add(aid)
target = extractor.config.get('target')
targets.add(target)
data = extractor.get_data(sid_cid=sid_cid)
total += len(data)
# add generic molecule ID column
if id_prefix == 'CID':
col = 'cid'
elif id_prefix == 'SID':
col = 'sid'
else:
raise NotImplementedError('Unrecognized ID prefix "{}"'.format(
id_prefix))
ids = []
for i, mol_id in enumerate(data[col]):
try:
ids.append(id_prefix + str(int(mol_id)))
except (TypeError, ValueError):
warnings.warn('No ID for the following row:\n{}'.format(data.loc[i]))
ids.append(None) # can be found with pd.isnull
# skip this assay if there are no valid IDs
if np.all(pd.isnull(ids)):
warnings.warn('No valid IDs for AID {}. Skipping.'.format(aid))
continue
data.loc[:, 'mol_id'] = pd.Series(ids, index=data.index)
# add generic assay ID column
assay_id = 'PCBA-' + str(aid)
if with_aid:
data.loc[:, 'assay_id'] = assay_id
# save dataframe
output_filename = '{}.{}'.format(assay_id, output_format)
print '{}\t{}\t{}\t{}'.format(aid, target, output_filename, len(data))
write_dataframe(data, output_filename)
summary.append({'aid': aid, 'target': target,
'filename': output_filename, 'size': len(data)})
# make sure we found everything
missing = set(config['aid']).difference(aids)
if len(missing):
warnings.warn('Missed AIDs {}'.format(missing))
# save a summary
summary = pd.DataFrame(summary)
if summary_filename is not None:
write_dataframe(summary, summary_filename)
warnings.warn('Found {} assays for {} targets ({} total data points)'.format(
len(aids), len(targets), total))
def main(dirs,
config_filename,
map_filename=None,
summary_filename=None,
with_aid=True,
with_target=True,
phenotype=False,
id_prefix='CID',
output_format='.pkl.gz'):
aids = set()
targets = set()
total = 0
config = pd.read_csv(config_filename)
summary = []
sid_cid = None
if map_filename is not None:
sid_cid = read_sid_cid_map(map_filename)
if 'aid' not in config.columns:
raise ValueError('Configuration file must contain "aid" column.')
assert len(config) == len(pd.unique(config['aid']))
for this_dir in dirs:
for filename in glob.glob(os.path.join(this_dir, '*.json.gz')):
# get AID from filename so we only have to load relevant assays
aid = int(os.path.basename(filename).split('.')[0])
if aid not in config['aid'].values:
continue
# get configuration for this AID
this_config = config[config['aid'] == aid].iloc[0]
if not with_aid and 'aid' in this_config:
del this_config['aid']
if not with_target and 'target' in this_config:
del this_config['target']
# get data
try:
extractor = PcbaDataExtractor(filename,
this_config,
with_aid=with_aid)
except NotImplementedError as e:
warnings.warn(e.message)
continue
if phenotype and 'phenotype' not in extractor.config:
warnings.warn('{} has no phenotype'.format(aid))
continue
assert aid == extractor.parser.get_aid(
) # sanity check for AID match
aids.add(aid)
target = extractor.config.get('target')
targets.add(target)
data = extractor.get_data(sid_cid=sid_cid)
total += len(data)
# add generic molecule ID column
if id_prefix == 'CID':
col = 'cid'
elif id_prefix == 'SID':
col = 'sid'
else:
raise NotImplementedError(
'Unrecognized ID prefix "{}"'.format(id_prefix))
ids = []
for i, mol_id in enumerate(data[col]):
try:
ids.append(id_prefix + str(int(mol_id)))
except (TypeError, ValueError):
warnings.warn('No ID for the following row:\n{}'.format(
data.loc[i]))
ids.append(None) # can be found with pd.isnull
# skip this assay if there are no valid IDs
if np.all(pd.isnull(ids)):
warnings.warn('No valid IDs for AID {}. Skipping.'.format(aid))
continue
data.loc[:, 'mol_id'] = pd.Series(ids, index=data.index)
# add generic assay ID column
assay_id = 'PCBA-' + str(aid)
if with_aid:
data.loc[:, 'assay_id'] = assay_id
# save dataframe
output_filename = '{}.{}'.format(assay_id, output_format)
print '{}\t{}\t{}\t{}'.format(aid, target, output_filename,
len(data))
write_dataframe(data, output_filename)
summary.append({
'aid': aid,
'target': target,
'filename': output_filename,
'size': len(data)
})
# make sure we found everything
missing = set(config['aid']).difference(aids)
if len(missing):
warnings.warn('Missed AIDs {}'.format(missing))
# save a summary
summary = pd.DataFrame(summary)
if summary_filename is not None:
write_dataframe(summary, summary_filename)
warnings.warn(
'Found {} assays for {} targets ({} total data points)'.format(
len(aids), len(targets), total))
