def filter_pairs(input_sharded_path, output_root, bsa, against_path,
shuffle_buffer):
input_sharded = sh.Sharded.load(input_sharded_path)
keys = input_sharded.get_keys()
if keys != ['ensemble']:
raise RuntimeError('Can only apply to sharded by ensemble.')
output_sharded = sh.Sharded(output_root, keys)
# We form the combined filter by starting with the identity filter and
# composing with further filters.
filter_fn = filters.identity_filter
filter_fn = filters.compose(
atom3d.filters.pdb.form_molecule_type_filter(allowed=['prot']),
filter_fn)
filter_fn = filters.compose(filters.form_size_filter(min_size=50),
filter_fn)
filter_fn = filters.compose(atom3d.filters.pdb.form_resolution_filter(3.5),
filter_fn)
filter_fn = filters.compose(
atom3d.filters.pdb.form_source_filter(allowed=['diffraction', 'EM']),
filter_fn)
if bsa is not None:
filter_fn = filters.compose(form_bsa_filter(bsa, 500), filter_fn)
if against_path is not None:
against = sh.Sharded.load(against_path)
filter_fn = filters.compose(
atom3d.filters.sequence.form_seq_filter_against(against, 0.3),
filter_fn)
filter_fn = filters.compose(
form_scop_pair_filter_against(against, 'superfamily'), filter_fn)
sho.filter_sharded(input_sharded, output_sharded, filter_fn)
split(output_sharded, output_root, shuffle_buffer)
def prepare(input_sharded_path, output_root, score_dir):
input_sharded = sh.Sharded.load(input_sharded_path)
if score_dir is not None:
prefix = sh.get_prefix(output_root)
num_shards = sh.get_num_shards(output_root)
keys = input_sharded.get_keys()
filter_sharded = sh.Sharded(f'{prefix:}_filtered@{num_shards:}', keys)
filter_fn = sc.form_score_filter(score_dir)
logger.info('Filtering against score file.')
sho.filter_sharded(input_sharded, filter_sharded, filter_fn)
split(filter_sharded, output_root)
filter_sharded.delete_files()
else:
split(input_sharded, output_root)
def split(input_sharded, output_root):
"""Split temporally and shuffle examples."""
prefix = sh.get_prefix(output_root)
num_shards = sh.get_num_shards(output_root)
# Re-key to ensemble, subunit. To allow for shuffling across targets.
tmp_sharded = sh.Sharded(
f'{prefix:}_rekeyed@{num_shards:}', ['ensemble', 'subunit'])
logger.info("Rekeying")
sho.rekey(input_sharded, tmp_sharded)
keys = tmp_sharded.get_keys()
train_sharded = sh.Sharded(f'{prefix:}_train@{num_shards:}', keys)
val_sharded = sh.Sharded(f'{prefix:}_val@{num_shards:}', keys)
test_sharded = sh.Sharded(f'{prefix:}_test@{num_shards:}', keys)
logger.info("Splitting")
train_filter_fn = filters.form_filter_against_list(TRAIN, 'ensemble')
val_filter_fn = filters.form_filter_against_list(VAL, 'ensemble')
test_filter_fn = filters.form_filter_against_list(TEST, 'ensemble')
sho.filter_sharded(
tmp_sharded, train_sharded, train_filter_fn, num_shards)
sho.filter_sharded(
tmp_sharded, val_sharded, val_filter_fn, num_shards)
sho.filter_sharded(
tmp_sharded, test_sharded, test_filter_fn, num_shards)
tmp_sharded.delete_files()
def split(input_sharded, output_root, info_csv, shuffle_buffer):
"""Split by protein."""
if input_sharded.get_keys() != ['ensemble']:
raise RuntimeError('Can only apply to sharded by ensemble.')
info = pd.read_csv(info_csv)
info['ensemble'] = info.apply(
lambda x: x['ligand'] + '__' + x['active_struc'].split('_')[
2] + '__' + x['inactive_struc'].split('_')[2],
axis=1)
info = info.set_index('ensemble')
# Remove duplicate ensembles.
info = info[~info.index.duplicated()]
ensembles = input_sharded.get_names()['ensemble']
in_use = info.loc[ensembles]
active = in_use[in_use['label'] == 'A']
inactive = in_use[in_use['label'] == 'I']
# Split by protein.
proteins = info['protein'].unique()
i_test, i_val, i_train = splits.random_split(len(proteins), 0.6, 0.2, 0.2)
p_train = proteins[i_train]
p_val = proteins[i_val]
p_test = proteins[i_test]
logger.info(f'Train proteins: {p_train:}')
logger.info(f'Val proteins: {p_val:}')
logger.info(f'Test proteins: {p_test:}')
train = info[info['protein'].isin(p_train)].index.tolist()
val = info[info['protein'].isin(p_val)].index.tolist()
test = info[info['protein'].isin(p_test)].index.tolist()
logger.info(f'{len(train):} train examples, {len(val):} val examples, '
f'{len(test):} test examples.')
keys = input_sharded.get_keys()
prefix = sh.get_prefix(output_root)
num_shards = sh.get_num_shards(output_root)
train_sharded = sh.Sharded(f'{prefix:}_train@{num_shards:}', keys)
val_sharded = sh.Sharded(f'{prefix:}_val@{num_shards:}', keys)
test_sharded = sh.Sharded(f'{prefix:}_test@{num_shards:}', keys)
train_filter_fn = filters.form_filter_against_list(train, 'ensemble')
val_filter_fn = filters.form_filter_against_list(val, 'ensemble')
test_filter_fn = filters.form_filter_against_list(test, 'ensemble')
sho.filter_sharded(input_sharded, train_sharded, train_filter_fn,
shuffle_buffer)
sho.filter_sharded(input_sharded, val_sharded, val_filter_fn,
shuffle_buffer)
sho.filter_sharded(input_sharded, test_sharded, test_filter_fn,
shuffle_buffer)
def split(input_sharded, output_root, scaffold_data, shuffle_buffer):
"""Split by scaffold."""
if input_sharded.get_keys() != ['ensemble']:
raise RuntimeError('Can only apply to sharded by ensemble.')
logger.info('Process scaffold and ensemble data')
scaffold_list = scaffold_data['Scaffold'].tolist()
ensemble_list = []
for i in range(len(scaffold_data)):
ensemble = ''
ensemble += scaffold_data.iloc[i]['ligand']
ensemble += '__'+scaffold_data.iloc[i]['active_struc'].split('_')[-1]
ensemble += '__'+scaffold_data.iloc[i]['inactive_struc'].split('_')[-1]
ensemble_list.append(ensemble)
ensemble_list = np.array(ensemble_list)
logger.info('Splitting by scaffold')
train_idx, val_idx, test_idx = splits.scaffold_split(scaffold_list)
train = ensemble_list[train_idx].tolist()
val = ensemble_list[val_idx].tolist()
test = ensemble_list[test_idx].tolist()
keys = input_sharded.get_keys()
if keys != ['ensemble']:
raise RuntimeError('Can only apply to sharded by ensemble.')
prefix = sh.get_prefix(output_root)
num_shards = sh.get_num_shards(output_root)
train_sharded = sh.Sharded(f'{prefix:}_train@{num_shards:}', keys)
val_sharded = sh.Sharded(f'{prefix:}_val@{num_shards:}', keys)
test_sharded = sh.Sharded(f'{prefix:}_test@{num_shards:}', keys)
logger.info('Writing sets')
train_filter_fn = filters.form_filter_against_list(train, 'ensemble')
val_filter_fn = filters.form_filter_against_list(val, 'ensemble')
test_filter_fn = filters.form_filter_against_list(test, 'ensemble')
sho.filter_sharded(
input_sharded, train_sharded, train_filter_fn, shuffle_buffer)
sho.filter_sharded(
input_sharded, val_sharded, val_filter_fn, shuffle_buffer)
sho.filter_sharded(
input_sharded, test_sharded, test_filter_fn, shuffle_buffer)
# write splits to text files
np.savetxt(output_root.split('@')[0]+'_train.txt', train, fmt='%s')
np.savetxt(output_root.split('@')[0]+'_val.txt', val, fmt='%s')
np.savetxt(output_root.split('@')[0]+'_test.txt', test, fmt='%s')
def split(input_sharded, output_root, shuffle_buffer, cutoff=30):
"""Split by sequence identity."""
if input_sharded.get_keys() != ['ensemble']:
raise RuntimeError('Can only apply to sharded by ensemble.')
all_chain_sequences = []
logger.info('Loading chain sequences')
for _, shard in input_sharded.iter_shards():
all_chain_sequences.extend(seq.get_all_chain_sequences_df(shard))
logger.info('Splitting by cluster')
train, val, test = atom3d.splits.sequence.cluster_split(
all_chain_sequences, cutoff)
# Will just look up ensembles.
train = [x[0] for x in train]
val = [x[0] for x in val]
test = [x[0] for x in test]
keys = input_sharded.get_keys()
if keys != ['ensemble']:
raise RuntimeError('Can only apply to sharded by ensemble.')
prefix = sh.get_prefix(output_root)
num_shards = sh.get_num_shards(output_root)
train_sharded = sh.Sharded(f'{prefix:}_train@{num_shards:}', keys)
val_sharded = sh.Sharded(f'{prefix:}_val@{num_shards:}', keys)
test_sharded = sh.Sharded(f'{prefix:}_test@{num_shards:}', keys)
logger.info('Writing sets')
train_filter_fn = filters.form_filter_against_list(train, 'ensemble')
val_filter_fn = filters.form_filter_against_list(val, 'ensemble')
test_filter_fn = filters.form_filter_against_list(test, 'ensemble')
sho.filter_sharded(input_sharded, train_sharded, train_filter_fn,
shuffle_buffer)
sho.filter_sharded(input_sharded, val_sharded, val_filter_fn,
shuffle_buffer)
sho.filter_sharded(input_sharded, test_sharded, test_filter_fn,
shuffle_buffer)
# write splits to text files
np.savetxt(output_root.split('@')[0] + '_train.txt', train, fmt='%s')
np.savetxt(output_root.split('@')[0] + '_val.txt', val, fmt='%s')
np.savetxt(output_root.split('@')[0] + '_test.txt', test, fmt='%s')
def split(input_sharded, output_root, scaffold_data, shuffle_buffer):
"""Split by sequence identity."""
if input_sharded.get_keys() != ['ensemble']:
raise RuntimeError('Can only apply to sharded by ensemble.')
logger.info('Splitting by scaffold')
scaffold_list = scaffold_data['Scaffold'].tolist()
train_idx, val_idx, test_idx = splits.scaffold_split(scaffold_list)
train = scaffold_data['pdb'][train_idx].tolist()
val = scaffold_data['pdb'][val_idx].tolist()
test = scaffold_data['pdb'][test_idx].tolist()
keys = input_sharded.get_keys()
if keys != ['ensemble']:
raise RuntimeError('Can only apply to sharded by ensemble.')
prefix = sh.get_prefix(output_root)
num_shards = sh.get_num_shards(output_root)
train_sharded = sh.Sharded(f'{prefix:}_train@{num_shards:}', keys)
val_sharded = sh.Sharded(f'{prefix:}_val@{num_shards:}', keys)
test_sharded = sh.Sharded(f'{prefix:}_test@{num_shards:}', keys)
logger.info('Writing sets')
train_filter_fn = filters.form_filter_against_list(train, 'ensemble')
val_filter_fn = filters.form_filter_against_list(val, 'ensemble')
test_filter_fn = filters.form_filter_against_list(test, 'ensemble')
sho.filter_sharded(input_sharded, train_sharded, train_filter_fn,
shuffle_buffer)
sho.filter_sharded(input_sharded, val_sharded, val_filter_fn,
shuffle_buffer)
sho.filter_sharded(input_sharded, test_sharded, test_filter_fn,
shuffle_buffer)
# write splits to text files
np.savetxt(output_root.split('@')[0] + '_train.txt', train, fmt='%s')
np.savetxt(output_root.split('@')[0] + '_val.txt', val, fmt='%s')
np.savetxt(output_root.split('@')[0] + '_test.txt', test, fmt='%s')
def split_dataset(dirname_all, dirname_split, input_ds_name, input_splits,
input_csvfile, input_exclude):
# Read the sharded dataset
input_sharded = sh.Sharded.load(input_ds_name)
input_shard = input_sharded.read_shard(0)
input_label = input_sharded.read_shard(0, 'labels')
# Create output directories
if not os.path.exists(dirname_all) and dirname_all != '':
os.makedirs(dirname_all, exist_ok=True)
if not os.path.exists(dirname_split) and dirname_split != '':
os.makedirs(dirname_split, exist_ok=True)
# Correct for ensemble = None
input_shard['ensemble'] = input_shard['model']
# Save the full (corrected) dataset
sharded_all = sh.Sharded(dirname_all + '/qm9_all@1',
input_sharded.get_keys())
sharded_all._write_shard(0, input_shard)
sharded_all.add_to_shard(0, input_label, 'labels')
# Read raw and split data
label_data = pd.read_csv(input_csvfile)
indices_ex = np.loadtxt(input_exclude, dtype=int)
indices_tr = np.loadtxt(input_splits + '/indices_train.dat', dtype=int)
indices_va = np.loadtxt(input_splits + '/indices_valid.dat', dtype=int)
indices_te = np.loadtxt(input_splits + '/indices_test.dat', dtype=int)
# Create lists of molecule IDs for exclusion and splits
mol_ids = label_data['mol_id'].tolist()
mol_ids_ex = label_data.loc[indices_ex]['mol_id'].tolist()
mol_ids_te = label_data.loc[indices_te]['mol_id'].tolist()
mol_ids_va = label_data.loc[indices_va]['mol_id'].tolist()
mol_ids_tr = label_data.loc[indices_tr]['mol_id'].tolist()
# Write lists of mol_ids to files
with open(dirname_split + '/mol_ids_excluded.txt', 'w') as f:
for mol_id in mol_ids_ex:
f.write("%s\n" % mol_id)
with open(dirname_split + '/mol_ids_training.txt', 'w') as f:
for mol_id in mol_ids_tr:
f.write("%s\n" % mol_id)
with open(dirname_split + '/mol_ids_validation.txt', 'w') as f:
for mol_id in mol_ids_va:
f.write("%s\n" % mol_id)
with open(dirname_split + '/mol_ids_test.txt', 'w') as f:
for mol_id in mol_ids_te:
f.write("%s\n" % mol_id)
# Split the labels
labels_te = input_label.loc[label_data['mol_id'].isin(
mol_ids_te)].reset_index(drop=True)
labels_va = input_label.loc[label_data['mol_id'].isin(
mol_ids_va)].reset_index(drop=True)
labels_tr = input_label.loc[label_data['mol_id'].isin(
mol_ids_tr)].reset_index(drop=True)
# Filter and write out training set
filter_tr = filters.form_filter_against_list(mol_ids_tr, 'subunit')
sharded_tr = sh.Sharded(dirname_split + '/train@1', sharded_all.get_keys())
sho.filter_sharded(sharded_all, sharded_tr, filter_tr)
sharded_tr.add_to_shard(0, labels_tr, 'labels')
# Filter and write out validation set
filter_va = filters.form_filter_against_list(mol_ids_va, 'structure')
sharded_va = sh.Sharded(dirname_split + '/val@1', sharded_all.get_keys())
sho.filter_sharded(sharded_all, sharded_va, filter_va)
sharded_va.add_to_shard(0, labels_va, 'labels')
# Filter and write out test set
filter_te = filters.form_filter_against_list(mol_ids_te, 'structure')
sharded_te = sh.Sharded(dirname_split + '/test@1',
input_sharded.get_keys())
sho.filter_sharded(sharded_all, sharded_te, filter_te)
sharded_te.add_to_shard(0, labels_te, 'labels')