if not os.path.exists(tmp_feature_dir):
os.makedirs(tmp_feature_dir)
mps = []
fp_save_folder = '/raid/shenwanxiang/FP_maps'
for fp_type in fp_types:
mp = loadmap(os.path.join(fp_save_folder, '%s.mp' % fp_type))
mps.append(mp)
classification_res = []
## classification
for data in datasets:
task_name = data.task_name
task_type = data.task_type
_, induces = load_data(task_name)
smiles = data.x
Y = pd.DataFrame(data.y).fillna(MASK).values
for mp, fp_type in zip(mps, fp_types):
print(fp_type)
X2_name = "X2_%s_%s.data" % (task_name, fp_type)
X2_name = os.path.join(tmp_feature_dir, X2_name)
if not os.path.exists(X2_name):
X2 = mp.batch_transform(smiles, scale=False, n_jobs=16)
dump(X2, X2_name)
else:
X2 = load(X2_name)
def get_data(dataset):
""" Check if exists, download if not, save splits return paths to separated splits """
df, indices = load_data(dataset)
df = df.rename(columns={'smiles': 'SMILES'})
df.columns = [col.replace(' ', '_') for col in df.columns]
return df, indices