Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from bayesian-optimization) (1.19.5)
Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from bayesian-optimization) (1.4.1)
Requirement already satisfied: scikit-learn>=0.18.0 in /usr/local/lib/python3.7/dist-packages (from bayesian-optimization) (0.22.2.post1)
Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18.0->bayesian-optimization) (1.0.1)
Building wheels for collected packages: bayesian-optimization
Building wheel for bayesian-optimization (setup.py) ... [?25l[?25hdone
Created wheel for bayesian-optimization: filename=bayesian_optimization-1.2.0-py3-none-any.whl size=11685 sha256=fe62c2a660ed5d9d514b5ea22d24477c1375e2a76b765fe2428891c61cb98514
Stored in directory: /root/.cache/pip/wheels/fd/9b/71/f127d694e02eb40bcf18c7ae9613b88a6be4470f57a8528c5b
x = pd.concat([composition,pd.DataFrame(normalized_atomic_properties)],axis=1)
x=x.iloc[:697]
y = df_all[['TEC']][:697]
# bins = [18,35,48,109,202,234,525,687,695]
bins = [18,35,48,109,202,234,525,687]
y_binned = np.digitize(y.index, bins, right=True) #stratified 7-fold: each folder contains a specific type of alloys (7 types in total, each takes 85% and 15% as training and testing)
x = torch.FloatTensor(x.values) #numpy to tensor
y = torch.FloatTensor(y.values) #numpy to tensor
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
train_features, test_features, train_labels, test_labels = train_test_split(x, y, test_size=0.15, random_state=seed, stratify=y_binned)