How to use the atoml.preprocess.clean_data.clean_infinite function in atoml
To help you get started, we’ve selected a few atoml examples, based on popular ways it is used in public projects.
Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
SUNCAT-Center / CatKit / docs / tutorials / 9_adsorbate_fingerprints.py View on Github 
# This list is passed on to the following setup functions,
# along with a list of atoms.
# Get and print the names of features.
features_labels = fingerprinter.return_names(functions)
for l in range(len(features_labels)):
print(l, features_labels[l])
# Get a matrix containing the fingerprints.
unlabeled_data_matrix = fingerprinter.return_vec(AtoML_atoms, functions)
print(np.shape(unlabeled_data_matrix), 'data matrix created.')
# Cleanup in case some of the functions are returning NaNs or Infs
print("Cleaning data.")
clean_data_matrix = clean_infinite(unlabeled_data_matrix)['train']
# Ready for Machine learning.
print(np.shape(clean_data_matrix), 'data matrix returned.')
