nice package
Subpackages
Submodules
nice.clebsch_gordan module
- class nice.clebsch_gordan.ClebschGordan(l_max)
Bases:
object
- nice.clebsch_gordan.check_clebsch_gordan(clebsch_gordan, required_l_max)
- nice.clebsch_gordan.get_single(l1, l2, l, m1, m2)
nice.contracted_pca module
- nice.contracted_pca.do_pca_step(features, n_components, normalize=True, epsilon=1e-08)
- nice.contracted_pca.do_sign_covariant_pca(X, n_components)
nice.nice_utilities module
- class nice.nice_utilities.Data(covariants, actual_sizes, importances=None)
Bases:
object- static get_amplitude(values, lambd)
- get_amplitudes()
- get_invariants()
- nice.nice_utilities.do_partial_expansion()
- nice.nice_utilities.do_partial_expansion_covariants()
- nice.nice_utilities.do_partial_expansion_invariants()
- nice.nice_utilities.get_placing()
- nice.nice_utilities.get_size_invariants()
- nice.nice_utilities.get_sizes()
- nice.nice_utilities.get_sizes_covariants()
- nice.nice_utilities.process_contiguousness()
nice.packing module
- nice.packing.copy_parallel()
- nice.packing.pack_dense()
- nice.packing.subtract_parallel()
- nice.packing.unite_parallel()
- nice.packing.unpack_dense()
nice.rascal_coefficients module
- nice.rascal_coefficients.convert_rascal_coefficients()
- nice.rascal_coefficients.copy_coefs()
- nice.rascal_coefficients.get_rascal_coefficients()
- nice.rascal_coefficients.get_rascal_coefficients_stared()
- nice.rascal_coefficients.process_structures()
Satisfying librascal desire of having all atoms inside the cell even if structure is not periodic. (changes only non periodic structures)
- nice.rascal_coefficients.split_by_central_specie()
nice.thresholding module
- nice.thresholding.get_thresholded_tasks()
nice.unrolling_individual_pca module
- class nice.unrolling_individual_pca.UnrollingIndividualPCA(*args, normalize_importances=True, **kwargs)
Bases:
sklearn.decomposition._truncated_svd.TruncatedSVD- fit(*args)
Fit model on training data X.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – Training data.
y (Ignored) –
- Returns
self – Returns the transformer object.
- Return type
object
- fit_transform(*args)
Fit model to X and perform dimensionality reduction on X.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – Training data.
y (Ignored) –
- Returns
X_new – Reduced version of X. This will always be a dense array.
- Return type
ndarray of shape (n_samples, n_components)
- transform(*args)
Perform dimensionality reduction on X.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – New data.
- Returns
X_new – Reduced version of X. This will always be a dense array.
- Return type
ndarray of shape (n_samples, n_components)
nice.unrolling_pca module
- class nice.unrolling_pca.UnrollingPCA(*args, **kwargs)
Bases:
sklearn.decomposition._truncated_svd.TruncatedSVD- fit(coefficients)
Fit model on training data X.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – Training data.
y (Ignored) –
- Returns
self – Returns the transformer object.
- Return type
object
- fit_transform(coefficients)
Fit model to X and perform dimensionality reduction on X.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – Training data.
y (Ignored) –
- Returns
X_new – Reduced version of X. This will always be a dense array.
- Return type
ndarray of shape (n_samples, n_components)
- transform(coefficients)
Perform dimensionality reduction on X.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – New data.
- Returns
X_new – Reduced version of X. This will always be a dense array.
- Return type
ndarray of shape (n_samples, n_components)
- nice.unrolling_pca.get_signs()
- nice.unrolling_pca.pack_dense()
- nice.unrolling_pca.unpack_dense()
nice.utilities module
- nice.utilities.get_all_species(structures)
getting all unique atomic species among the structures
- Parameters
structures – list of ase atoms objects
- Returns
sorted numpy array with ints with all unique species in the format where 1 states for H, 2 for He and so on. (inherits from ase function atoms_object.get_atomic_numbers())
- nice.utilities.get_compositional_features(structures, all_species)
getting compositional features suitable for linear regression which contains information about the number of atoms with particular species in the structure
- Parameters
structures – list of Ase atoms objects
all_species – numpy array with ints of all unique species in the dataset. If all species argument is the same for several calls of this function, resulting blocks of compositional features are guaranteed to be consisted with each other
- Returns
numpy array with shape [len(structures), len(all_species)] with compositional features
- nice.utilities.get_spherical_expansion(structures, rascal_hypers, all_species, task_size=100, num_threads=None, split_by_central_specie=True, show_progress=True)
getting spherical expansion coefficients
- Parameters
structures – list of Ase atoms objects
rascal_hypers – dictionary with parameters for librascal controlling spherical expansion
all_species – numpy array with ints of all unique species in the dataset. If all species argument is the same for several calls of this function, resulting blocks of spherical expansion coefficients are guaranteed to be consisted with each other
task_size – number of structures in chunk for multiprocessing
num_threads – number of threads in multiprocessing. If None than all available (len(os.sched_getaffinity(0))) threads are used
split_by_central_specie – whether group or not spherical expansion coefficients by central specie
show_progress – whether or not show progress via tqdm
- Returns
dictionary in which keys are elements of all_speceis and entries are numpy arrays with indexing [environmental index, radial basis/neighbor specie index, lambda, m] with spherical expansion coefficients for environments around atoms with specie indicated in key. Coefficients are stored from the beginning, i. e. [:, : lambda, :(2 * lambda + 1)] elements are valid
- nice.utilities.make_structural_features(features, structures, all_species, show_progress=True)
getting structural features suitable for linear regression which consist of sums over atomic features
- Parameters
features – nested dictionary with atomic features. First level keys are central species, second level keys are body orders. Entries are 2-dimensional numpy arrays.
structures – list of Ase atoms objects
all_species – numpy array with ints of all unique species in the dataset. If all species argument is the same for several calls of this function, resulting blocks of structural features are guaranteed to be consistent with each other. If for given block of structures there are no atoms of some particular specie, features dictionary still have to contain key with this specie. It should contain numpy arrays with shapes [0, number of features]. This is need to get proper placing of features to fulfill consistency.
show_progress – whether or not show progress via tqdm
- Returns
numpy array with shape [len(structures), number of structural features] with structural features
- nice.utilities.transform_sequentially(nice, structures, rascal_hypers, all_species, block_size=500, show_progress=True)
transforming structures into structural features by chunks in order to use less amount of RAM
- Parameters
nice – dictionary where keys are species and entries are nice transformers. If you want to use single nice transformer to all environments regardless of central specie just pass {key : nice_single for specie in all_species}
structures – list of Ase atoms objects
rascal_hypers – dictionary with parameters for librascal controlling spherical expansion. Should be the same as used for fitting nice transformers
all_species – numpy array with ints of all unique species in the dataset.
block_size – size of chunks measured in number of environments
show_progress – whether or not show progress via tqdm
- Returns
numpy array with shape [len(structures), number of structural features] with structural features