Loading deepchem/feat/material_featurizers/lcnn_featurizer.py +68 −48 Original line number Diff line number Diff line import numpy as np from deepchem.feat import MaterialStructureFeaturizer from collections import defaultdict from typing import List, Dict, Tuple, Iterable, Union, DefaultDict from typing import List, Dict, Tuple, DefaultDict, Any class LCNNFeaturizer(MaterialStructureFeaturizer): Loading Loading @@ -125,7 +125,9 @@ class LCNNFeaturizer(MaterialStructureFeaturizer): return {"X_Sites": np.array(xSites), "X_NSs": np.array(xNSs)} def input_reader(text: str, template: bool = False) -> Iterable[Union[List[str], np.ndarray, List[int], int]]: def input_reader( text: str, template: bool = False ) -> Tuple[np.ndarray, np.ndarray, List[str], List[str], np.ndarray, int, int]: """ Read Input structures in a format which can produce the coordinate dimensions and axes. If it is a primitive cell, it returns the lattice cell, coordinates, Loading @@ -141,11 +143,24 @@ def input_reader(text: str, template: bool = False) -> Iterable[Union[List[str], Returns ------- list of local_env : list of local_env class cell: np.ndarray cell basis vector coord: np.ndarray scaled coordinates st: List[str] Sitetype, 'S1' or 'A1' oss/aos: List[str] possible occupation state pbc: np.ndarray Periodic Boundary Condition(Valid only for data point) ns: int (Valid only for data point) na: int (Valid only for data point) """ s = text.rstrip('\n').split('\n') nl = 0 nl, ns, na = 0, 0, 0 # read comment if template: datum = False Loading Loading @@ -185,17 +200,17 @@ def input_reader(text: str, template: bool = False) -> Iterable[Union[List[str], nl += 1 if datum: return cell, coord, st, oss return cell, coord, st, oss, pbc, ns, na else: return cell, pbc, coord, st, ns, na, aos return cell, coord, st, aos, pbc, ns, na class _SiteEnvironment(object): def __init__(self, pos: List[np.ndarray], pos: np.ndarray, sitetypes: List[str], env2config: Union[List[int], np.ndarray], env2config: List[int], permutations: List[List[int]], cutoff: float, Grtol: float = 0.0, Loading @@ -212,14 +227,15 @@ class _SiteEnvironment(object): Parameters ---------- pos : Union[list, np.ndarray] pos : np.ndarray n x 3 list or numpy array of (non-scaled) positions. n is the number of atom. sitetypes : list sitetypes : List[str] n list of string. String must be S or A followed by a number. S indicates a spectator sites and A indicates a active sites. permutations : list env2config: List[int] permutations : List[List[int]] p x n list of list of integer. p is the permutation index and n is the number of sites. cutoff : float Loading Loading @@ -285,16 +301,17 @@ class _SiteEnvironment(object): self._nm = iso.categorical_node_match('n', '') self._em = iso.numerical_edge_match('d', 0, rtol, 0) def _construct_graph(self, pos: List[np.ndarray], sitetypes: List[str]): def _construct_graph(self, pos: np.ndarray, sitetypes: List[str]): """ Returns local environment graph using networkx and tolerance specified. Parameters ---------- pos: list pos: np.ndarray ns x 3. coordinates of positions. ns is the number of sites. sitetypes: ns. sitetype for each site sitetypes: List[str] Returns ------ Loading Loading @@ -333,9 +350,7 @@ class _SiteEnvironment(object): n += 1 return G def get_mapping(self, env: Dict[str, Union[List[int], List[str], np.ndarray]] ) -> Dict[int, int]: def get_mapping(self, env: Dict[str, Any]) -> Dict[int, int]: """ Returns mapping of sites from input to this object Loading @@ -348,15 +363,15 @@ class _SiteEnvironment(object): Parameters ---------- env : Dict[str, list] env : Dict[str, Any] dictionary that contains information of local environment of a site in datum. See _get_SiteEnvironments defintion in the class _SiteEnvironments for what this variable should be. Returns ------- dict : Union[Dict[int, int], None] Atom mapping. None if there is no mapping dict : Dict[int, int] Atom mapping from Primitive cell to data point. """ try: import networkx.algorithms.isomorphism as iso Loading Loading @@ -404,7 +419,7 @@ class _SiteEnvironment(object): s += '-Consider increasing neighbor finding tolerance' raise ValueError(s) def _kabsch(self, P: List[np.ndarray], Q: List[np.ndarray]) -> List[np.ndarray]: def _kabsch(self, P: np.ndarray, Q: np.ndarray) -> np.ndarray: """ Returns rotation matrix to align coordinates using Kabsch algorithm. Loading Loading @@ -432,7 +447,8 @@ class _SiteEnvironment(object): class _SiteEnvironments(object): def __init__(self, site_envs: List[_SiteEnvironment], ns: int, na: int, aos: List[str], eigen_tol: float, pbc: List[bool], cutoff: float): aos: List[str], eigen_tol: float, pbc: np.ndarray, cutoff: float): """ Initialize Use Load to intialize this class. Loading @@ -451,12 +467,14 @@ class _SiteEnvironments(object): eigen_tol : float tolerance for eigenanalysis of point group analysis in pymatgen. pbc : List[str] periodic boundary condition. Boolean array, periodic boundary condition. cutoff : float Cutoff radius in angstrom for pooling sites to construct local environment """ self.site_envs = site_envs self.unique_site_types: List[str] = [env.sitetypes[0] for env in self.site_envs] self.unique_site_types: List[str] = [ env.sitetypes[0] for env in self.site_envs ] self.ns = ns self.na = na self.aos = aos Loading @@ -464,8 +482,8 @@ class _SiteEnvironments(object): self.pbc = pbc self.cutoff = cutoff def read_datum(self, text: str, cutoff_factor: float = 1.1) -> Tuple[List[float], List[list]]: def read_datum(self, text: str, cutoff_factor: float = 1.1 ) -> Tuple[List[float], List[List[int]]]: """ Load structure data and return neighbor information Loading @@ -481,10 +499,10 @@ class _SiteEnvironments(object): ------ XSites : List[float] One hot encoding features of the site. XNSs : List[list] XNSs : List[List[int]] Neighbors calculated in diffrent permutations """ cell, coord, st, oss = input_reader(text) cell, coord, st, oss, _, _, _ = input_reader(text) # Construct one hot encoding XSites = np.zeros((len(oss), len(self.aos))) for i, o in enumerate(oss): Loading Loading @@ -515,7 +533,8 @@ class _SiteEnvironments(object): mapping = self.site_envs[i].get_mapping(new_env) # align input to the primitive cell (reference) aligned_idx = [ new_env['env2config'][mapping[i]] for i in range(len(new_env['env2config'])) new_env['env2config'][mapping[i]] for i in range(len(new_env['env2config'])) ] # apply permutations nni_perm = np.take(aligned_idx, self.site_envs[i].permutations) Loading @@ -528,7 +547,7 @@ class _SiteEnvironments(object): @classmethod def _truncate(cls, env_ref: _SiteEnvironment, env: Dict[str, Union[List[Union[int, float, str]], np.ndarray]]) -> Dict[str, Union[List[int], List[str], np.ndarray]]: env: Dict[str, Any]) -> Dict[str, Any]: """ When cutoff_factor is used, it will pool more site than cutoff factor specifies. This will rule out nonrelevant sites by distance. Loading @@ -536,7 +555,9 @@ class _SiteEnvironments(object): Parameters ---------- env_ref: _SiteEnvironment env: _SiteEnvironment Site information of the primitive cell env: Dict[str, Any] Site information of the data point Returns ------- Loading @@ -560,13 +581,12 @@ class _SiteEnvironments(object): for i in range(len(env['sitetypes'])) if i in siteidx ] env['env2config']: List[int] = [env['env2config'][i] for i in siteidx] env['env2config'] = [env['env2config'][i] for i in siteidx] del env['dist'] return env def load_primitive_cell(path: str, cutoff: float, def load_primitive_cell(path: str, cutoff: float, eigen_tol: float = 1e-5) -> _SiteEnvironments: """ This loads the primitive cell, along with all the permutations Loading @@ -580,7 +600,7 @@ def load_primitive_cell(path: str, cutoff : float. cutoff distance in angstrom for collecting local environment. eigen_tol : float eigen_tol : float (default) tolerance for eigenanalysis of point group analysis in pymatgen. Loading @@ -589,28 +609,28 @@ def load_primitive_cell(path: str, SiteEnvironments: _SiteEnvironments Instance of the _SiteEnvironments object """ cell, pbc, coord, st, ns, na, aos = input_reader(path, template=True) cell, coord, st, aos, pbc, ns, na = input_reader(path, template=True) site_envs = _get_SiteEnvironments( coord, cell, st, cutoff, pbc, True, eigen_tol=eigen_tol) site_envs = [ site_envs_format = [ _SiteEnvironment(e['pos'], e['sitetypes'], e['env2config'], e['permutations'], cutoff) for e in site_envs ] ust = [env.sitetypes[0] for env in site_envs] ust = [env.sitetypes[0] for env in site_envs_format] usi = np.unique(ust, return_index=True)[1] site_envs = [site_envs[i] for i in usi] return _SiteEnvironments(site_envs, ns, na, aos, eigen_tol, pbc, cutoff) site_envs_format = [site_envs_format[i] for i in usi] return _SiteEnvironments(site_envs_format, ns, na, aos, eigen_tol, pbc, cutoff) def _get_SiteEnvironments(coord: Union[List[np.ndarray], np.ndarray], cell: Union[List[np.ndarray], np.ndarray], def _get_SiteEnvironments(coord: np.ndarray, cell: np.ndarray, SiteTypes: List[str], cutoff: float, pbc: List[bool], pbc: np.ndarray, get_permutations: bool = True, eigen_tol: float = 1e-5) -> List[Dict[str, Union[List[Union[int, str, float]], np.ndarray]]]: eigen_tol: float = 1e-5) -> List[Dict[str, Any]]: """ Used to extract information about both primitve cells and data points. Extract local environments from primitive cell. Using the two diffrent types Loading @@ -618,7 +638,7 @@ def _get_SiteEnvironments(coord: Union[List[np.ndarray], np.ndarray], Parameters ---------- coord : Union[list, np.ndarray] coord : np.ndarray n x 3 list or numpy array of scaled positions. n is the number of atom. cell : np.ndarray Loading @@ -630,11 +650,11 @@ def _get_SiteEnvironments(coord: Union[List[np.ndarray], np.ndarray], cutoff : float cutoff distance in angstrom for collecting local environment. pbc : list[str] pbc : np.ndarray Periodic boundary condition get_permutations : bool (default True) Whether to find permutatated neighbor list or not. eigen_tol : float eigen_tol : float (default 1e-5) Tolerance for eigenanalysis of point group analysis in pymatgen. Loading deepchem/feat/tests/data/lcnn_test_data.pydeleted 100644 → 0 +0 −72 Original line number Diff line number Diff line primitive_cell = """#primitive strucutre 2.81852800e+00 0.00000000e+00 0.00000000e+00 T -1.40926400e+00 2.44091700e+00 0.00000000e+00 T 0.00000000e+00 0.00000000e+00 2.55082550e+01 F 1 1 1 0 2 6 0.666670000000 0.333330000000 0.090220999986 S1 0.333330000000 0.666670000000 0.180439359180 S1 0.000000000000 0.000000000000 0.270657718374 S1 0.666670000000 0.333330000000 0.360876077568 S1 0.333330000000 0.666670000000 0.451094436762 S1 0.000000000000 0.000000000000 0.496569911270 A1 """ structure = """-4.22779200e+00 -2.44091700e+00 0.00000000e+00 2.81852800e+00 -4.88183400e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 2.31755900e+01 24 0.333330000000 0.500000000000 0.099298999982 S1 0.000000000000 0.500000000000 0.198598000008 S1 0.166670000000 0.250000000000 0.297897000033 S1 0.333330000000 0.500000000000 0.397196000059 S1 0.000000000000 0.500000000000 0.496495000084 S1 0.333330000000 0.000000000000 0.099298999982 S1 0.000000000000 0.000000000000 0.198598000008 S1 0.166670000000 0.750000000000 0.297897000033 S1 0.333330000000 0.000000000000 0.397196000059 S1 0.000000000000 0.000000000000 0.496495000084 S1 0.833330000000 0.250000000000 0.099298999982 S1 0.500000000000 0.250000000000 0.198598000008 S1 0.666670000000 0.000000000000 0.297897000033 S1 0.833330000000 0.250000000000 0.397196000059 S1 0.500000000000 0.250000000000 0.496495000084 S1 0.833330000000 0.750000000000 0.099298999982 S1 0.500000000000 0.750000000000 0.198598000008 S1 0.666670000000 0.500000000000 0.297897000033 S1 0.833330000000 0.750000000000 0.397196000059 S1 0.500000000000 0.750000000000 0.496495000084 S1 0.666670000000 0.500000000000 0.546547663253 A1 0 0.666670000000 0.000000000000 0.546547663253 A1 0 0.166670000000 0.750000000000 0.546547663253 A1 2 0.166670000000 0.250000000000 0.546547663253 A1 1 """ check_feature = [[0., 1., 0.], [0., 1., 0.], [0., 0., 1.], [1., 0., 0.]] check_edges = [ [[[0, 2, 3, 1, 1, 2, 3, 1, 0, 2, 3, 1, 0, 1, 2, 0, 0, 3, 1], [0, 3, 2, 1, 1, 3, 2, 1, 0, 3, 2, 1, 0, 1, 3, 0, 0, 2, 1], [0, 1, 2, 0, 1, 2, 0, 3, 1, 0, 3, 1, 2, 0, 3, 1, 3, 1, 2], [0, 1, 3, 0, 1, 3, 0, 2, 1, 0, 2, 1, 3, 0, 2, 1, 2, 1, 3], [0, 3, 0, 1, 0, 1, 3, 2, 1, 2, 1, 0, 2, 1, 0, 1, 3, 2, 3], [0, 2, 0, 1, 0, 1, 2, 3, 1, 3, 1, 0, 3, 1, 0, 1, 2, 3, 2]], [[1, 3, 2, 0, 0, 3, 2, 0, 1, 3, 2, 0, 1, 0, 3, 1, 1, 2, 0], [1, 2, 3, 0, 0, 2, 3, 0, 1, 2, 3, 0, 1, 0, 2, 1, 1, 3, 0], [1, 0, 3, 1, 0, 3, 1, 2, 0, 1, 2, 0, 3, 1, 2, 0, 2, 0, 3], [1, 0, 2, 1, 0, 2, 1, 3, 0, 1, 3, 0, 2, 1, 3, 0, 3, 0, 2], [1, 2, 1, 0, 1, 0, 2, 3, 0, 3, 0, 1, 3, 0, 1, 0, 2, 3, 2], [1, 3, 1, 0, 1, 0, 3, 2, 0, 2, 0, 1, 2, 0, 1, 0, 3, 2, 3]], [[2, 1, 0, 3, 3, 1, 0, 3, 2, 1, 0, 3, 2, 3, 1, 2, 2, 0, 3], [2, 0, 1, 3, 3, 0, 1, 3, 2, 0, 1, 3, 2, 3, 0, 2, 2, 1, 3], [2, 3, 1, 2, 3, 1, 2, 0, 3, 2, 0, 3, 1, 2, 0, 3, 0, 3, 1], [2, 3, 0, 2, 3, 0, 2, 1, 3, 2, 1, 3, 0, 2, 1, 3, 1, 3, 0], [2, 0, 2, 3, 2, 3, 0, 1, 3, 1, 3, 2, 1, 3, 2, 3, 0, 1, 0], [2, 1, 2, 3, 2, 3, 1, 0, 3, 0, 3, 2, 0, 3, 2, 3, 1, 0, 1]], [[3, 0, 1, 2, 2, 0, 1, 2, 3, 0, 1, 2, 3, 2, 0, 3, 3, 1, 2], [ 3, 1, 0, 2, 2, 1, 0, 2, 3, 1, 0, 2, 3, 2, 1, 3, 3, 0, 2 ], [3, 2, 0, 3, 2, 0, 3, 1, 2, 3, 1, 2, 0, 3, 1, 2, 1, 2, 0], [3, 2, 1, 3, 2, 1, 3, 0, 2, 3, 0, 2, 1, 3, 0, 2, 0, 2, 1], [3, 1, 3, 2, 3, 2, 1, 0, 2, 0, 2, 3, 0, 2, 3, 2, 1, 0, 1], [3, 0, 3, 2, 3, 2, 0, 1, 2, 1, 2, 3, 1, 2, 3, 2, 0, 1, 0]]] ] deepchem/feat/tests/data/platinum_absorption_strucutre.json 0 → 100644 +46 −0 Original line number Diff line number Diff line { "primitive_cell": "#primitive strucutre\n2.81852800e+00 0.00000000e+00 0.00000000e+00 T\n-1.40926400e+00 2.44091700e+00 0.00000000e+00 T\n0.00000000e+00 0.00000000e+00 2.55082550e+01 F\n1 1\n1 0 2\n6\n0.666670000000 0.333330000000 0.090220999986 S1\n0.333330000000 0.666670000000 0.180439359180 S1\n0.000000000000 0.000000000000 0.270657718374 S1\n0.666670000000 0.333330000000 0.360876077568 S1\n0.333330000000 0.666670000000 0.451094436762 S1\n0.000000000000 0.000000000000 0.496569911270 A1\n", "structure": "-4.22779200e+00 -2.44091700e+00 0.00000000e+00\n 2.81852800e+00 -4.88183400e+00 0.00000000e+00\n 0.00000000e+00 0.00000000e+00 2.31755900e+01\n24\n 0.333330000000 0.500000000000 0.099298999982 S1\n 0.000000000000 0.500000000000 0.198598000008 S1\n 0.166670000000 0.250000000000 0.297897000033 S1\n 0.333330000000 0.500000000000 0.397196000059 S1\n 0.000000000000 0.500000000000 0.496495000084 S1\n 0.333330000000 0.000000000000 0.099298999982 S1\n 0.000000000000 0.000000000000 0.198598000008 S1\n 0.166670000000 0.750000000000 0.297897000033 S1\n 0.333330000000 0.000000000000 0.397196000059 S1\n 0.000000000000 0.000000000000 0.496495000084 S1\n 0.833330000000 0.250000000000 0.099298999982 S1\n 0.500000000000 0.250000000000 0.198598000008 S1\n 0.666670000000 0.000000000000 0.297897000033 S1\n 0.833330000000 0.250000000000 0.397196000059 S1\n 0.500000000000 0.250000000000 0.496495000084 S1\n 0.833330000000 0.750000000000 0.099298999982 S1\n 0.500000000000 0.750000000000 0.198598000008 S1\n 0.666670000000 0.500000000000 0.297897000033 S1\n 0.833330000000 0.750000000000 0.397196000059 S1\n 0.500000000000 0.750000000000 0.496495000084 S1\n 0.666670000000 0.500000000000 0.546547663253 A1 0\n 0.666670000000 0.000000000000 0.546547663253 A1 0\n 0.166670000000 0.750000000000 0.546547663253 A1 2\n 0.166670000000 0.250000000000 0.546547663253 A1 1\n", "node_feature": [ [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0] ], "edges": [ [ [ [0, 2, 3, 1, 1, 2, 3, 1, 0, 2, 3, 1, 0, 1, 2, 0, 0, 3, 1], [0, 3, 2, 1, 1, 3, 2, 1, 0, 3, 2, 1, 0, 1, 3, 0, 0, 2, 1], [0, 1, 2, 0, 1, 2, 0, 3, 1, 0, 3, 1, 2, 0, 3, 1, 3, 1, 2], [0, 1, 3, 0, 1, 3, 0, 2, 1, 0, 2, 1, 3, 0, 2, 1, 2, 1, 3], [0, 3, 0, 1, 0, 1, 3, 2, 1, 2, 1, 0, 2, 1, 0, 1, 3, 2, 3], [0, 2, 0, 1, 0, 1, 2, 3, 1, 3, 1, 0, 3, 1, 0, 1, 2, 3, 2] ], [ [1, 3, 2, 0, 0, 3, 2, 0, 1, 3, 2, 0, 1, 0, 3, 1, 1, 2, 0], [1, 2, 3, 0, 0, 2, 3, 0, 1, 2, 3, 0, 1, 0, 2, 1, 1, 3, 0], [1, 0, 3, 1, 0, 3, 1, 2, 0, 1, 2, 0, 3, 1, 2, 0, 2, 0, 3], [1, 0, 2, 1, 0, 2, 1, 3, 0, 1, 3, 0, 2, 1, 3, 0, 3, 0, 2], [1, 2, 1, 0, 1, 0, 2, 3, 0, 3, 0, 1, 3, 0, 1, 0, 2, 3, 2], [1, 3, 1, 0, 1, 0, 3, 2, 0, 2, 0, 1, 2, 0, 1, 0, 3, 2, 3] ], [ [2, 1, 0, 3, 3, 1, 0, 3, 2, 1, 0, 3, 2, 3, 1, 2, 2, 0, 3], [2, 0, 1, 3, 3, 0, 1, 3, 2, 0, 1, 3, 2, 3, 0, 2, 2, 1, 3], [2, 3, 1, 2, 3, 1, 2, 0, 3, 2, 0, 3, 1, 2, 0, 3, 0, 3, 1], [2, 3, 0, 2, 3, 0, 2, 1, 3, 2, 1, 3, 0, 2, 1, 3, 1, 3, 0], [2, 0, 2, 3, 2, 3, 0, 1, 3, 1, 3, 2, 1, 3, 2, 3, 0, 1, 0], [2, 1, 2, 3, 2, 3, 1, 0, 3, 0, 3, 2, 0, 3, 2, 3, 1, 0, 1] ], [ [3, 0, 1, 2, 2, 0, 1, 2, 3, 0, 1, 2, 3, 2, 0, 3, 3, 1, 2], [3, 1, 0, 2, 2, 1, 0, 2, 3, 1, 0, 2, 3, 2, 1, 3, 3, 0, 2], [3, 2, 0, 3, 2, 0, 3, 1, 2, 3, 1, 2, 0, 3, 1, 2, 1, 2, 0], [3, 2, 1, 3, 2, 1, 3, 0, 2, 3, 0, 2, 1, 3, 0, 2, 0, 2, 1], [3, 1, 3, 2, 3, 2, 1, 0, 2, 0, 2, 3, 0, 2, 3, 2, 1, 0, 1], [3, 0, 3, 2, 3, 2, 0, 1, 2, 1, 2, 3, 1, 2, 3, 2, 0, 1, 0] ] ] ] } deepchem/feat/tests/test_lcnn_featurizer.py +13 −7 Original line number Diff line number Diff line import os import json import numpy as np from deepchem.feat.material_featurizers.lcnn_featurizer import LCNNFeaturizer from data.lcnn_test_data import primitive_cell, structure, check_edges, check_feature def test_LCNNFeaturizer(): featuriser = LCNNFeaturizer(np.around(6.00), primitive_cell) data = featuriser._featurize(structure) assert np.all(data['X_Sites'] == np.array(check_feature)) assert np.all(data['X_NSs'] == np.array(check_edges)) current_dir = os.path.dirname(os.path.realpath(__file__)) strucutre_file = os.path.join(current_dir, 'platinum_absorption_strucutre.json') with open(strucutre_file, 'r') as f: test_data = json.load(f) featuriser = LCNNFeaturizer(np.around(6.00), test_data["primitive_cell"]) data = featuriser._featurize(test_data["structure"]) assert np.all(data['X_Sites'] == np.array(test_data["node_feature"])) assert np.all(data['X_NSs'] == test_data["edges"]) assert data['X_Sites'].shape == (4, 3) assert data['X_NSs'].shape == (1, 4, 6, 19) Loading
deepchem/feat/material_featurizers/lcnn_featurizer.py +68 −48 Original line number Diff line number Diff line import numpy as np from deepchem.feat import MaterialStructureFeaturizer from collections import defaultdict from typing import List, Dict, Tuple, Iterable, Union, DefaultDict from typing import List, Dict, Tuple, DefaultDict, Any class LCNNFeaturizer(MaterialStructureFeaturizer): Loading Loading @@ -125,7 +125,9 @@ class LCNNFeaturizer(MaterialStructureFeaturizer): return {"X_Sites": np.array(xSites), "X_NSs": np.array(xNSs)} def input_reader(text: str, template: bool = False) -> Iterable[Union[List[str], np.ndarray, List[int], int]]: def input_reader( text: str, template: bool = False ) -> Tuple[np.ndarray, np.ndarray, List[str], List[str], np.ndarray, int, int]: """ Read Input structures in a format which can produce the coordinate dimensions and axes. If it is a primitive cell, it returns the lattice cell, coordinates, Loading @@ -141,11 +143,24 @@ def input_reader(text: str, template: bool = False) -> Iterable[Union[List[str], Returns ------- list of local_env : list of local_env class cell: np.ndarray cell basis vector coord: np.ndarray scaled coordinates st: List[str] Sitetype, 'S1' or 'A1' oss/aos: List[str] possible occupation state pbc: np.ndarray Periodic Boundary Condition(Valid only for data point) ns: int (Valid only for data point) na: int (Valid only for data point) """ s = text.rstrip('\n').split('\n') nl = 0 nl, ns, na = 0, 0, 0 # read comment if template: datum = False Loading Loading @@ -185,17 +200,17 @@ def input_reader(text: str, template: bool = False) -> Iterable[Union[List[str], nl += 1 if datum: return cell, coord, st, oss return cell, coord, st, oss, pbc, ns, na else: return cell, pbc, coord, st, ns, na, aos return cell, coord, st, aos, pbc, ns, na class _SiteEnvironment(object): def __init__(self, pos: List[np.ndarray], pos: np.ndarray, sitetypes: List[str], env2config: Union[List[int], np.ndarray], env2config: List[int], permutations: List[List[int]], cutoff: float, Grtol: float = 0.0, Loading @@ -212,14 +227,15 @@ class _SiteEnvironment(object): Parameters ---------- pos : Union[list, np.ndarray] pos : np.ndarray n x 3 list or numpy array of (non-scaled) positions. n is the number of atom. sitetypes : list sitetypes : List[str] n list of string. String must be S or A followed by a number. S indicates a spectator sites and A indicates a active sites. permutations : list env2config: List[int] permutations : List[List[int]] p x n list of list of integer. p is the permutation index and n is the number of sites. cutoff : float Loading Loading @@ -285,16 +301,17 @@ class _SiteEnvironment(object): self._nm = iso.categorical_node_match('n', '') self._em = iso.numerical_edge_match('d', 0, rtol, 0) def _construct_graph(self, pos: List[np.ndarray], sitetypes: List[str]): def _construct_graph(self, pos: np.ndarray, sitetypes: List[str]): """ Returns local environment graph using networkx and tolerance specified. Parameters ---------- pos: list pos: np.ndarray ns x 3. coordinates of positions. ns is the number of sites. sitetypes: ns. sitetype for each site sitetypes: List[str] Returns ------ Loading Loading @@ -333,9 +350,7 @@ class _SiteEnvironment(object): n += 1 return G def get_mapping(self, env: Dict[str, Union[List[int], List[str], np.ndarray]] ) -> Dict[int, int]: def get_mapping(self, env: Dict[str, Any]) -> Dict[int, int]: """ Returns mapping of sites from input to this object Loading @@ -348,15 +363,15 @@ class _SiteEnvironment(object): Parameters ---------- env : Dict[str, list] env : Dict[str, Any] dictionary that contains information of local environment of a site in datum. See _get_SiteEnvironments defintion in the class _SiteEnvironments for what this variable should be. Returns ------- dict : Union[Dict[int, int], None] Atom mapping. None if there is no mapping dict : Dict[int, int] Atom mapping from Primitive cell to data point. """ try: import networkx.algorithms.isomorphism as iso Loading Loading @@ -404,7 +419,7 @@ class _SiteEnvironment(object): s += '-Consider increasing neighbor finding tolerance' raise ValueError(s) def _kabsch(self, P: List[np.ndarray], Q: List[np.ndarray]) -> List[np.ndarray]: def _kabsch(self, P: np.ndarray, Q: np.ndarray) -> np.ndarray: """ Returns rotation matrix to align coordinates using Kabsch algorithm. Loading Loading @@ -432,7 +447,8 @@ class _SiteEnvironment(object): class _SiteEnvironments(object): def __init__(self, site_envs: List[_SiteEnvironment], ns: int, na: int, aos: List[str], eigen_tol: float, pbc: List[bool], cutoff: float): aos: List[str], eigen_tol: float, pbc: np.ndarray, cutoff: float): """ Initialize Use Load to intialize this class. Loading @@ -451,12 +467,14 @@ class _SiteEnvironments(object): eigen_tol : float tolerance for eigenanalysis of point group analysis in pymatgen. pbc : List[str] periodic boundary condition. Boolean array, periodic boundary condition. cutoff : float Cutoff radius in angstrom for pooling sites to construct local environment """ self.site_envs = site_envs self.unique_site_types: List[str] = [env.sitetypes[0] for env in self.site_envs] self.unique_site_types: List[str] = [ env.sitetypes[0] for env in self.site_envs ] self.ns = ns self.na = na self.aos = aos Loading @@ -464,8 +482,8 @@ class _SiteEnvironments(object): self.pbc = pbc self.cutoff = cutoff def read_datum(self, text: str, cutoff_factor: float = 1.1) -> Tuple[List[float], List[list]]: def read_datum(self, text: str, cutoff_factor: float = 1.1 ) -> Tuple[List[float], List[List[int]]]: """ Load structure data and return neighbor information Loading @@ -481,10 +499,10 @@ class _SiteEnvironments(object): ------ XSites : List[float] One hot encoding features of the site. XNSs : List[list] XNSs : List[List[int]] Neighbors calculated in diffrent permutations """ cell, coord, st, oss = input_reader(text) cell, coord, st, oss, _, _, _ = input_reader(text) # Construct one hot encoding XSites = np.zeros((len(oss), len(self.aos))) for i, o in enumerate(oss): Loading Loading @@ -515,7 +533,8 @@ class _SiteEnvironments(object): mapping = self.site_envs[i].get_mapping(new_env) # align input to the primitive cell (reference) aligned_idx = [ new_env['env2config'][mapping[i]] for i in range(len(new_env['env2config'])) new_env['env2config'][mapping[i]] for i in range(len(new_env['env2config'])) ] # apply permutations nni_perm = np.take(aligned_idx, self.site_envs[i].permutations) Loading @@ -528,7 +547,7 @@ class _SiteEnvironments(object): @classmethod def _truncate(cls, env_ref: _SiteEnvironment, env: Dict[str, Union[List[Union[int, float, str]], np.ndarray]]) -> Dict[str, Union[List[int], List[str], np.ndarray]]: env: Dict[str, Any]) -> Dict[str, Any]: """ When cutoff_factor is used, it will pool more site than cutoff factor specifies. This will rule out nonrelevant sites by distance. Loading @@ -536,7 +555,9 @@ class _SiteEnvironments(object): Parameters ---------- env_ref: _SiteEnvironment env: _SiteEnvironment Site information of the primitive cell env: Dict[str, Any] Site information of the data point Returns ------- Loading @@ -560,13 +581,12 @@ class _SiteEnvironments(object): for i in range(len(env['sitetypes'])) if i in siteidx ] env['env2config']: List[int] = [env['env2config'][i] for i in siteidx] env['env2config'] = [env['env2config'][i] for i in siteidx] del env['dist'] return env def load_primitive_cell(path: str, cutoff: float, def load_primitive_cell(path: str, cutoff: float, eigen_tol: float = 1e-5) -> _SiteEnvironments: """ This loads the primitive cell, along with all the permutations Loading @@ -580,7 +600,7 @@ def load_primitive_cell(path: str, cutoff : float. cutoff distance in angstrom for collecting local environment. eigen_tol : float eigen_tol : float (default) tolerance for eigenanalysis of point group analysis in pymatgen. Loading @@ -589,28 +609,28 @@ def load_primitive_cell(path: str, SiteEnvironments: _SiteEnvironments Instance of the _SiteEnvironments object """ cell, pbc, coord, st, ns, na, aos = input_reader(path, template=True) cell, coord, st, aos, pbc, ns, na = input_reader(path, template=True) site_envs = _get_SiteEnvironments( coord, cell, st, cutoff, pbc, True, eigen_tol=eigen_tol) site_envs = [ site_envs_format = [ _SiteEnvironment(e['pos'], e['sitetypes'], e['env2config'], e['permutations'], cutoff) for e in site_envs ] ust = [env.sitetypes[0] for env in site_envs] ust = [env.sitetypes[0] for env in site_envs_format] usi = np.unique(ust, return_index=True)[1] site_envs = [site_envs[i] for i in usi] return _SiteEnvironments(site_envs, ns, na, aos, eigen_tol, pbc, cutoff) site_envs_format = [site_envs_format[i] for i in usi] return _SiteEnvironments(site_envs_format, ns, na, aos, eigen_tol, pbc, cutoff) def _get_SiteEnvironments(coord: Union[List[np.ndarray], np.ndarray], cell: Union[List[np.ndarray], np.ndarray], def _get_SiteEnvironments(coord: np.ndarray, cell: np.ndarray, SiteTypes: List[str], cutoff: float, pbc: List[bool], pbc: np.ndarray, get_permutations: bool = True, eigen_tol: float = 1e-5) -> List[Dict[str, Union[List[Union[int, str, float]], np.ndarray]]]: eigen_tol: float = 1e-5) -> List[Dict[str, Any]]: """ Used to extract information about both primitve cells and data points. Extract local environments from primitive cell. Using the two diffrent types Loading @@ -618,7 +638,7 @@ def _get_SiteEnvironments(coord: Union[List[np.ndarray], np.ndarray], Parameters ---------- coord : Union[list, np.ndarray] coord : np.ndarray n x 3 list or numpy array of scaled positions. n is the number of atom. cell : np.ndarray Loading @@ -630,11 +650,11 @@ def _get_SiteEnvironments(coord: Union[List[np.ndarray], np.ndarray], cutoff : float cutoff distance in angstrom for collecting local environment. pbc : list[str] pbc : np.ndarray Periodic boundary condition get_permutations : bool (default True) Whether to find permutatated neighbor list or not. eigen_tol : float eigen_tol : float (default 1e-5) Tolerance for eigenanalysis of point group analysis in pymatgen. Loading
deepchem/feat/tests/data/lcnn_test_data.pydeleted 100644 → 0 +0 −72 Original line number Diff line number Diff line primitive_cell = """#primitive strucutre 2.81852800e+00 0.00000000e+00 0.00000000e+00 T -1.40926400e+00 2.44091700e+00 0.00000000e+00 T 0.00000000e+00 0.00000000e+00 2.55082550e+01 F 1 1 1 0 2 6 0.666670000000 0.333330000000 0.090220999986 S1 0.333330000000 0.666670000000 0.180439359180 S1 0.000000000000 0.000000000000 0.270657718374 S1 0.666670000000 0.333330000000 0.360876077568 S1 0.333330000000 0.666670000000 0.451094436762 S1 0.000000000000 0.000000000000 0.496569911270 A1 """ structure = """-4.22779200e+00 -2.44091700e+00 0.00000000e+00 2.81852800e+00 -4.88183400e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 2.31755900e+01 24 0.333330000000 0.500000000000 0.099298999982 S1 0.000000000000 0.500000000000 0.198598000008 S1 0.166670000000 0.250000000000 0.297897000033 S1 0.333330000000 0.500000000000 0.397196000059 S1 0.000000000000 0.500000000000 0.496495000084 S1 0.333330000000 0.000000000000 0.099298999982 S1 0.000000000000 0.000000000000 0.198598000008 S1 0.166670000000 0.750000000000 0.297897000033 S1 0.333330000000 0.000000000000 0.397196000059 S1 0.000000000000 0.000000000000 0.496495000084 S1 0.833330000000 0.250000000000 0.099298999982 S1 0.500000000000 0.250000000000 0.198598000008 S1 0.666670000000 0.000000000000 0.297897000033 S1 0.833330000000 0.250000000000 0.397196000059 S1 0.500000000000 0.250000000000 0.496495000084 S1 0.833330000000 0.750000000000 0.099298999982 S1 0.500000000000 0.750000000000 0.198598000008 S1 0.666670000000 0.500000000000 0.297897000033 S1 0.833330000000 0.750000000000 0.397196000059 S1 0.500000000000 0.750000000000 0.496495000084 S1 0.666670000000 0.500000000000 0.546547663253 A1 0 0.666670000000 0.000000000000 0.546547663253 A1 0 0.166670000000 0.750000000000 0.546547663253 A1 2 0.166670000000 0.250000000000 0.546547663253 A1 1 """ check_feature = [[0., 1., 0.], [0., 1., 0.], [0., 0., 1.], [1., 0., 0.]] check_edges = [ [[[0, 2, 3, 1, 1, 2, 3, 1, 0, 2, 3, 1, 0, 1, 2, 0, 0, 3, 1], [0, 3, 2, 1, 1, 3, 2, 1, 0, 3, 2, 1, 0, 1, 3, 0, 0, 2, 1], [0, 1, 2, 0, 1, 2, 0, 3, 1, 0, 3, 1, 2, 0, 3, 1, 3, 1, 2], [0, 1, 3, 0, 1, 3, 0, 2, 1, 0, 2, 1, 3, 0, 2, 1, 2, 1, 3], [0, 3, 0, 1, 0, 1, 3, 2, 1, 2, 1, 0, 2, 1, 0, 1, 3, 2, 3], [0, 2, 0, 1, 0, 1, 2, 3, 1, 3, 1, 0, 3, 1, 0, 1, 2, 3, 2]], [[1, 3, 2, 0, 0, 3, 2, 0, 1, 3, 2, 0, 1, 0, 3, 1, 1, 2, 0], [1, 2, 3, 0, 0, 2, 3, 0, 1, 2, 3, 0, 1, 0, 2, 1, 1, 3, 0], [1, 0, 3, 1, 0, 3, 1, 2, 0, 1, 2, 0, 3, 1, 2, 0, 2, 0, 3], [1, 0, 2, 1, 0, 2, 1, 3, 0, 1, 3, 0, 2, 1, 3, 0, 3, 0, 2], [1, 2, 1, 0, 1, 0, 2, 3, 0, 3, 0, 1, 3, 0, 1, 0, 2, 3, 2], [1, 3, 1, 0, 1, 0, 3, 2, 0, 2, 0, 1, 2, 0, 1, 0, 3, 2, 3]], [[2, 1, 0, 3, 3, 1, 0, 3, 2, 1, 0, 3, 2, 3, 1, 2, 2, 0, 3], [2, 0, 1, 3, 3, 0, 1, 3, 2, 0, 1, 3, 2, 3, 0, 2, 2, 1, 3], [2, 3, 1, 2, 3, 1, 2, 0, 3, 2, 0, 3, 1, 2, 0, 3, 0, 3, 1], [2, 3, 0, 2, 3, 0, 2, 1, 3, 2, 1, 3, 0, 2, 1, 3, 1, 3, 0], [2, 0, 2, 3, 2, 3, 0, 1, 3, 1, 3, 2, 1, 3, 2, 3, 0, 1, 0], [2, 1, 2, 3, 2, 3, 1, 0, 3, 0, 3, 2, 0, 3, 2, 3, 1, 0, 1]], [[3, 0, 1, 2, 2, 0, 1, 2, 3, 0, 1, 2, 3, 2, 0, 3, 3, 1, 2], [ 3, 1, 0, 2, 2, 1, 0, 2, 3, 1, 0, 2, 3, 2, 1, 3, 3, 0, 2 ], [3, 2, 0, 3, 2, 0, 3, 1, 2, 3, 1, 2, 0, 3, 1, 2, 1, 2, 0], [3, 2, 1, 3, 2, 1, 3, 0, 2, 3, 0, 2, 1, 3, 0, 2, 0, 2, 1], [3, 1, 3, 2, 3, 2, 1, 0, 2, 0, 2, 3, 0, 2, 3, 2, 1, 0, 1], [3, 0, 3, 2, 3, 2, 0, 1, 2, 1, 2, 3, 1, 2, 3, 2, 0, 1, 0]]] ]
deepchem/feat/tests/data/platinum_absorption_strucutre.json 0 → 100644 +46 −0 Original line number Diff line number Diff line { "primitive_cell": "#primitive strucutre\n2.81852800e+00 0.00000000e+00 0.00000000e+00 T\n-1.40926400e+00 2.44091700e+00 0.00000000e+00 T\n0.00000000e+00 0.00000000e+00 2.55082550e+01 F\n1 1\n1 0 2\n6\n0.666670000000 0.333330000000 0.090220999986 S1\n0.333330000000 0.666670000000 0.180439359180 S1\n0.000000000000 0.000000000000 0.270657718374 S1\n0.666670000000 0.333330000000 0.360876077568 S1\n0.333330000000 0.666670000000 0.451094436762 S1\n0.000000000000 0.000000000000 0.496569911270 A1\n", "structure": "-4.22779200e+00 -2.44091700e+00 0.00000000e+00\n 2.81852800e+00 -4.88183400e+00 0.00000000e+00\n 0.00000000e+00 0.00000000e+00 2.31755900e+01\n24\n 0.333330000000 0.500000000000 0.099298999982 S1\n 0.000000000000 0.500000000000 0.198598000008 S1\n 0.166670000000 0.250000000000 0.297897000033 S1\n 0.333330000000 0.500000000000 0.397196000059 S1\n 0.000000000000 0.500000000000 0.496495000084 S1\n 0.333330000000 0.000000000000 0.099298999982 S1\n 0.000000000000 0.000000000000 0.198598000008 S1\n 0.166670000000 0.750000000000 0.297897000033 S1\n 0.333330000000 0.000000000000 0.397196000059 S1\n 0.000000000000 0.000000000000 0.496495000084 S1\n 0.833330000000 0.250000000000 0.099298999982 S1\n 0.500000000000 0.250000000000 0.198598000008 S1\n 0.666670000000 0.000000000000 0.297897000033 S1\n 0.833330000000 0.250000000000 0.397196000059 S1\n 0.500000000000 0.250000000000 0.496495000084 S1\n 0.833330000000 0.750000000000 0.099298999982 S1\n 0.500000000000 0.750000000000 0.198598000008 S1\n 0.666670000000 0.500000000000 0.297897000033 S1\n 0.833330000000 0.750000000000 0.397196000059 S1\n 0.500000000000 0.750000000000 0.496495000084 S1\n 0.666670000000 0.500000000000 0.546547663253 A1 0\n 0.666670000000 0.000000000000 0.546547663253 A1 0\n 0.166670000000 0.750000000000 0.546547663253 A1 2\n 0.166670000000 0.250000000000 0.546547663253 A1 1\n", "node_feature": [ [0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [1.0, 0.0, 0.0] ], "edges": [ [ [ [0, 2, 3, 1, 1, 2, 3, 1, 0, 2, 3, 1, 0, 1, 2, 0, 0, 3, 1], [0, 3, 2, 1, 1, 3, 2, 1, 0, 3, 2, 1, 0, 1, 3, 0, 0, 2, 1], [0, 1, 2, 0, 1, 2, 0, 3, 1, 0, 3, 1, 2, 0, 3, 1, 3, 1, 2], [0, 1, 3, 0, 1, 3, 0, 2, 1, 0, 2, 1, 3, 0, 2, 1, 2, 1, 3], [0, 3, 0, 1, 0, 1, 3, 2, 1, 2, 1, 0, 2, 1, 0, 1, 3, 2, 3], [0, 2, 0, 1, 0, 1, 2, 3, 1, 3, 1, 0, 3, 1, 0, 1, 2, 3, 2] ], [ [1, 3, 2, 0, 0, 3, 2, 0, 1, 3, 2, 0, 1, 0, 3, 1, 1, 2, 0], [1, 2, 3, 0, 0, 2, 3, 0, 1, 2, 3, 0, 1, 0, 2, 1, 1, 3, 0], [1, 0, 3, 1, 0, 3, 1, 2, 0, 1, 2, 0, 3, 1, 2, 0, 2, 0, 3], [1, 0, 2, 1, 0, 2, 1, 3, 0, 1, 3, 0, 2, 1, 3, 0, 3, 0, 2], [1, 2, 1, 0, 1, 0, 2, 3, 0, 3, 0, 1, 3, 0, 1, 0, 2, 3, 2], [1, 3, 1, 0, 1, 0, 3, 2, 0, 2, 0, 1, 2, 0, 1, 0, 3, 2, 3] ], [ [2, 1, 0, 3, 3, 1, 0, 3, 2, 1, 0, 3, 2, 3, 1, 2, 2, 0, 3], [2, 0, 1, 3, 3, 0, 1, 3, 2, 0, 1, 3, 2, 3, 0, 2, 2, 1, 3], [2, 3, 1, 2, 3, 1, 2, 0, 3, 2, 0, 3, 1, 2, 0, 3, 0, 3, 1], [2, 3, 0, 2, 3, 0, 2, 1, 3, 2, 1, 3, 0, 2, 1, 3, 1, 3, 0], [2, 0, 2, 3, 2, 3, 0, 1, 3, 1, 3, 2, 1, 3, 2, 3, 0, 1, 0], [2, 1, 2, 3, 2, 3, 1, 0, 3, 0, 3, 2, 0, 3, 2, 3, 1, 0, 1] ], [ [3, 0, 1, 2, 2, 0, 1, 2, 3, 0, 1, 2, 3, 2, 0, 3, 3, 1, 2], [3, 1, 0, 2, 2, 1, 0, 2, 3, 1, 0, 2, 3, 2, 1, 3, 3, 0, 2], [3, 2, 0, 3, 2, 0, 3, 1, 2, 3, 1, 2, 0, 3, 1, 2, 1, 2, 0], [3, 2, 1, 3, 2, 1, 3, 0, 2, 3, 0, 2, 1, 3, 0, 2, 0, 2, 1], [3, 1, 3, 2, 3, 2, 1, 0, 2, 0, 2, 3, 0, 2, 3, 2, 1, 0, 1], [3, 0, 3, 2, 3, 2, 0, 1, 2, 1, 2, 3, 1, 2, 3, 2, 0, 1, 0] ] ] ] }
deepchem/feat/tests/test_lcnn_featurizer.py +13 −7 Original line number Diff line number Diff line import os import json import numpy as np from deepchem.feat.material_featurizers.lcnn_featurizer import LCNNFeaturizer from data.lcnn_test_data import primitive_cell, structure, check_edges, check_feature def test_LCNNFeaturizer(): featuriser = LCNNFeaturizer(np.around(6.00), primitive_cell) data = featuriser._featurize(structure) assert np.all(data['X_Sites'] == np.array(check_feature)) assert np.all(data['X_NSs'] == np.array(check_edges)) current_dir = os.path.dirname(os.path.realpath(__file__)) strucutre_file = os.path.join(current_dir, 'platinum_absorption_strucutre.json') with open(strucutre_file, 'r') as f: test_data = json.load(f) featuriser = LCNNFeaturizer(np.around(6.00), test_data["primitive_cell"]) data = featuriser._featurize(test_data["structure"]) assert np.all(data['X_Sites'] == np.array(test_data["node_feature"])) assert np.all(data['X_NSs'] == test_data["edges"]) assert data['X_Sites'].shape == (4, 3) assert data['X_NSs'].shape == (1, 4, 6, 19)
