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389 | class Featurizer:
def __init__(
self,
featurizer_class=None, # black
design_space_structures: List[Atoms] = None,
species_list: List[str] = None,
max_size: int = None,
preset: str = None,
kwargs: Dict = None,
):
self._featurizer_class = SineMatrix
self.featurizer_class = featurizer_class
self._preset = None
self.preset = preset
self._kwargs = None
self.kwargs = kwargs
self._max_size = 100
self.max_size = max_size
self._species_list = ["Fe", "Ni", "Pt", "Pd", "Cu", "C", "N", "O", "H"]
self.species_list = species_list
# overrides max_size and species_list if given
self._design_space_structures = None
self.design_space_structures = design_space_structures
def __eq__(self, other: object) -> bool:
if isinstance(other, Featurizer):
for attr in [
"featurizer_class",
"species_list",
"max_size",
"preset",
"kwargs",
]:
if getattr(self, attr) != getattr(other, attr):
return False
return True
return False
def __repr__(self) -> str:
pt = PrettyTable()
pt.field_names = ["", "Featurizer"]
class_name = (
self.featurizer_class.__module__ + "." + self.featurizer_class.__name__
)
pt.add_row(["class", class_name])
pt.add_row(["kwargs", self.kwargs])
pt.add_row(["species list", self.species_list])
pt.add_row(["maximum structure size", self.max_size])
pt.add_row(["preset", self.preset])
pt.add_row(
[
"design space structures provided?",
self.design_space_structures is not None,
]
)
pt.max_width = 70
return str(pt)
def copy(self):
"""
Returns a copy of the featurizer
"""
ds_structs_copy = (
[struct.copy() for struct in self.design_space_structures]
if self.design_space_structures
else None
)
feat = self.__class__(
featurizer_class=self.featurizer_class,
design_space_structures=ds_structs_copy,
species_list=self.species_list.copy(),
max_size=self.max_size,
preset=self.preset,
kwargs=copy.deepcopy(self.kwargs) if self.kwargs else None,
)
return feat
def to_jsonified_dict(self) -> Dict:
# collect design space structures
if self.design_space_structures is not None:
collected_structs = []
for struct in self.design_space_structures:
collected_structs.append(atoms_encoder(struct))
else:
collected_structs = None
mod_string = self.featurizer_class.__module__
class_string = self.featurizer_class.__name__
return {
"design_space_structures": collected_structs,
"species_list": self.species_list if self.species_list else None,
"preset": self.preset if self.preset else None,
"kwargs": self.kwargs if self.kwargs else None,
"max_size": self.max_size if self.max_size else None,
"featurizer_class": {
"module_string": mod_string,
"class_string": class_string,
},
}
def write_json_to_disk(self, write_location: str = ".", json_name: str = None):
"""
Writes `Featurizer` to disk as a json
"""
jsonified_list = self.to_jsonified_dict()
if json_name is None:
json_name = "featurizer.json"
json_path = os.path.join(write_location, json_name)
with open(json_path, "w") as f:
json.dump(jsonified_list, f)
@staticmethod
def from_jsonified_dict(all_data: Dict):
if all_data.get("design_space_structures") is not None:
# ensure structures are properly encoded using `ase.io.jsonio.encode`
try:
structures = []
for encoded_atoms in all_data.get("design_space_structures"):
structures.append(atoms_decoder(encoded_atoms))
except (json.JSONDecodeError, TypeError):
msg = "Please ensure design space structures encoded using `ase.io.jsonio.encode`"
raise FeaturizerError(msg)
else:
structures = None
if all_data.get("featurizer_class") is None:
# allow not providing featurizer class (will use default)
featurizer_class = None
elif not (
isinstance(all_data.get("featurizer_class"), dict)
and len(all_data.get("featurizer_class")) == 2
and all_data["featurizer_class"].get("module_string") is not None
and all_data["featurizer_class"].get("class_string") is not None
):
# check featurizer class, if provided, is done so in correct format
msg = f"featurizer_class must be provided\
in the form {{'module_string': module name, 'class_string': class name}},\
got {all_data.get('featurizer_class')}"
raise FeaturizerError(msg)
else:
mod = importlib.import_module(
all_data["featurizer_class"].get("module_string")
)
featurizer_class = getattr(
mod, all_data["featurizer_class"].get("class_string")
)
return Featurizer(
featurizer_class=featurizer_class,
design_space_structures=structures,
species_list=all_data.get("species_list"),
preset=all_data.get("preset"),
max_size=all_data.get("max_size"),
kwargs=all_data.get("kwargs"),
)
@staticmethod
def from_json(json_name: str):
with open(json_name, "r") as f:
all_data = json.load(f)
return Featurizer.from_jsonified_dict(all_data=all_data)
@property
def featurizer_class(self):
return self._featurizer_class
@featurizer_class.setter
def featurizer_class(self, featurizer_class):
if (
featurizer_class in SUPPORTED_MATMINER_CLASSES
or featurizer_class in SUPPORTED_DSCRIBE_CLASSES
):
self._featurizer_class = featurizer_class
self._preset = None
self._kwargs = None
elif featurizer_class is None:
pass
else:
msg = f"Featurization class {featurizer_class} is not currently supported."
raise FeaturizerError(msg)
@property
def preset(self):
return self._preset
@preset.setter
def preset(self, preset):
if self.featurizer_class in [CrystalNNFingerprint, ElementProperty]:
self._preset = preset
elif preset is None:
self._preset = preset
else:
msg = f"Presets are not supported for {self.featurizer_class.__module__}"
raise FeaturizerError(msg)
@property
def kwargs(self):
return self._kwargs
@kwargs.setter
def kwargs(self, kwargs):
if kwargs is not None:
self._kwargs = kwargs.copy()
@property
def design_space_structures(self):
return self._design_space_structures
@design_space_structures.setter
def design_space_structures(self, design_space_structures: List[Atoms]):
if design_space_structures is not None:
self._design_space_structures = [
struct.copy() for struct in design_space_structures
]
# analyze new design space
ds_structs = design_space_structures
_species_list = []
for s in ds_structs:
# get all unique species
found_species = np.unique(s.get_chemical_symbols()).tolist()
new_species = [
spec for spec in found_species if spec not in _species_list
]
_species_list.extend(new_species)
# sort species list
sorted_species_list = sorted(
_species_list, key=lambda el: Element(el).mendeleev_no
)
self._max_size = max([len(s) for s in ds_structs])
self._species_list = sorted_species_list
@property
def max_size(self):
return self._max_size
@max_size.setter
def max_size(self, max_size):
if max_size is not None:
self._max_size = max_size
@property
def species_list(self):
return self._species_list
@species_list.setter
def species_list(self, species_list: List[str]):
if species_list is not None:
_species_list = species_list.copy()
# sort species list by mendeleev number
sorted_species_list = sorted(
_species_list, key=lambda el: Element(el).mendeleev_no
)
self._species_list = sorted_species_list
# TODO: "get_featurization_object" -> "get_featurizer"
@property
def featurization_object(self):
return self._get_featurization_object()
def _get_featurization_object(self):
# instantiate featurizer object
if hasattr(self.featurizer_class, "from_preset") and self.preset is not None:
return self.featurizer_class.from_preset(self.preset)
if self.featurizer_class in [SineMatrix, CoulombMatrix]:
return self.featurizer_class(
n_atoms_max=self.max_size, permutation="none", **self.kwargs or {},
)
if self.featurizer_class in [SOAP, ACSF]:
return self.featurizer_class(species=self.species_list, **self.kwargs or {})
return self.featurizer_class(**self.kwargs or {})
def featurize_single(self, structure: Atoms):
"""
Featurize a single structure. Returns a single vector
Parameters
----------
structure:
ase.Atoms object of structure to be featurized
Returns
-------
representation:
Numpy array of feature vector (not flattened)
"""
feat_class = self.featurizer_class
featurization_object = self.featurization_object
# dscribe classes
if feat_class in [SOAP, ACSF]:
adsorbate_indices = np.where(structure.get_tags() <= 0)[0].tolist()
return featurization_object.create(structure, positions=adsorbate_indices,)
if feat_class in [SineMatrix, CoulombMatrix]:
return featurization_object.create(structure).reshape(-1,)
# matminer classes
pym_struct = AseAtomsAdaptor().get_structure(structure)
if feat_class == ElementProperty:
return np.array(featurization_object.featurize(pym_struct.composition))
representation = np.array([])
if feat_class in [CrystalNNFingerprint, OPSiteFingerprint]:
adsorbate_indices = np.where(structure.get_tags() <= 0)[0].tolist()
for idx in adsorbate_indices:
feat = featurization_object.featurize(pym_struct, idx)
representation = np.concatenate((representation, feat))
return representation
if feat_class == ChemicalSRO:
adsorbate_indices = np.where(structure.get_tags() <= 0)[0].tolist()
formatted_list = [[pym_struct, idx] for idx in adsorbate_indices]
featurization_object.fit(formatted_list)
for idx in adsorbate_indices:
feat = featurization_object.featurize(pym_struct, idx)
representation = np.concatenate((representation, feat))
return representation
return None
def featurize_multiple(self, structures: List[Atoms]):
"""
Featurize multiple structures. Returns a matrix where each
row is the flattened feature vector of each system
Parameters
----------
structures:
List of ase.Atoms structures to be featurized
Returns
-------
X:
Numpy array of shape (number of structures, number of features)
"""
first_vec = self.featurize_single(structures[0]).flatten()
num_features = len(first_vec)
# if adsorbate featurization, assumes only 1 adsorbate in design space
# (otherwise would require padding)
X = np.zeros((len(structures), num_features))
X[0, :] = first_vec.copy()
for i in range(1, len(structures)):
X[i, :] = self.featurize_single(structures[i]).flatten()
return X
|