gt4sd.properties.crystals.core module¶
Summary¶
Classes:
Metal/non-metal classifier class. |
|
Reference¶
- class S3ParametersCrystals(**data)[source]¶
Bases:
S3Parameters- domain: DomainSubmodule¶
- __dict__¶
- __pydantic_fields_set__: set[str]¶
The names of fields explicitly set during instantiation.
- __pydantic_extra__: dict[str, Any] | None¶
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_private__: dict[str, Any] | None¶
Values of private attributes set on the model instance.
- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': 'str', 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'domain': <enum 'DomainSubmodule'>, 'model_config': 'ClassVar[ConfigDict]'}¶
- __class_vars__: ClassVar[set[str]] = {}¶
The names of the class variables defined on the model.
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True¶
Whether model building is completed, or if there are still undefined fields.
- __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶
A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.S3ParametersCrystals'>, 'config': {'title': 'S3ParametersCrystals'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.S3ParametersCrystals'>>]}, 'ref': 'gt4sd.properties.crystals.core.S3ParametersCrystals:93913111858384', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_updates': {'examples': ['Tox21']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_updates': {'description': 'Name of the algorithm', 'examples': ['MCA']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:93913111829088', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'S3ParametersCrystals', 'type': 'model-fields'}, 'type': 'model'}¶
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False¶
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=True, examples=['Tox21']), 'algorithm_name': FieldInfo(annotation=str, required=True, description='Name of the algorithm', examples=['MCA']), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>)}¶
A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶
The name of the post-init method for the model, if defined.
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005569d9ac0cd0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_application": SerField { key_py: Py( 0x00007f85657124c0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, }, "domain": SerField { key_py: Py( 0x00007f863fdd1130, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b01150, ), ), serializer: Enum( EnumSerializer { class: Py( 0x00005569d9ab9a60, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_type": SerField { key_py: Py( 0x00007f863c298b70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f858b9db230, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_name": SerField { key_py: Py( 0x00007f863c299a70, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, }, "algorithm_version": SerField { key_py: Py( 0x00007f863c2ac030, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "S3ParametersCrystals", }, ), definitions=[])¶
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶
__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="S3ParametersCrystals", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_type", py_key: Py( 0x00007f8564ebceb0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c298b70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f858b9db230, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "domain", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "domain", py_key: Py( 0x00007f8564b28eb0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863fdd1130, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b01150, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x00005569d9ab9a60, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "properties": 1, "molecules": 0, "crystals": 2, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f8564b28080, ), ), values: [ Py( 0x00007f8564b01070, ), Py( 0x00007f8564b010e0, ), Py( 0x00007f8564b01150, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_name", py_key: Py( 0x00007f8564b28ef0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299a70, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_version", py_key: Py( 0x00007f8564b0f6e0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c2ac030, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_application", py_key: Py( 0x00007f8564b0f780, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f85657124c0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "S3ParametersCrystals", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x00005569d9ac0cd0, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f863e1e3a60, ), name: "S3ParametersCrystals", }, ), definitions=[], cache_strings=True)¶
The pydantic-core SchemaValidator used to validate instances of the model.
- __signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str, algorithm_version: str, algorithm_application: str) -> None>¶
The synthesized __init__ [Signature][inspect.Signature] of the model.
- _abc_impl = <_abc._abc_data object>¶
- model_config: ClassVar[ConfigDict] = {}¶
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class CGCNNParameters(**data)[source]¶
Bases:
S3ParametersCrystals- algorithm_name: str¶
- batch_size: int¶
- workers: int¶
- __dict__¶
- __pydantic_fields_set__: set[str]¶
The names of fields explicitly set during instantiation.
- __pydantic_extra__: dict[str, Any] | None¶
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_private__: dict[str, Any] | None¶
Values of private attributes set on the model instance.
- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': 'str', 'algorithm_name': <class 'str'>, 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': <class 'int'>, 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': <class 'int'>}¶
- __class_vars__: ClassVar[set[str]] = {}¶
The names of the class variables defined on the model.
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True¶
Whether model building is completed, or if there are still undefined fields.
- __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶
A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.CGCNNParameters'>, 'config': {'title': 'CGCNNParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.CGCNNParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.CGCNNParameters:93913111868000', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_updates': {'examples': ['Tox21']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:93913111829088', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'CGCNNParameters', 'type': 'model-fields'}, 'type': 'model'}¶
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False¶
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=True, examples=['Tox21']), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶
A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶
The name of the post-init method for the model, if defined.
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005569d9ac3260, ), serializer: Fields( GeneralFieldsSerializer { fields: { "batch_size": SerField { key_py: Py( 0x00007f863c299270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a20d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_version": SerField { key_py: Py( 0x00007f863c2ac030, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, }, "workers": SerField { key_py: Py( 0x00007f863dad90b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a00d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "domain": SerField { key_py: Py( 0x00007f863fdd1130, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b01150, ), ), serializer: Enum( EnumSerializer { class: Py( 0x00005569d9ab9a60, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_name": SerField { key_py: Py( 0x00007f863c299a70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f861e5ca570, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_type": SerField { key_py: Py( 0x00007f863c298b70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f858b9db230, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_application": SerField { key_py: Py( 0x00007f85657124c0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "CGCNNParameters", }, ), definitions=[])¶
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶
__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="CGCNNParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_type", py_key: Py( 0x00007f8565c54c70, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c298b70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f858b9db230, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "domain", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "domain", py_key: Py( 0x00007f8564b2a270, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863fdd1130, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b01150, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x00005569d9ab9a60, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "properties": 1, "crystals": 2, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f8564b29580, ), ), values: [ Py( 0x00007f8564b01070, ), Py( 0x00007f8564b010e0, ), Py( 0x00007f8564b01150, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_name", py_key: Py( 0x00007f8564b2a2b0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299a70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f861e5ca570, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_version", py_key: Py( 0x00007f8564b0f8c0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c2ac030, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_application", py_key: Py( 0x00007f8564b0f960, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f85657124c0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "batch_size", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "batch_size", py_key: Py( 0x00007f8564b2a2f0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a20d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "workers", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "workers", py_key: Py( 0x00007f8564b296f0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863dad90b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a00d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, ], model_name: "CGCNNParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x00005569d9ac3260, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f863e1e3a60, ), name: "CGCNNParameters", }, ), definitions=[], cache_strings=True)¶
The pydantic-core SchemaValidator used to validate instances of the model.
- __signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str, batch_size: int = 256, workers: int = 0) -> None>¶
The synthesized __init__ [Signature][inspect.Signature] of the model.
- _abc_impl = <_abc._abc_data object>¶
- model_config: ClassVar[ConfigDict] = {}¶
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class MetalNonMetalClassifierParameters(**data)[source]¶
Bases:
S3ParametersCrystals- algorithm_name: str¶
- algorithm_application: str¶
- __dict__¶
- __pydantic_fields_set__: set[str]¶
The names of fields explicitly set during instantiation.
- __pydantic_extra__: dict[str, Any] | None¶
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_private__: dict[str, Any] | None¶
Values of private attributes set on the model instance.
- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': <class 'str'>, 'algorithm_type': 'str', 'algorithm_version': 'str', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]'}¶
- __class_vars__: ClassVar[set[str]] = {}¶
The names of the class variables defined on the model.
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True¶
Whether model building is completed, or if there are still undefined fields.
- __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶
A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.MetalNonMetalClassifierParameters'>, 'config': {'title': 'MetalNonMetalClassifierParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.MetalNonMetalClassifierParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.MetalNonMetalClassifierParameters:93913111879920', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'MetalNonMetalClassifier', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'RFC', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:93913111829088', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'MetalNonMetalClassifierParameters', 'type': 'model-fields'}, 'type': 'model'}¶
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False¶
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='MetalNonMetalClassifier'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='RFC'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>)}¶
A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶
The name of the post-init method for the model, if defined.
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005569d9ac60f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_application": SerField { key_py: Py( 0x00007f85657124c0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b0e420, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_version": SerField { key_py: Py( 0x00007f863c2ac030, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, }, "domain": SerField { key_py: Py( 0x00007f863fdd1130, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b01150, ), ), serializer: Enum( EnumSerializer { class: Py( 0x00005569d9ab9a60, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_type": SerField { key_py: Py( 0x00007f863c298b70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f858b9db230, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_name": SerField { key_py: Py( 0x00007f863c299a70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f863f6bf5b0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "MetalNonMetalClassifierParameters", }, ), definitions=[])¶
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶
__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="MetalNonMetalClassifierParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_type", py_key: Py( 0x00007f8564b2a9f0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c298b70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f858b9db230, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "domain", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "domain", py_key: Py( 0x00007f8564b2b270, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863fdd1130, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b01150, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x00005569d9ab9a60, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "molecules": 0, "crystals": 2, "properties": 1, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f856b373640, ), ), values: [ Py( 0x00007f8564b01070, ), Py( 0x00007f8564b010e0, ), Py( 0x00007f8564b01150, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_name", py_key: Py( 0x00007f8564b2b2b0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299a70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f863f6bf5b0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_version", py_key: Py( 0x00007f8564b0faa0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c2ac030, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_application", py_key: Py( 0x00007f8564b0fb40, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f85657124c0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b0e420, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, ], model_name: "MetalNonMetalClassifierParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x00005569d9ac60f0, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f863e1e3a60, ), name: "MetalNonMetalClassifierParameters", }, ), definitions=[], cache_strings=True)¶
The pydantic-core SchemaValidator used to validate instances of the model.
- __signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'RFC', algorithm_version: str, algorithm_application: str = 'MetalNonMetalClassifier') -> None>¶
The synthesized __init__ [Signature][inspect.Signature] of the model.
- _abc_impl = <_abc._abc_data object>¶
- model_config: ClassVar[ConfigDict] = {}¶
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class FormationEnergyParameters(**data)[source]¶
Bases:
CGCNNParameters- algorithm_application: str¶
- __dict__¶
- __pydantic_fields_set__: set[str]¶
The names of fields explicitly set during instantiation.
- __pydantic_extra__: dict[str, Any] | None¶
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_private__: dict[str, Any] | None¶
Values of private attributes set on the model instance.
- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶
- __class_vars__: ClassVar[set[str]] = {}¶
The names of the class variables defined on the model.
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True¶
Whether model building is completed, or if there are still undefined fields.
- __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶
A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.FormationEnergyParameters'>, 'config': {'title': 'FormationEnergyParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.FormationEnergyParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.FormationEnergyParameters:93913111890096', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'FormationEnergy', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:93913111829088', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'FormationEnergyParameters', 'type': 'model-fields'}, 'type': 'model'}¶
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False¶
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='FormationEnergy'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶
A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶
The name of the post-init method for the model, if defined.
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005569d9ac88b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_type": SerField { key_py: Py( 0x00007f863c298b70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f858b9db230, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_name": SerField { key_py: Py( 0x00007f863c299a70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f861e5ca570, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_application": SerField { key_py: Py( 0x00007f85657124c0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b07630, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "batch_size": SerField { key_py: Py( 0x00007f863c299270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a20d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "workers": SerField { key_py: Py( 0x00007f863dad90b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a00d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "domain": SerField { key_py: Py( 0x00007f863fdd1130, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b01150, ), ), serializer: Enum( EnumSerializer { class: Py( 0x00005569d9ab9a60, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_version": SerField { key_py: Py( 0x00007f863c2ac030, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "FormationEnergyParameters", }, ), definitions=[])¶
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶
__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="FormationEnergyParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_type", py_key: Py( 0x00007f8564b2b970, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c298b70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f858b9db230, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "domain", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "domain", py_key: Py( 0x00007f8564b38530, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863fdd1130, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b01150, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x00005569d9ab9a60, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "crystals": 2, "properties": 1, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f8564b2b800, ), ), values: [ Py( 0x00007f8564b01070, ), Py( 0x00007f8564b010e0, ), Py( 0x00007f8564b01150, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_name", py_key: Py( 0x00007f8564b38570, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299a70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f861e5ca570, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_version", py_key: Py( 0x00007f8564b0fc80, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c2ac030, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_application", py_key: Py( 0x00007f8564b0fd20, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f85657124c0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b07630, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "batch_size", py_key: Py( 0x00007f8564b385b0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a20d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "workers", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "workers", py_key: Py( 0x00007f8564b385f0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863dad90b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a00d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, ], model_name: "FormationEnergyParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x00005569d9ac88b0, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f863e1e3a60, ), name: "FormationEnergyParameters", }, ), definitions=[], cache_strings=True)¶
The pydantic-core SchemaValidator used to validate instances of the model.
- __signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'FormationEnergy', batch_size: int = 256, workers: int = 0) -> None>¶
The synthesized __init__ [Signature][inspect.Signature] of the model.
- _abc_impl = <_abc._abc_data object>¶
- model_config: ClassVar[ConfigDict] = {}¶
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class AbsoluteEnergyParameters(**data)[source]¶
Bases:
CGCNNParameters- algorithm_application: str¶
- __dict__¶
- __pydantic_fields_set__: set[str]¶
The names of fields explicitly set during instantiation.
- __pydantic_extra__: dict[str, Any] | None¶
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_private__: dict[str, Any] | None¶
Values of private attributes set on the model instance.
- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶
- __class_vars__: ClassVar[set[str]] = {}¶
The names of the class variables defined on the model.
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True¶
Whether model building is completed, or if there are still undefined fields.
- __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶
A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.AbsoluteEnergyParameters'>, 'config': {'title': 'AbsoluteEnergyParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.AbsoluteEnergyParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.AbsoluteEnergyParameters:93913111905600', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'AbsoluteEnergy', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:93913111829088', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'AbsoluteEnergyParameters', 'type': 'model-fields'}, 'type': 'model'}¶
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False¶
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='AbsoluteEnergy'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶
A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶
The name of the post-init method for the model, if defined.
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005569d9acc540, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_name": SerField { key_py: Py( 0x00007f863c299a70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f861e5ca570, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_version": SerField { key_py: Py( 0x00007f863c2ac030, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, }, "algorithm_application": SerField { key_py: Py( 0x00007f85657124c0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b06db0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "batch_size": SerField { key_py: Py( 0x00007f863c299270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a20d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "workers": SerField { key_py: Py( 0x00007f863dad90b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a00d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_type": SerField { key_py: Py( 0x00007f863c298b70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f858b9db230, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "domain": SerField { key_py: Py( 0x00007f863fdd1130, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b01150, ), ), serializer: Enum( EnumSerializer { class: Py( 0x00005569d9ab9a60, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "AbsoluteEnergyParameters", }, ), definitions=[])¶
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶
__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="AbsoluteEnergyParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_type", py_key: Py( 0x00007f8564b38bf0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c298b70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f858b9db230, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "domain", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "domain", py_key: Py( 0x00007f8564b38c70, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863fdd1130, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b01150, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x00005569d9ab9a60, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "crystals": 2, "molecules": 0, "properties": 1, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f8564b38380, ), ), values: [ Py( 0x00007f8564b01070, ), Py( 0x00007f8564b010e0, ), Py( 0x00007f8564b01150, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_name", py_key: Py( 0x00007f8564b397f0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299a70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f861e5ca570, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_version", py_key: Py( 0x00007f8564b0fe60, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c2ac030, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_application", py_key: Py( 0x00007f8564b0ff00, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f85657124c0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b06db0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "batch_size", py_key: Py( 0x00007f8564b39830, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a20d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "workers", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "workers", py_key: Py( 0x00007f8564b39870, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863dad90b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a00d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, ], model_name: "AbsoluteEnergyParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x00005569d9acc540, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f863e1e3a60, ), name: "AbsoluteEnergyParameters", }, ), definitions=[], cache_strings=True)¶
The pydantic-core SchemaValidator used to validate instances of the model.
- __signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'AbsoluteEnergy', batch_size: int = 256, workers: int = 0) -> None>¶
The synthesized __init__ [Signature][inspect.Signature] of the model.
- _abc_impl = <_abc._abc_data object>¶
- model_config: ClassVar[ConfigDict] = {}¶
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class BandGapParameters(**data)[source]¶
Bases:
CGCNNParameters- algorithm_application: str¶
- __dict__¶
- __pydantic_fields_set__: set[str]¶
The names of fields explicitly set during instantiation.
- __pydantic_extra__: dict[str, Any] | None¶
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_private__: dict[str, Any] | None¶
Values of private attributes set on the model instance.
- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶
- __class_vars__: ClassVar[set[str]] = {}¶
The names of the class variables defined on the model.
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True¶
Whether model building is completed, or if there are still undefined fields.
- __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶
A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.BandGapParameters'>, 'config': {'title': 'BandGapParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.BandGapParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.BandGapParameters:93913111921104', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'BandGap', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:93913111829088', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'BandGapParameters', 'type': 'model-fields'}, 'type': 'model'}¶
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False¶
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='BandGap'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶
A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶
The name of the post-init method for the model, if defined.
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005569d9ad01d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "domain": SerField { key_py: Py( 0x00007f863fdd1130, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b01150, ), ), serializer: Enum( EnumSerializer { class: Py( 0x00005569d9ab9a60, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_type": SerField { key_py: Py( 0x00007f863c298b70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f858b9db230, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_name": SerField { key_py: Py( 0x00007f863c299a70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f861e5ca570, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_version": SerField { key_py: Py( 0x00007f863c2ac030, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, }, "workers": SerField { key_py: Py( 0x00007f863dad90b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a00d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_application": SerField { key_py: Py( 0x00007f85657124c0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b06bf0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "batch_size": SerField { key_py: Py( 0x00007f863c299270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a20d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "BandGapParameters", }, ), definitions=[])¶
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶
__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="BandGapParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_type", py_key: Py( 0x00007f8564b39ef0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c298b70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f858b9db230, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "domain", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "domain", py_key: Py( 0x00007f8564b3aa70, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863fdd1130, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b01150, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x00005569d9ab9a60, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "crystals": 2, "molecules": 0, "properties": 1, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f8564b39640, ), ), values: [ Py( 0x00007f8564b01070, ), Py( 0x00007f8564b010e0, ), Py( 0x00007f8564b01150, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_name", py_key: Py( 0x00007f8564b3aab0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299a70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f861e5ca570, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_version", py_key: Py( 0x00007f8564b40030, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c2ac030, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_application", py_key: Py( 0x00007f8564b400d0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f85657124c0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b06bf0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "batch_size", py_key: Py( 0x00007f8564b3aaf0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a20d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "workers", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "workers", py_key: Py( 0x00007f8564b3ab30, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863dad90b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a00d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, ], model_name: "BandGapParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x00005569d9ad01d0, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f863e1e3a60, ), name: "BandGapParameters", }, ), definitions=[], cache_strings=True)¶
The pydantic-core SchemaValidator used to validate instances of the model.
- __signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'BandGap', batch_size: int = 256, workers: int = 0) -> None>¶
The synthesized __init__ [Signature][inspect.Signature] of the model.
- _abc_impl = <_abc._abc_data object>¶
- model_config: ClassVar[ConfigDict] = {}¶
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class FermiEnergyParameters(**data)[source]¶
Bases:
CGCNNParameters- algorithm_application: str¶
- __dict__¶
- __pydantic_fields_set__: set[str]¶
The names of fields explicitly set during instantiation.
- __pydantic_extra__: dict[str, Any] | None¶
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_private__: dict[str, Any] | None¶
Values of private attributes set on the model instance.
- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶
- __class_vars__: ClassVar[set[str]] = {}¶
The names of the class variables defined on the model.
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True¶
Whether model building is completed, or if there are still undefined fields.
- __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶
A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.FermiEnergyParameters'>, 'config': {'title': 'FermiEnergyParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.FermiEnergyParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.FermiEnergyParameters:93913111936608', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'FermiEnergy', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:93913111829088', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'FermiEnergyParameters', 'type': 'model-fields'}, 'type': 'model'}¶
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False¶
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='FermiEnergy'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶
A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶
The name of the post-init method for the model, if defined.
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005569d9ad3e60, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_version": SerField { key_py: Py( 0x00007f863c2ac030, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, }, "workers": SerField { key_py: Py( 0x00007f863dad90b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a00d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_name": SerField { key_py: Py( 0x00007f863c299a70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f861e5ca570, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "batch_size": SerField { key_py: Py( 0x00007f863c299270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a20d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_type": SerField { key_py: Py( 0x00007f863c298b70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f858b9db230, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "domain": SerField { key_py: Py( 0x00007f863fdd1130, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b01150, ), ), serializer: Enum( EnumSerializer { class: Py( 0x00005569d9ab9a60, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_application": SerField { key_py: Py( 0x00007f85657124c0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b074f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "FermiEnergyParameters", }, ), definitions=[])¶
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶
__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="FermiEnergyParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_type", py_key: Py( 0x00007f8564b3b1b0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c298b70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f858b9db230, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "domain", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "domain", py_key: Py( 0x00007f8564b3bd30, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863fdd1130, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b01150, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x00005569d9ab9a60, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "properties": 1, "crystals": 2, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f8564b3a8c0, ), ), values: [ Py( 0x00007f8564b01070, ), Py( 0x00007f8564b010e0, ), Py( 0x00007f8564b01150, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_name", py_key: Py( 0x00007f8564b3bd70, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299a70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f861e5ca570, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_version", py_key: Py( 0x00007f8564b40210, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c2ac030, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_application", py_key: Py( 0x00007f8564b402b0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f85657124c0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b074f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "batch_size", py_key: Py( 0x00007f8564b3bdb0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a20d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "workers", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "workers", py_key: Py( 0x00007f8564b3bdf0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863dad90b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a00d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, ], model_name: "FermiEnergyParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x00005569d9ad3e60, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f863e1e3a60, ), name: "FermiEnergyParameters", }, ), definitions=[], cache_strings=True)¶
The pydantic-core SchemaValidator used to validate instances of the model.
- __signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'FermiEnergy', batch_size: int = 256, workers: int = 0) -> None>¶
The synthesized __init__ [Signature][inspect.Signature] of the model.
- _abc_impl = <_abc._abc_data object>¶
- model_config: ClassVar[ConfigDict] = {}¶
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class BulkModuliParameters(**data)[source]¶
Bases:
CGCNNParameters- algorithm_application: str¶
- __dict__¶
- __pydantic_fields_set__: set[str]¶
The names of fields explicitly set during instantiation.
- __pydantic_extra__: dict[str, Any] | None¶
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_private__: dict[str, Any] | None¶
Values of private attributes set on the model instance.
- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶
- __class_vars__: ClassVar[set[str]] = {}¶
The names of the class variables defined on the model.
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True¶
Whether model building is completed, or if there are still undefined fields.
- __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶
A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.BulkModuliParameters'>, 'config': {'title': 'BulkModuliParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.BulkModuliParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.BulkModuliParameters:93913111952112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'BulkModuli', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:93913111829088', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'BulkModuliParameters', 'type': 'model-fields'}, 'type': 'model'}¶
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False¶
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='BulkModuli'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶
A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶
The name of the post-init method for the model, if defined.
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005569d9ad7af0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "domain": SerField { key_py: Py( 0x00007f863fdd1130, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b01150, ), ), serializer: Enum( EnumSerializer { class: Py( 0x00005569d9ab9a60, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, }, "batch_size": SerField { key_py: Py( 0x00007f863c299270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a20d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_type": SerField { key_py: Py( 0x00007f863c298b70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f858b9db230, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_name": SerField { key_py: Py( 0x00007f863c299a70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f861e5ca570, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "workers": SerField { key_py: Py( 0x00007f863dad90b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a00d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_version": SerField { key_py: Py( 0x00007f863c2ac030, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, }, "algorithm_application": SerField { key_py: Py( 0x00007f85657124c0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b07330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "BulkModuliParameters", }, ), definitions=[])¶
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶
__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="BulkModuliParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_type", py_key: Py( 0x00007f85708eb2b0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c298b70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f858b9db230, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "domain", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "domain", py_key: Py( 0x00007f8564b45070, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863fdd1130, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b01150, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x00005569d9ab9a60, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "properties": 1, "crystals": 2, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f8564b3bb80, ), ), values: [ Py( 0x00007f8564b01070, ), Py( 0x00007f8564b010e0, ), Py( 0x00007f8564b01150, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_name", py_key: Py( 0x00007f8564b450b0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299a70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f861e5ca570, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_version", py_key: Py( 0x00007f8564b403f0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c2ac030, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_application", py_key: Py( 0x00007f8564b40490, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f85657124c0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b07330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "batch_size", py_key: Py( 0x00007f8564b450f0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a20d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "workers", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "workers", py_key: Py( 0x00007f8564b444f0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863dad90b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a00d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, ], model_name: "BulkModuliParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x00005569d9ad7af0, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f863e1e3a60, ), name: "BulkModuliParameters", }, ), definitions=[], cache_strings=True)¶
The pydantic-core SchemaValidator used to validate instances of the model.
- __signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'BulkModuli', batch_size: int = 256, workers: int = 0) -> None>¶
The synthesized __init__ [Signature][inspect.Signature] of the model.
- _abc_impl = <_abc._abc_data object>¶
- model_config: ClassVar[ConfigDict] = {}¶
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class ShearModuliParameters(**data)[source]¶
Bases:
CGCNNParameters- algorithm_application: str¶
- __dict__¶
- __pydantic_fields_set__: set[str]¶
The names of fields explicitly set during instantiation.
- __pydantic_extra__: dict[str, Any] | None¶
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_private__: dict[str, Any] | None¶
Values of private attributes set on the model instance.
- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶
- __class_vars__: ClassVar[set[str]] = {}¶
The names of the class variables defined on the model.
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True¶
Whether model building is completed, or if there are still undefined fields.
- __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶
A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.ShearModuliParameters'>, 'config': {'title': 'ShearModuliParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.ShearModuliParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.ShearModuliParameters:93913111967056', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'ShearModuli', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:93913111829088', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'ShearModuliParameters', 'type': 'model-fields'}, 'type': 'model'}¶
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False¶
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='ShearModuli'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶
A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶
The name of the post-init method for the model, if defined.
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005569d9adb550, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_name": SerField { key_py: Py( 0x00007f863c299a70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f861e5ca570, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_type": SerField { key_py: Py( 0x00007f863c298b70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f858b9db230, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_application": SerField { key_py: Py( 0x00007f85657124c0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b1eb30, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_version": SerField { key_py: Py( 0x00007f863c2ac030, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, }, "domain": SerField { key_py: Py( 0x00007f863fdd1130, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b01150, ), ), serializer: Enum( EnumSerializer { class: Py( 0x00005569d9ab9a60, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, }, "workers": SerField { key_py: Py( 0x00007f863dad90b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a00d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "batch_size": SerField { key_py: Py( 0x00007f863c299270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a20d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "ShearModuliParameters", }, ), definitions=[])¶
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶
__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="ShearModuliParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_type", py_key: Py( 0x00007f8564b45770, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c298b70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f858b9db230, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "domain", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "domain", py_key: Py( 0x00007f8564b462f0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863fdd1130, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b01150, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x00005569d9ab9a60, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "crystals": 2, "properties": 1, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f8564b44ec0, ), ), values: [ Py( 0x00007f8564b01070, ), Py( 0x00007f8564b010e0, ), Py( 0x00007f8564b01150, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_name", py_key: Py( 0x00007f8564b46330, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299a70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f861e5ca570, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_version", py_key: Py( 0x00007f8564b405d0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c2ac030, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_application", py_key: Py( 0x00007f8564b40670, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f85657124c0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b1eb30, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "batch_size", py_key: Py( 0x00007f8564b46370, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a20d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "workers", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "workers", py_key: Py( 0x00007f8564b463b0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863dad90b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a00d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, ], model_name: "ShearModuliParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x00005569d9adb550, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f863e1e3a60, ), name: "ShearModuliParameters", }, ), definitions=[], cache_strings=True)¶
The pydantic-core SchemaValidator used to validate instances of the model.
- __signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'ShearModuli', batch_size: int = 256, workers: int = 0) -> None>¶
The synthesized __init__ [Signature][inspect.Signature] of the model.
- _abc_impl = <_abc._abc_data object>¶
- model_config: ClassVar[ConfigDict] = {}¶
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class PoissonRatioParameters(**data)[source]¶
Bases:
CGCNNParameters- algorithm_application: str¶
- __dict__¶
- __pydantic_fields_set__: set[str]¶
The names of fields explicitly set during instantiation.
- __pydantic_extra__: dict[str, Any] | None¶
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_private__: dict[str, Any] | None¶
Values of private attributes set on the model instance.
- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶
- __class_vars__: ClassVar[set[str]] = {}¶
The names of the class variables defined on the model.
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True¶
Whether model building is completed, or if there are still undefined fields.
- __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶
A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.PoissonRatioParameters'>, 'config': {'title': 'PoissonRatioParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.PoissonRatioParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.PoissonRatioParameters:93913111984064', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'PoissonRatio', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:93913111829088', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'PoissonRatioParameters', 'type': 'model-fields'}, 'type': 'model'}¶
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False¶
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='PoissonRatio'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶
A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶
The name of the post-init method for the model, if defined.
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005569d9adf7c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_version": SerField { key_py: Py( 0x00007f863c2ac030, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, }, "algorithm_application": SerField { key_py: Py( 0x00007f85657124c0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b1ebb0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_type": SerField { key_py: Py( 0x00007f863c298b70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f858b9db230, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "batch_size": SerField { key_py: Py( 0x00007f863c299270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a20d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "workers": SerField { key_py: Py( 0x00007f863dad90b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a00d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "domain": SerField { key_py: Py( 0x00007f863fdd1130, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b01150, ), ), serializer: Enum( EnumSerializer { class: Py( 0x00005569d9ab9a60, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_name": SerField { key_py: Py( 0x00007f863c299a70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f861e5ca570, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "PoissonRatioParameters", }, ), definitions=[])¶
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶
__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="PoissonRatioParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_type", py_key: Py( 0x00007f8564b469f0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c298b70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f858b9db230, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "domain", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "domain", py_key: Py( 0x00007f8564b47570, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863fdd1130, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b01150, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x00005569d9ab9a60, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "molecules": 0, "properties": 1, "crystals": 2, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f8564b46140, ), ), values: [ Py( 0x00007f8564b01070, ), Py( 0x00007f8564b010e0, ), Py( 0x00007f8564b01150, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_name", py_key: Py( 0x00007f8564b475b0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299a70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f861e5ca570, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_version", py_key: Py( 0x00007f856af050c0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c2ac030, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_application", py_key: Py( 0x00007f8564b40800, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f85657124c0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b1ebb0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "batch_size", py_key: Py( 0x00007f8564b475f0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a20d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "workers", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "workers", py_key: Py( 0x00007f8564b47630, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863dad90b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a00d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, ], model_name: "PoissonRatioParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x00005569d9adf7c0, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f863e1e3a60, ), name: "PoissonRatioParameters", }, ), definitions=[], cache_strings=True)¶
The pydantic-core SchemaValidator used to validate instances of the model.
- __signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'PoissonRatio', batch_size: int = 256, workers: int = 0) -> None>¶
The synthesized __init__ [Signature][inspect.Signature] of the model.
- _abc_impl = <_abc._abc_data object>¶
- model_config: ClassVar[ConfigDict] = {}¶
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class MetalSemiconductorClassifierParameters(**data)[source]¶
Bases:
CGCNNParameters- algorithm_application: str¶
- __dict__¶
- __pydantic_fields_set__: set[str]¶
The names of fields explicitly set during instantiation.
- __pydantic_extra__: dict[str, Any] | None¶
A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.
- __pydantic_private__: dict[str, Any] | None¶
Values of private attributes set on the model instance.
- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶
- __class_vars__: ClassVar[set[str]] = {}¶
The names of the class variables defined on the model.
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶
Metadata about the private attributes of the model.
- __pydantic_complete__: ClassVar[bool] = True¶
Whether model building is completed, or if there are still undefined fields.
- __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶
A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
- __pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.MetalSemiconductorClassifierParameters'>, 'config': {'title': 'MetalSemiconductorClassifierParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.MetalSemiconductorClassifierParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.MetalSemiconductorClassifierParameters:93913111999008', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'MetalSemiconductorClassifier', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:93913111829088', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'MetalSemiconductorClassifierParameters', 'type': 'model-fields'}, 'type': 'model'}¶
The core schema of the model.
- __pydantic_custom_init__: ClassVar[bool] = False¶
Whether the model has a custom __init__ method.
- __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶
Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
- __pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='MetalSemiconductorClassifier'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶
A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.
- __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶
Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
- __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶
Parent namespace of the model, used for automatic rebuilding of models.
- __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶
The name of the post-init method for the model, if defined.
- __pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x00005569d9ae3220, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_type": SerField { key_py: Py( 0x00007f863c298b70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f858b9db230, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_name": SerField { key_py: Py( 0x00007f863c299a70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f861e5ca570, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "domain": SerField { key_py: Py( 0x00007f863fdd1130, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b01150, ), ), serializer: Enum( EnumSerializer { class: Py( 0x00005569d9ab9a60, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, }, "algorithm_version": SerField { key_py: Py( 0x00007f863c2ac030, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, }, "algorithm_application": SerField { key_py: Py( 0x00007f85657124c0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f8564b0e470, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "batch_size": SerField { key_py: Py( 0x00007f863c299270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a20d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, "workers": SerField { key_py: Py( 0x00007f863dad90b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f86404a00d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "MetalSemiconductorClassifierParameters", }, ), definitions=[])¶
The pydantic-core SchemaSerializer used to dump instances of the model.
- __pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶
__setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__
- __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="MetalSemiconductorClassifierParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_type", py_key: Py( 0x00007f8564b47c70, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c298b70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f858b9db230, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "domain", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "domain", py_key: Py( 0x00007f8564b547f0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863fdd1130, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b01150, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x00005569d9ab9a60, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "crystals": 2, "properties": 1, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f8564b473c0, ), ), values: [ Py( 0x00007f8564b01070, ), Py( 0x00007f8564b010e0, ), Py( 0x00007f8564b01150, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_name", py_key: Py( 0x00007f8564b54830, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299a70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f861e5ca570, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_version", py_key: Py( 0x00007f8564b408f0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c2ac030, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "algorithm_application", py_key: Py( 0x00007f8564b40990, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f85657124c0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f8564b0e470, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "batch_size", py_key: Py( 0x00007f8564b54870, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863c299270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a20d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, Field { name: "workers", lookup_key_collection: LookupKeyCollection { by_name: Simple( LookupPath { first_item: PathItemString { key: "workers", py_key: Py( 0x00007f8564b548b0, ), }, rest: [], }, ), by_alias: None, by_alias_then_name: None, }, name_py: Py( 0x00007f863dad90b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f86404a00d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f863e1e3a60, ), }, ), frozen: false, }, ], model_name: "MetalSemiconductorClassifierParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x00005569d9ae3220, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f863e1e3a60, ), name: "MetalSemiconductorClassifierParameters", }, ), definitions=[], cache_strings=True)¶
The pydantic-core SchemaValidator used to validate instances of the model.
- __signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'MetalSemiconductorClassifier', batch_size: int = 256, workers: int = 0) -> None>¶
The synthesized __init__ [Signature][inspect.Signature] of the model.
- _abc_impl = <_abc._abc_data object>¶
- model_config: ClassVar[ConfigDict] = {}¶
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class MetalNonMetalClassifier(parameters)[source]¶
Bases:
PredictorAlgorithmMetal/non-metal classifier class.
- __init__(parameters)[source]¶
Targeted or untargeted generation.
- Parameters
configuration – application specific helper that allows to setup the generator.
- get_model(resources_path)[source]¶
Instantiate the actual model.
- Parameters
resources_path (
str) – local path to model files.- Returns
the model.
- Return type
Predictor
- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'max_runtime': 'int'}¶
- __doc__ = 'Metal/non-metal classifier class.'¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __parameters__ = ()¶
- _abc_impl = <_abc._abc_data object>¶
- class FormationEnergy(parameters)[source]¶
Bases:
_CGCNN- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'max_runtime': 'int'}¶
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __parameters__ = ()¶
- _abc_impl = <_abc._abc_data object>¶
- class AbsoluteEnergy(parameters)[source]¶
Bases:
_CGCNN- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'max_runtime': 'int'}¶
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __parameters__ = ()¶
- _abc_impl = <_abc._abc_data object>¶
- class BandGap(parameters)[source]¶
Bases:
_CGCNN- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'max_runtime': 'int'}¶
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __parameters__ = ()¶
- _abc_impl = <_abc._abc_data object>¶
- class FermiEnergy(parameters)[source]¶
Bases:
_CGCNN- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'max_runtime': 'int'}¶
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __parameters__ = ()¶
- _abc_impl = <_abc._abc_data object>¶
- class BulkModuli(parameters)[source]¶
Bases:
_CGCNN- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'max_runtime': 'int'}¶
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __parameters__ = ()¶
- _abc_impl = <_abc._abc_data object>¶
- class ShearModuli(parameters)[source]¶
Bases:
_CGCNN- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'max_runtime': 'int'}¶
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __parameters__ = ()¶
- _abc_impl = <_abc._abc_data object>¶
- class PoissonRatio(parameters)[source]¶
Bases:
_CGCNN- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'max_runtime': 'int'}¶
- __doc__ = None¶
- __module__ = 'gt4sd.properties.crystals.core'¶
- __parameters__ = ()¶
- _abc_impl = <_abc._abc_data object>¶