gt4sd.algorithms.controlled_sampling.advanced_manufacturing.core module¶

Advanced manufacturing algorithms.

Summary¶

Classes:

`AdvancedManufacturing`	Advance manufacturing generator algorithm.
`CatalystGenerator`	Configuration to generate catalysts with a desired binding energy.

Reference¶

class AdvancedManufacturing(configuration, target)[source]¶

Bases: GeneratorAlgorithm[S, T]

Advance manufacturing generator algorithm.

__init__(configuration, target)[source]¶

Instantiate AdvancedManufacturing ready to generate items.

Parameters

configuration (AlgorithmConfiguration[~S, ~T]) – domain and application specification defining parameters, types and validations.
target (Optional[~T, None]) – a target for which to generate items.

Example

An example for generating small molecules (SMILES) with a target binding energy:

config = CatalystGenerator()
algorithm = AdvancedManufacturing(configuration=config, target=10.0)
items = list(algorithm.sample(10))
print(items)

get_generator(configuration, target)[source]¶

Get the function to sample batches via AdvancedManufacturing’s generator.

Parameters

configuration (AlgorithmConfiguration[~S, ~T]) – helps to set up specific application of AdvancedManufacturing.
target (Optional[~T, None]) – context or condition for the generation.

Return type

Callable[[~T], Iterable[Any]]

Returns

callable with target generating a batch of items.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'generate': 'Untargeted', 'generator': 'Union[Untargeted, Targeted[T]]', 'max_runtime': 'int', 'max_samples': 'int', 'target': 'Optional[T]'}¶

__doc__ = 'Advance manufacturing generator algorithm.'¶

__module__ = 'gt4sd.algorithms.controlled_sampling.advanced_manufacturing.core'¶

__orig_bases__ = (gt4sd.algorithms.core.GeneratorAlgorithm[~S, ~T],)¶

__parameters__ = (~S, ~T)¶

_abc_impl = <_abc._abc_data object>¶

class CatalystGenerator(*args, **kwargs)[source]¶

Bases: CatalystGenerator, Generic[T]

Configuration to generate catalysts with a desired binding energy.

algorithm_type: ClassVar[str] = 'controlled_sampling'¶: General type of generative algorithm.

domain: ClassVar[str] = 'materials'¶: General application domain. Hints at input/output types.

algorithm_version: str = 'v0'¶

To differentiate between different versions of an application.

There is no imposed naming convention.

number_of_points: int = 32¶

number_of_steps: int = 50¶

generated_length: int = 100¶

primer_smiles: str = ''¶

get_target_description()[source]¶

Get description of the target for generation.

Return type: Dict[str, str]
Returns: target description.

get_conditional_generator(resources_path)[source]¶

Instantiate the actual generator implementation.

Parameters: resources_path (str) – local path to model files.
Return type: Generator
Returns: instance with generate_batch method for targeted generation.

classmethod get_filepath_mappings_for_training_pipeline_arguments()[source]¶

Ger filepath mappings for the given training pipeline arguments.

Parameters: training_pipeline_arguments (TrainingPipelineArguments) – training pipeline arguments.
Return type: Dict[str, str]
Returns: a mapping between artifacts’ files and training pipeline’s output files.

__annotations__ = {'algorithm_application': 'ClassVar[str]', 'algorithm_name': 'ClassVar[str]', 'algorithm_type': typing.ClassVar[str], 'algorithm_version': <class 'str'>, 'domain': typing.ClassVar[str], 'generated_length': <class 'int'>, 'number_of_points': <class 'int'>, 'number_of_steps': <class 'int'>, 'primer_smiles': <class 'str'>}¶

__dataclass_fields__ = {'algorithm_application': Field(name='algorithm_application',type=typing.ClassVar[str],default='CatalystGenerator',default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=<dataclasses._MISSING_TYPE object>,_field_type=_FIELD_CLASSVAR), 'algorithm_name': Field(name='algorithm_name',type=typing.ClassVar[str],default='AdvancedManufacturing',default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=<dataclasses._MISSING_TYPE object>,_field_type=_FIELD_CLASSVAR), 'algorithm_type': Field(name='algorithm_type',type=typing.ClassVar[str],default='controlled_sampling',default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=<dataclasses._MISSING_TYPE object>,_field_type=_FIELD_CLASSVAR), 'algorithm_version': Field(name='algorithm_version',type=<class 'str'>,default='v0',default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=False,_field_type=_FIELD), 'domain': Field(name='domain',type=typing.ClassVar[str],default='materials',default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),kw_only=<dataclasses._MISSING_TYPE object>,_field_type=_FIELD_CLASSVAR), 'generated_length': Field(name='generated_length',type=<class 'int'>,default=100,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({'description': 'Maximum length in tokens of the generated molcules (relates to the SMILES length).'}),kw_only=False,_field_type=_FIELD), 'number_of_points': Field(name='number_of_points',type=<class 'int'>,default=32,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({'description': 'Number of points to sample with the Gaussian Process.'}),kw_only=False,_field_type=_FIELD), 'number_of_steps': Field(name='number_of_steps',type=<class 'int'>,default=50,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({'description': 'Number of optimization steps in the Gaussian Process optimization.'}),kw_only=False,_field_type=_FIELD), 'primer_smiles': Field(name='primer_smiles',type=<class 'str'>,default='',default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({'description': 'Primer molecule to initiate the sampling in SMILES format. Defaults to no primer.'}),kw_only=False,_field_type=_FIELD)}¶

__dataclass_params__ = _DataclassParams(init=True,repr=True,eq=True,order=False,unsafe_hash=False,frozen=False)¶

__doc__ = 'Configuration to generate catalysts with a desired binding energy.'¶

__eq__(other)¶: Return self==value.

__hash__ = None¶

__init__(*args, **kwargs)¶

__match_args__ = ('algorithm_version', 'number_of_points', 'number_of_steps', 'generated_length', 'primer_smiles')¶

__module__ = 'gt4sd.algorithms.controlled_sampling.advanced_manufacturing.core'¶

__orig_bases__ = (<class 'types.CatalystGenerator'>, typing.Generic[~T])¶

__parameters__ = (~T,)¶

__pydantic_complete__ = True¶

__pydantic_config__ = {}¶

__pydantic_core_schema__ = {'cls': <class 'gt4sd.algorithms.controlled_sampling.advanced_manufacturing.core.CatalystGenerator'>, 'config': {'title': 'CatalystGenerator'}, 'fields': ['algorithm_version', 'number_of_points', 'number_of_steps', 'generated_length', 'primer_smiles'], 'frozen': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.algorithms.controlled_sampling.advanced_manufacturing.core.CatalystGenerator'>, title=None)]}, 'post_init': False, 'ref': 'types.CatalystGenerator:94662817388080', 'schema': {'collect_init_only': False, 'computed_fields': [], 'dataclass_name': 'CatalystGenerator', 'fields': [{'type': 'dataclass-field', 'name': 'algorithm_version', 'schema': {'type': 'default', 'schema': {'type': 'str'}, 'default': 'v0'}, 'kw_only': False, 'init': True, 'metadata': {'pydantic_js_functions': [], 'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>]}}, {'type': 'dataclass-field', 'name': 'number_of_points', 'schema': {'type': 'default', 'schema': {'type': 'int'}, 'default': 32}, 'kw_only': False, 'init': True, 'metadata': {'pydantic_js_functions': [], 'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>]}}, {'type': 'dataclass-field', 'name': 'number_of_steps', 'schema': {'type': 'default', 'schema': {'type': 'int'}, 'default': 50}, 'kw_only': False, 'init': True, 'metadata': {'pydantic_js_functions': [], 'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>]}}, {'type': 'dataclass-field', 'name': 'generated_length', 'schema': {'type': 'default', 'schema': {'type': 'int'}, 'default': 100}, 'kw_only': False, 'init': True, 'metadata': {'pydantic_js_functions': [], 'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>]}}, {'type': 'dataclass-field', 'name': 'primer_smiles', 'schema': {'type': 'default', 'schema': {'type': 'str'}, 'default': ''}, 'kw_only': False, 'init': True, 'metadata': {'pydantic_js_functions': [], 'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>]}}], 'type': 'dataclass-args'}, 'slots': True, 'type': 'dataclass'}¶

__pydantic_decorators__ = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶

__pydantic_fields__ = {'algorithm_version': FieldInfo(annotation=str, required=False, default='v0', init=True, init_var=False, kw_only=False), 'generated_length': FieldInfo(annotation=int, required=False, default=100, description='Maximum length in tokens of the generated molcules (relates to the SMILES length).', init=True, init_var=False, kw_only=False), 'number_of_points': FieldInfo(annotation=int, required=False, default=32, description='Number of points to sample with the Gaussian Process.', init=True, init_var=False, kw_only=False), 'number_of_steps': FieldInfo(annotation=int, required=False, default=50, description='Number of optimization steps in the Gaussian Process optimization.', init=True, init_var=False, kw_only=False), 'primer_smiles': FieldInfo(annotation=str, required=False, default='', description='Primer molecule to initiate the sampling in SMILES format. Defaults to no primer.', init=True, init_var=False, kw_only=False)}¶

__pydantic_serializer__ = SchemaSerializer(serializer=Dataclass( DataclassSerializer { class: Py( 0x00005618679a9630, ), serializer: Fields( GeneralFieldsSerializer { fields: { "number_of_steps": SerField { key_py: Py( 0x00007f1dc54f0370, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea9468710, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "primer_smiles": SerField { key_py: Py( 0x00007f1dc54f03b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea9470030, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "generated_length": SerField { key_py: Py( 0x00007f1dc54e3500, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea9468d50, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "number_of_points": SerField { key_py: Py( 0x00007f1dc54e34b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94684d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1dc54e3460, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea52cf3f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), fields: [ Py( 0x00007f1ea52ed250, ), Py( 0x00007f1dca93e4c0, ), Py( 0x00007f1dca921870, ), Py( 0x00007f1dca9b95c0, ), Py( 0x00007f1dca923730, ), ], name: "CatalystGenerator", }, ), definitions=[])¶

__pydantic_validator__ = SchemaValidator(title="CatalystGenerator", validator=Dataclass( DataclassValidator { strict: false, validator: DataclassArgs( DataclassArgsValidator { fields: [ Field { kw_only: false, name: "algorithm_version", py_name: Py( 0x00007f1ea52ed250, ), init: true, init_only: false, lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dc54e32d0, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dc54e3320, ), ), ], ), }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea52cf3f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { kw_only: false, name: "number_of_points", py_name: Py( 0x00007f1dca93e4c0, ), init: true, init_only: false, lookup_key: Simple { key: "number_of_points", py_key: Py( 0x00007f1dc54e3280, ), path: LookupPath( [ S( "number_of_points", Py( 0x00007f1dc54e3370, ), ), ], ), }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94684d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { kw_only: false, name: "number_of_steps", py_name: Py( 0x00007f1dca921870, ), init: true, init_only: false, lookup_key: Simple { key: "number_of_steps", py_key: Py( 0x00007f1dc54e77b0, ), path: LookupPath( [ S( "number_of_steps", Py( 0x00007f1dc54f0330, ), ), ], ), }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea9468710, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { kw_only: false, name: "generated_length", py_name: Py( 0x00007f1dca9b95c0, ), init: true, init_only: false, lookup_key: Simple { key: "generated_length", py_key: Py( 0x00007f1dc54e33c0, ), path: LookupPath( [ S( "generated_length", Py( 0x00007f1dc54e3410, ), ), ], ), }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea9468d50, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { kw_only: false, name: "primer_smiles", py_name: Py( 0x00007f1dca923730, ), init: true, init_only: false, lookup_key: Simple { key: "primer_smiles", py_key: Py( 0x00007f1dc54f02f0, ), path: LookupPath( [ S( "primer_smiles", Py( 0x00007f1dc54f02b0, ), ), ], ), }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea9470030, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], positional_count: 5, init_only_count: None, dataclass_name: "CatalystGenerator", validator_name: "dataclass-args[CatalystGenerator]", extra_behavior: Ignore, extras_validator: None, loc_by_alias: true, }, ), class: Py( 0x00005618679a9630, ), fields: [ Py( 0x00007f1ea52ed250, ), Py( 0x00007f1dca93e4c0, ), Py( 0x00007f1dca921870, ), Py( 0x00007f1dca9b95c0, ), Py( 0x00007f1dca923730, ), ], post_init: None, revalidate: Never, name: "CatalystGenerator", frozen: false, slots: true, }, ), definitions=[], cache_strings=True)¶

__repr__()¶: Return repr(self).

__signature__ = <Signature (algorithm_version: str = 'v0', number_of_points: int = 32, number_of_steps: int = 50, generated_length: int = 100, primer_smiles: str = '') -> None>¶

__wrapped__¶: alias of CatalystGenerator