gt4sd.algorithms.generation.polymer_blocks.core module¶
PaccMann vanilla generator trained on polymer building blocks (catalysts/monomers).
Summary¶
Classes:
Configuration to generate subunits of polymers. |
Reference¶
- class PolymerBlocks(configuration, target=None)[source]¶
Bases:
GeneratorAlgorithm[S,None]- __init__(configuration, target=None)[source]¶
Polymer blocks generation.
- Parameters
configuration (
AlgorithmConfiguration) – domain and application specification, defining types and validations.target (
None) – unused since it is not a conditional generator.
Example
An example for generating small molecules (SMILES) that resembles monomers/catalysts for polymer synthesis:
configuration = PolymerBlocksGenerator() polymer_blocks = PolymerBlocks(configuration=configuration) items = list(polymer_blocks.sample(10)) print(items)
- get_generator(configuration, target)[source]¶
Get the function to sample batches via the Generator.
- Parameters
configuration (
AlgorithmConfiguration[~S,None]) – helps to set up the application.target (
None) – context or condition for the generation. Unused in the algorithm.
- Return type
Callable[[],Iterable[Any]]- Returns
callable generating a batch of items.
- validate_configuration(configuration)[source]¶
Overload to validate the a configuration for the algorithm.
- Parameters
configuration (
AlgorithmConfiguration) – the algorithm configuration.- Raises
InvalidAlgorithmConfiguration – in case the configuration for the algorithm is invalid.
- Return type
- Returns
the validated configuration.
- __abstractmethods__ = frozenset({})¶
- __annotations__ = {'generate': 'Untargeted', 'generator': 'Union[Untargeted, Targeted[T]]', 'max_runtime': 'int', 'max_samples': 'int', 'target': 'Optional[T]'}¶
- __doc__ = None¶
- __module__ = 'gt4sd.algorithms.generation.polymer_blocks.core'¶
- __orig_bases__ = (gt4sd.algorithms.core.GeneratorAlgorithm[~S, NoneType],)¶
- __parameters__ = (~S,)¶
- _abc_impl = <_abc._abc_data object>¶
- class PolymerBlocksGenerator(*args, **kwargs)[source]¶
Bases:
PolymerBlocksGenerator,Generic[T]Configuration to generate subunits of polymers.
- algorithm_type: ClassVar[str] = 'generation'¶
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.
- batch_size: int = 32¶
- generated_length: int = 100¶
- get_target_description()[source]¶
Get description of the target for generation.
- Return type
Optional[Dict[str,str],None]- Returns
target description, returns None in case no target is used.
- validate_item(item)[source]¶
Overload to validate an item.
- Parameters
item (
str) – validate an item.- Raises
InvalidItem – in case the item can not be validated.
- Returns
the validated item.
- Return type
S
- 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'>, 'batch_size': <class 'int'>, 'domain': typing.ClassVar[str], 'generated_length': <class 'int'>}¶
- __dataclass_fields__ = {'algorithm_application': Field(name='algorithm_application',type=typing.ClassVar[str],default='PolymerBlocksGenerator',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='PolymerBlocks',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='generation',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), 'batch_size': Field(name='batch_size',type=<class 'int'>,default=32,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({'description': 'Batch size used for the generative model sampling.'}),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)}¶
- __dataclass_params__ = _DataclassParams(init=True,repr=True,eq=True,order=False,unsafe_hash=False,frozen=False)¶
- __doc__ = 'Configuration to generate subunits of polymers.'¶
- __eq__(other)¶
Return self==value.
- __hash__ = None¶
- __init__(*args, **kwargs)¶
- __is_pydantic_dataclass__ = True¶
- __match_args__ = ('algorithm_version', 'batch_size', 'generated_length')¶
- __module__ = 'gt4sd.algorithms.generation.polymer_blocks.core'¶
- __orig_bases__ = (<class 'types.PolymerBlocksGenerator'>, typing.Generic[~T])¶
- __parameters__ = (~T,)¶
- __pydantic_complete__ = True¶
- __pydantic_config__ = {}¶
- __pydantic_core_schema__ = {'cls': <class 'gt4sd.algorithms.generation.polymer_blocks.core.PolymerBlocksGenerator'>, 'config': {'title': 'PolymerBlocksGenerator'}, 'fields': ['algorithm_version', 'batch_size', 'generated_length'], 'frozen': False, 'post_init': False, 'ref': 'types.PolymerBlocksGenerator:94818331012784', 'schema': {'collect_init_only': False, 'computed_fields': [], 'dataclass_name': 'PolymerBlocksGenerator', 'fields': [{'type': 'dataclass-field', 'name': 'algorithm_version', 'schema': {'type': 'default', 'schema': {'type': 'str'}, 'default': 'v0'}, 'kw_only': False, 'init': True, 'metadata': {}}, {'type': 'dataclass-field', 'name': 'batch_size', 'schema': {'type': 'default', 'schema': {'type': 'int'}, 'default': 32}, 'kw_only': False, 'init': True, 'metadata': {'pydantic_js_updates': {'description': 'Batch size used for the generative model sampling.'}}}, {'type': 'dataclass-field', 'name': 'generated_length', 'schema': {'type': 'default', 'schema': {'type': 'int'}, 'default': 100}, 'kw_only': False, 'init': True, 'metadata': {'pydantic_js_updates': {'description': 'Maximum length in tokens of the generated molcules (relates to the SMILES length).'}}}], '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, kw_only=False), 'batch_size': FieldInfo(annotation=int, required=False, default=32, description='Batch size used for the generative model sampling.', init=True, 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, kw_only=False)}¶
- classmethod __pydantic_fields_complete__()¶
Return whether the fields were successfully collected (i.e. type hints were successfully resolved).
This is a private helper, not meant to be used outside Pydantic.
- Return type
bool
- __pydantic_serializer__ = SchemaSerializer(serializer=PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9cf02eb0, ), serializer: PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9cf02eb0, ), serializer: Dataclass( DataclassSerializer { class: Py( 0x0000563c9cf02eb0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "batch_size": SerField { key: "batch_size", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3004d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "generated_length": SerField { key: "generated_length", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f300d50, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_version": SerField { key: "algorithm_version", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222afe89f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), fields: [ Py( 0x00007f222affaf10, ), Py( 0x00007f222afe83f0, ), Py( 0x00007f214cb7e970, ), ], name: "PolymerBlocksGenerator", }, ), enabled_from_config: false, }, ), enabled_from_config: false, }, ), definitions=[])¶
- __pydantic_validator__ = SchemaValidator(title="PolymerBlocksGenerator", validator=Dataclass( DataclassValidator { strict: false, validator: DataclassArgs( DataclassArgsValidator { fields: [ Field { kw_only: false, name: "algorithm_version", init: true, init_only: false, lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_version", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222afe89f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { kw_only: false, name: "batch_size", init: true, init_only: false, lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "batch_size", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3004d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { kw_only: false, name: "generated_length", init: true, init_only: false, lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "generated_length", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f300d50, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, ], positional_count: 3, init_only_count: None, dataclass_name: "PolymerBlocksGenerator", validator_name: "dataclass-args[PolymerBlocksGenerator]", extra_behavior: Ignore, extras_validator: None, loc_by_alias: true, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x0000563c9cf02eb0, ), generic_origin: None, fields: [ Py( 0x00007f222affaf10, ), Py( 0x00007f222afe83f0, ), Py( 0x00007f214cb7e970, ), ], post_init: None, revalidate: Never, name: "PolymerBlocksGenerator", frozen: false, slots: true, }, ), definitions=[], cache_strings=True)¶
- __repr__()¶
Return repr(self).
- __signature__ = <Signature (algorithm_version: str = 'v0', batch_size: int = 32, generated_length: int = 100) -> None>¶
- __wrapped__¶
alias of
PolymerBlocksGenerator