gt4sd.algorithms.generation.paccmann_vae.core module¶
PaccMannVAE Algorithm.
PaccMannVAE is an unconditional molecular generative model.
Summary¶
Classes:
Molecular VAE as in the PaccMannRL paper. |
|
Configuration to generate molecules with PaccMannVAE. |
Reference¶
- class PaccMannVAE(configuration, target=None)[source]¶
Bases:
GeneratorAlgorithm
[S
,None
]Molecular VAE as in the PaccMannRL paper.
- __init__(configuration, target=None)[source]¶
Instantiate PaccMannVAE ready to generate molecules.
- Parameters
configuration (
AlgorithmConfiguration
[~S,None
]) – domain and application specification defining parameters, types and validations.target (
None
) – unused since it is not a conditional generator.
Example
An example for unconditional generation of small molecules:
config = PaccMannVAEGenerator() algorithm = PaccMannVAE(configuration=config) items = list(algorithm.sample(10)) print(items)
- get_generator(configuration, target)[source]¶
Get the function to sample batches via PaccMannVAE.
- Parameters
configuration (
AlgorithmConfiguration
[~S,None
]) – helps to set up specific application of PaccMannVAE.- Return type
Callable
[[],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__ = 'Molecular VAE as in the PaccMann\\ :superscript:`RL` paper.'¶
- __module__ = 'gt4sd.algorithms.generation.paccmann_vae.core'¶
- __orig_bases__ = (gt4sd.algorithms.core.GeneratorAlgorithm[~S, NoneType],)¶
- __parameters__ = (~S,)¶
- _abc_impl = <_abc._abc_data object>¶
- class PaccMannVAEGenerator(*args, **kwargs)[source]¶
Bases:
PaccMannVAEGenerator
,Generic
[T
]Configuration to generate molecules with PaccMannVAE.
Implementation from the paper: https://doi.org/10.1016/j.isci.2021.102269
- 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¶
- temperature: float = 1.4¶
- 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.
- get_conditional_generator()[source]¶
Instantiate the actual generator implementation.
- Parameters
resources_path – local path to model files.
- Return type
- Returns
instance with
generate_batch
method for targeted generation.
- classmethod list_versions()[source]¶
Get possible algorithm versions.
S3 is searched as well as the local cache is searched for matching versions.
- Return type
Set
[str
]- Returns
viable values as
algorithm_version
for the environment.
- __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'>, 'temperature': <class 'float'>}¶
- __dataclass_fields__ = {'algorithm_application': Field(name='algorithm_application',type=typing.ClassVar[str],default='PaccMannVAEGenerator',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='PaccMannVAE',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), 'temperature': Field(name='temperature',type=<class 'float'>,default=1.4,default_factory=<dataclasses._MISSING_TYPE object>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({'description': 'Temperature parameter for the softmax sampling in decoding.'}),kw_only=False,_field_type=_FIELD)}¶
- __dataclass_params__ = _DataclassParams(init=True,repr=True,eq=True,order=False,unsafe_hash=False,frozen=False)¶
- __doc__ = '\n Configuration to generate molecules with PaccMannVAE.\n\n Implementation from the paper: https://doi.org/10.1016/j.isci.2021.102269\n '¶
- __eq__(other)¶
Return self==value.
- __hash__ = None¶
- __init__(*args, **kwargs)¶
- __match_args__ = ('algorithm_version', 'batch_size', 'temperature', 'generated_length')¶
- __module__ = 'gt4sd.algorithms.generation.paccmann_vae.core'¶
- __orig_bases__ = (<class 'types.PaccMannVAEGenerator'>, typing.Generic[~T])¶
- __parameters__ = (~T,)¶
- __pydantic_complete__ = True¶
- __pydantic_config__ = {}¶
- __pydantic_core_schema__ = {'cls': <class 'gt4sd.algorithms.generation.paccmann_vae.core.PaccMannVAEGenerator'>, 'config': {'title': 'PaccMannVAEGenerator'}, 'fields': ['algorithm_version', 'batch_size', 'temperature', 'generated_length'], 'frozen': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.algorithms.generation.paccmann_vae.core.PaccMannVAEGenerator'>, title=None)]}, 'post_init': False, 'ref': 'types.PaccMannVAEGenerator:94662829823344', 'schema': {'collect_init_only': False, 'computed_fields': [], 'dataclass_name': 'PaccMannVAEGenerator', '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': 'batch_size', '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': 'temperature', 'schema': {'type': 'default', 'schema': {'type': 'float'}, 'default': 1.4}, '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-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), 'batch_size': FieldInfo(annotation=int, required=False, default=32, description='Batch size used for the generative model sampling.', 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), 'temperature': FieldInfo(annotation=float, required=False, default=1.4, description='Temperature parameter for the softmax sampling in decoding.', init=True, init_var=False, kw_only=False)}¶
- __pydantic_serializer__ = SchemaSerializer(serializer=Dataclass( DataclassSerializer { class: Py( 0x0000561868585570, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_version": SerField { key_py: Py( 0x00007f1dc3855840, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea52cf3f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f1dc38296b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94684d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "temperature": SerField { key_py: Py( 0x00007f1dc38291f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dc3929890, ), ), serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), ), required: true, }, "generated_length": SerField { key_py: Py( 0x00007f1dc3855890, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea9468d50, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), fields: [ Py( 0x00007f1ea52ed250, ), Py( 0x00007f1ea52ccc70, ), Py( 0x00007f1e33d8d4b0, ), Py( 0x00007f1dca9b95c0, ), ], name: "PaccMannVAEGenerator", }, ), definitions=[])¶
- __pydantic_validator__ = SchemaValidator(title="PaccMannVAEGenerator", 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( 0x00007f1dc3855750, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dc38557a0, ), ), ], ), }, 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: "batch_size", py_name: Py( 0x00007f1ea52ccc70, ), init: true, init_only: false, lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f1dc3945470, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f1dc3944070, ), ), ], ), }, 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: "temperature", py_name: Py( 0x00007f1e33d8d4b0, ), init: true, init_only: false, lookup_key: Simple { key: "temperature", py_key: Py( 0x00007f1dc3945bf0, ), path: LookupPath( [ S( "temperature", Py( 0x00007f1dc3818b30, ), ), ], ), }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dc3929890, ), ), on_error: Raise, validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), validate_default: false, copy_default: false, name: "default[float]", 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( 0x00007f1dc3855700, ), path: LookupPath( [ S( "generated_length", Py( 0x00007f1dc38557f0, ), ), ], ), }, 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, }, ], positional_count: 4, init_only_count: None, dataclass_name: "PaccMannVAEGenerator", validator_name: "dataclass-args[PaccMannVAEGenerator]", extra_behavior: Ignore, extras_validator: None, loc_by_alias: true, }, ), class: Py( 0x0000561868585570, ), fields: [ Py( 0x00007f1ea52ed250, ), Py( 0x00007f1ea52ccc70, ), Py( 0x00007f1e33d8d4b0, ), Py( 0x00007f1dca9b95c0, ), ], post_init: None, revalidate: Never, name: "PaccMannVAEGenerator", frozen: false, slots: true, }, ), definitions=[], cache_strings=True)¶
- __repr__()¶
Return repr(self).
- __signature__ = <Signature (algorithm_version: str = 'v0', batch_size: int = 32, temperature: float = 1.4, generated_length: int = 100) -> None>¶
- __wrapped__¶
alias of
PaccMannVAEGenerator