gt4sd.properties.core module

Summary

Classes:

ApiTokenParameters
CallablePropertyPredictor	Property predictor based on a callable.
ConfigurableCallablePropertyPredictor	Property predictor based on a callable that is configured using the provided parameters.
DomainSubmodule	An enumeration.
IpAdressParameters
PropertyPredictor	PropertyPredictor base class.
PropertyPredictorParameters	Abstract class for property computation.
S3Parameters

Reference

class DomainSubmodule(value)

Bases: str, Enum

An enumeration.

molecules: str = 'molecules'

properties: str = 'properties'

crystals: str = 'crystals'

_generate_next_value_(start, count, last_values)

Generate the next value when not given.

name: the name of the member start: the initial start value or None count: the number of existing members last_value: the last value assigned or None

__module__ = 'gt4sd.properties.core'

__annotations__ = {'crystals': <class 'str'>, 'molecules': <class 'str'>, 'properties': <class 'str'>}

__dict__ = mappingproxy({'_generate_next_value_': <function Enum._generate_next_value_>, '__module__': 'gt4sd.properties.core', '__annotations__': {'molecules': <class 'str'>, 'properties': <class 'str'>, 'crystals': <class 'str'>}, '__doc__': 'An enumeration.', '__dict__': <attribute '__dict__' of 'DomainSubmodule' objects>, '__weakref__': <attribute '__weakref__' of 'DomainSubmodule' objects>, '_member_names_': ['molecules', 'properties', 'crystals'], '_member_map_': {'molecules': <DomainSubmodule.molecules: 'molecules'>, 'properties': <DomainSubmodule.properties: 'properties'>, 'crystals': <DomainSubmodule.crystals: 'crystals'>}, '_member_type_': <class 'str'>, '_value2member_map_': {'molecules': <DomainSubmodule.molecules: 'molecules'>, 'properties': <DomainSubmodule.properties: 'properties'>, 'crystals': <DomainSubmodule.crystals: 'crystals'>}, 'molecules': <DomainSubmodule.molecules: 'molecules'>, 'properties': <DomainSubmodule.properties: 'properties'>, 'crystals': <DomainSubmodule.crystals: 'crystals'>, '__repr__': <function Enum.__repr__>, '__str__': <function Enum.__str__>, '__format__': <function Enum.__format__>, '__new__': <function Enum.__new__>})

__weakref__

list of weak references to the object (if defined)

_member_names_ = ['molecules', 'properties', 'crystals']

_member_map_ = {'crystals': DomainSubmodule.crystals, 'molecules': DomainSubmodule.molecules, 'properties': DomainSubmodule.properties}

_member_type_

alias of str

_value2member_map_ = {'crystals': DomainSubmodule.crystals, 'molecules': DomainSubmodule.molecules, 'properties': DomainSubmodule.properties}

__doc__ = 'An enumeration.'

class PropertyPredictorParameters(**data)

Bases: BaseModel

Abstract class for property computation.

__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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]'}

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__doc__ = 'Abstract class for property computation.'

__module__ = 'gt4sd.properties.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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.core.PropertyPredictorParameters'>, 'config': {'title': 'PropertyPredictorParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.core.PropertyPredictorParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.core.PropertyPredictorParameters'>>]}, 'ref': 'gt4sd.properties.core.PropertyPredictorParameters:94662808870720', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'PropertyPredictorParameters', '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_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(             0x0000561867189f40,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {},                 computed_fields: Some(                     ComputedFields(                         [],                     ),                 ),                 mode: SimpleDict,                 extra_serializer: None,                 filter: SchemaFilter {                     include: None,                     exclude: None,                 },                 required_fields: 0,             },         ),         has_extra: false,         root_model: false,         name: "PropertyPredictorParameters",     }, ), definitions=[])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="PropertyPredictorParameters", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [],                 model_name: "PropertyPredictorParameters",                 extra_behavior: Ignore,                 extras_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,             },         ),         class: Py(             0x0000561867189f40,         ),         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f1ea71db950,         ),         name: "PropertyPredictorParameters",     }, ), definitions=[], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature () -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

__weakref__

list of weak references to the object (if defined)

_abc_impl = <_abc._abc_data object>

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class S3Parameters(**data)

Bases: PropertyPredictorParameters

algorithm_type: str

domain: DomainSubmodule

algorithm_name: str

algorithm_version: 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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': <class 'str'>, 'algorithm_type': <class 'str'>, 'algorithm_version': <class 'str'>, 'domain': <enum 'DomainSubmodule'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]'}

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__doc__ = None

__module__ = 'gt4sd.properties.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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.core.S3Parameters'>, 'config': {'title': 'S3Parameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.core.S3Parameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.core.S3Parameters'>>]}, 'ref': 'gt4sd.properties.core.S3Parameters:94662808875184', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'S3Parameters', '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_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(             0x000056186718b0b0,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "algorithm_name": SerField {                         key_py: Py(                             0x00007f1ea52ce770,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Str(                                 StrSerializer,                             ),                         ),                         required: true,                     },                     "domain": SerField {                         key_py: Py(                             0x00007f1ea8dd1270,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Enum(                                 EnumSerializer {                                     class: Py(                                         0x0000561867189b80,                                     ),                                     serializer: Some(                                         Str(                                             StrSerializer,                                         ),                                     ),                                 },                             ),                         ),                         required: true,                     },                     "algorithm_application": SerField {                         key_py: Py(                             0x00007f1dcf4e0530,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Str(                                 StrSerializer,                             ),                         ),                         required: true,                     },                     "algorithm_type": SerField {                         key_py: Py(                             0x00007f1ea52cf4f0,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             WithDefault(                                 WithDefaultSerializer {                                     default: Default(                                         Py(                                             0x00007f1dea152330,                                         ),                                     ),                                     serializer: Str(                                         StrSerializer,                                     ),                                 },                             ),                         ),                         required: true,                     },                     "algorithm_version": SerField {                         key_py: Py(                             0x00007f1ea52ed250,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Str(                                 StrSerializer,                             ),                         ),                         required: true,                     },                 },                 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: "S3Parameters",     }, ), definitions=[])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="S3Parameters", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "algorithm_type",                         lookup_key: Simple {                             key: "algorithm_type",                             py_key: Py(                                 0x00007f1dce8bf9b0,                             ),                             path: LookupPath(                                 [                                     S(                                         "algorithm_type",                                         Py(                                             0x00007f1dce8bf970,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f1ea52cf4f0,                         ),                         validator: WithDefault(                             WithDefaultValidator {                                 default: Default(                                     Py(                                         0x00007f1dea152330,                                     ),                                 ),                                 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 {                         name: "domain",                         lookup_key: Simple {                             key: "domain",                             py_key: Py(                                 0x00007f1dce8bf930,                             ),                             path: LookupPath(                                 [                                     S(                                         "domain",                                         Py(                                             0x00007f1dce8bf8f0,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f1ea8dd1270,                         ),                         validator: StrEnum(                             EnumValidator {                                 phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>,                                 class: Py(                                     0x0000561867189b80,                                 ),                                 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,                                     values: [                                         Py(                                             0x00007f1dcea99230,                                         ),                                         Py(                                             0x00007f1dcea992a0,                                         ),                                         Py(                                             0x00007f1dcea99310,                                         ),                                     ],                                 },                                 missing: None,                                 expected_repr: "'molecules', 'properties' or 'crystals'",                                 strict: false,                                 class_repr: "DomainSubmodule",                                 name: "str-enum[DomainSubmodule]",                             },                         ),                         frozen: false,                     },                     Field {                         name: "algorithm_name",                         lookup_key: Simple {                             key: "algorithm_name",                             py_key: Py(                                 0x00007f1dce8bf9f0,                             ),                             path: LookupPath(                                 [                                     S(                                         "algorithm_name",                                         Py(                                             0x00007f1dce8bfa30,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f1ea52ce770,                         ),                         validator: Str(                             StrValidator {                                 strict: false,                                 coerce_numbers_to_str: false,                             },                         ),                         frozen: false,                     },                     Field {                         name: "algorithm_version",                         lookup_key: Simple {                             key: "algorithm_version",                             py_key: Py(                                 0x00007f1dce8c8580,                             ),                             path: LookupPath(                                 [                                     S(                                         "algorithm_version",                                         Py(                                             0x00007f1dce8c8620,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f1ea52ed250,                         ),                         validator: Str(                             StrValidator {                                 strict: false,                                 coerce_numbers_to_str: false,                             },                         ),                         frozen: false,                     },                     Field {                         name: "algorithm_application",                         lookup_key: Simple {                             key: "algorithm_application",                             py_key: Py(                                 0x00007f1dce8c85d0,                             ),                             path: LookupPath(                                 [                                     S(                                         "algorithm_application",                                         Py(                                             0x00007f1dce8c8670,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f1dcf4e0530,                         ),                         validator: Str(                             StrValidator {                                 strict: false,                                 coerce_numbers_to_str: false,                             },                         ),                         frozen: false,                     },                 ],                 model_name: "S3Parameters",                 extra_behavior: Ignore,                 extras_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,             },         ),         class: Py(             0x000056186718b0b0,         ),         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f1ea71db950,         ),         name: "S3Parameters",     }, ), 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, 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_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_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=True, description='Submodule of gt4sd.properties', examples=['molecules'])}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class ApiTokenParameters(**data)

Bases: PropertyPredictorParameters

api_token: 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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'api_token': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]'}

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__doc__ = None

__module__ = 'gt4sd.properties.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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.core.ApiTokenParameters'>, 'config': {'title': 'ApiTokenParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.core.ApiTokenParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.core.ApiTokenParameters'>>]}, 'ref': 'gt4sd.properties.core.ApiTokenParameters:94662808881872', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'api_token': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'ApiTokenParameters', '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_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(             0x000056186718cad0,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "api_token": SerField {                         key_py: Py(                             0x00007f1de214f970,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Str(                                 StrSerializer,                             ),                         ),                         required: true,                     },                 },                 computed_fields: Some(                     ComputedFields(                         [],                     ),                 ),                 mode: SimpleDict,                 extra_serializer: None,                 filter: SchemaFilter {                     include: None,                     exclude: None,                 },                 required_fields: 1,             },         ),         has_extra: false,         root_model: false,         name: "ApiTokenParameters",     }, ), definitions=[])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="ApiTokenParameters", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "api_token",                         lookup_key: Simple {                             key: "api_token",                             py_key: Py(                                 0x00007f1dce8d07f0,                             ),                             path: LookupPath(                                 [                                     S(                                         "api_token",                                         Py(                                             0x00007f1dce8d05f0,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f1de214f970,                         ),                         validator: Str(                             StrValidator {                                 strict: false,                                 coerce_numbers_to_str: false,                             },                         ),                         frozen: false,                     },                 ],                 model_name: "ApiTokenParameters",                 extra_behavior: Ignore,                 extras_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,             },         ),         class: Py(             0x000056186718cad0,         ),         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f1ea71db950,         ),         name: "ApiTokenParameters",     }, ), definitions=[], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, api_token: str) -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'api_token': FieldInfo(annotation=str, required=True, description='The API token/key to access the service', examples=['apk-c9db......'])}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class IpAdressParameters(**data)

Bases: PropertyPredictorParameters

host_ip: 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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'host_ip': <class 'str'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]'}

__class_vars__: ClassVar[set[str]] = {}

The names of the class variables defined on the model.

__doc__ = None

__module__ = 'gt4sd.properties.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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.core.IpAdressParameters'>, 'config': {'title': 'IpAdressParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.core.IpAdressParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.core.IpAdressParameters'>>]}, 'ref': 'gt4sd.properties.core.IpAdressParameters:94662808891968', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'host_ip': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'IpAdressParameters', '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_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(             0x000056186718f240,         ),         serializer: Fields(             GeneralFieldsSerializer {                 fields: {                     "host_ip": SerField {                         key_py: Py(                             0x00007f1ea72d54b0,                         ),                         alias: None,                         alias_py: None,                         serializer: Some(                             Str(                                 StrSerializer,                             ),                         ),                         required: true,                     },                 },                 computed_fields: Some(                     ComputedFields(                         [],                     ),                 ),                 mode: SimpleDict,                 extra_serializer: None,                 filter: SchemaFilter {                     include: None,                     exclude: None,                 },                 required_fields: 1,             },         ),         has_extra: false,         root_model: false,         name: "IpAdressParameters",     }, ), definitions=[])

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="IpAdressParameters", validator=Model(     ModelValidator {         revalidate: Never,         validator: ModelFields(             ModelFieldsValidator {                 fields: [                     Field {                         name: "host_ip",                         lookup_key: Simple {                             key: "host_ip",                             py_key: Py(                                 0x00007f1dce8d1370,                             ),                             path: LookupPath(                                 [                                     S(                                         "host_ip",                                         Py(                                             0x00007f1dce8d11b0,                                         ),                                     ),                                 ],                             ),                         },                         name_py: Py(                             0x00007f1ea72d54b0,                         ),                         validator: Str(                             StrValidator {                                 strict: false,                                 coerce_numbers_to_str: false,                             },                         ),                         frozen: false,                     },                 ],                 model_name: "IpAdressParameters",                 extra_behavior: Ignore,                 extras_validator: None,                 strict: false,                 from_attributes: false,                 loc_by_alias: true,             },         ),         class: Py(             0x000056186718f240,         ),         post_init: None,         frozen: false,         custom_init: false,         root_model: false,         undefined: Py(             0x00007f1ea71db950,         ),         name: "IpAdressParameters",     }, ), definitions=[], cache_strings=True)

The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, host_ip: str) -> None>

The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'host_ip': FieldInfo(annotation=str, required=True, description='The host IP address to access the service', examples=['xx.xx.xxx.xxx'])}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class PropertyPredictor(parameters=PropertyPredictorParameters())

Bases: object

PropertyPredictor base class.

__init__(parameters=PropertyPredictorParameters())

Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__call__(sample)

Call the PropertyPredictor.

Parameters

sample (Any) – a sample to use for predicting the property of interest.

Return type

Property

Example

An example for predicting properties:

property_predictor = PropertyPredictor(parameters)
value = property_predictor(sample)

__dict__ = mappingproxy({'__module__': 'gt4sd.properties.core', '__doc__': 'PropertyPredictor base class.', '__init__': <function PropertyPredictor.__init__>, '__call__': <function PropertyPredictor.__call__>, '__dict__': <attribute '__dict__' of 'PropertyPredictor' objects>, '__weakref__': <attribute '__weakref__' of 'PropertyPredictor' objects>, '__annotations__': {}})

__doc__ = 'PropertyPredictor base class.'

__module__ = 'gt4sd.properties.core'

__weakref__

list of weak references to the object (if defined)

class CallablePropertyPredictor(callable_fn, parameters=PropertyPredictorParameters())

Bases: PropertyPredictor

Property predictor based on a callable.

__init__(callable_fn, parameters=PropertyPredictorParameters())

Construct a PropertyPredictor using the related parameters. :type parameters: PropertyPredictorParameters :param parameters: parameters to configure the predictor.

__call__(sample)

Call the PropertyPredictor.

Parameters

sample (Any) – a sample to use for predicting the property of interest.

Returns

Property predicted by the predictor.

Return type

Property

Example

An example for predicting properties:

property_predictor = CallablePropertyPredictor(callable_fn=lambda a: id(a), parameters)
value = property_predictor(sample)

__annotations__ = {}

__doc__ = 'Property predictor based on a callable.'

__module__ = 'gt4sd.properties.core'

class ConfigurableCallablePropertyPredictor(callable_fn, parameters=PropertyPredictorParameters())

Bases: CallablePropertyPredictor

Property predictor based on a callable that is configured using the provided parameters.

__call__(sample)

Call the PropertyPredictor.

Parameters

sample (Any) – a sample to use for predicting the property of interest.

Return type

Property

Example

An example for predicting properties:

property_predictor = CallablePropertyPredictor(callable_fn=lambda a, b: id(a), parameters)
value = property_predictor(sample)

__annotations__ = {}

__doc__ = 'Property predictor based on a callable that is configured using the provided parameters.'

__module__ = 'gt4sd.properties.core'