lomas_server.utils package

Submodules

lomas_server.utils.anti_timing_att module

lomas_server.utils.collection_models module

class lomas_server.utils.collection_models.Dataset(*, dataset_name: str, metadata: MetadataOfPathDB | MetadataOfS3DB)

Bases: BaseModel

BaseModel for a dataset

dataset_name: str

metadata: MetadataOfPathDB | MetadataOfS3DB

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]] = {'dataset_name': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[MetadataOfPathDB, MetadataOfS3DB], required=True, discriminator='database_type')}

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

This replaces Model.__fields__ from Pydantic V1.

class lomas_server.utils.collection_models.DatasetOfPathDB(*, dataset_name: str, metadata: MetadataOfPathDB | MetadataOfS3DB, database_type: Literal[PrivateDatabaseType.PATH], dataset_path: str)

Bases: Dataset

BaseModel for a local dataset

database_type: Literal[PrivateDatabaseType.PATH]

dataset_path: str

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]] = {'database_type': FieldInfo(annotation=Literal[<PrivateDatabaseType.PATH: 'PATH_DB'>], required=True), 'dataset_name': FieldInfo(annotation=str, required=True), 'dataset_path': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[MetadataOfPathDB, MetadataOfS3DB], required=True, discriminator='database_type')}

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

This replaces Model.__fields__ from Pydantic V1.

class lomas_server.utils.collection_models.DatasetOfS3DB(*, dataset_name: str, metadata: MetadataOfPathDB | MetadataOfS3DB, database_type: Literal[PrivateDatabaseType.S3], endpoint_url: str, bucket: str, key: str, credentials_name: str)

Bases: Dataset

BaseModel for a dataset on S3

bucket: str

credentials_name: str

database_type: Literal[PrivateDatabaseType.S3]

endpoint_url: str

key: str

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]] = {'bucket': FieldInfo(annotation=str, required=True), 'credentials_name': FieldInfo(annotation=str, required=True), 'database_type': FieldInfo(annotation=Literal[<PrivateDatabaseType.S3: 'S3_DB'>], required=True), 'dataset_name': FieldInfo(annotation=str, required=True), 'endpoint_url': FieldInfo(annotation=str, required=True), 'key': FieldInfo(annotation=str, required=True), 'metadata': FieldInfo(annotation=Union[MetadataOfPathDB, MetadataOfS3DB], required=True, discriminator='database_type')}

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

This replaces Model.__fields__ from Pydantic V1.

class lomas_server.utils.collection_models.DatasetOfUser(*, dataset_name: str, initial_epsilon: float, initial_delta: float, total_spent_epsilon: float, total_spent_delta: float)

Bases: BaseModel

BaseModel for informations of a user on a dataset

dataset_name: str

initial_delta: float

initial_epsilon: float

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]] = {'dataset_name': FieldInfo(annotation=str, required=True), 'initial_delta': FieldInfo(annotation=float, required=True), 'initial_epsilon': FieldInfo(annotation=float, required=True), 'total_spent_delta': FieldInfo(annotation=float, required=True), 'total_spent_epsilon': FieldInfo(annotation=float, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

total_spent_delta: float

total_spent_epsilon: float

class lomas_server.utils.collection_models.DatasetsCollection(*, datasets: List[DatasetOfPathDB | DatasetOfS3DB])

Bases: BaseModel

BaseModel for datasets collection

datasets: List[DatasetOfPathDB | DatasetOfS3DB]

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]] = {'datasets': FieldInfo(annotation=List[Union[DatasetOfPathDB, DatasetOfS3DB]], required=True, discriminator='database_type')}

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

This replaces Model.__fields__ from Pydantic V1.

class lomas_server.utils.collection_models.Metadata(*, max_ids: int, row_privacy: bool, censor_dims: bool | None = False, columns: Dict[str, Dict[str, int | float | str | List[str]]])

Bases: BaseModel

BaseModel for a metadata format

censor_dims: bool | None

columns: Dict[str, Dict[str, int | float | str | List[str]]]

max_ids: int

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]] = {'censor_dims': FieldInfo(annotation=Union[bool, NoneType], required=False, default=False), 'columns': FieldInfo(annotation=Dict[str, Dict[str, Union[int, float, str, List[str]]]], required=True), 'max_ids': FieldInfo(annotation=int, required=True), 'row_privacy': FieldInfo(annotation=bool, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

row_privacy: bool

class lomas_server.utils.collection_models.MetadataOfDataset

Bases: BaseModel

BaseModel for metadata of a dataset

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].

This replaces Model.__fields__ from Pydantic V1.

class lomas_server.utils.collection_models.MetadataOfPathDB(*, database_type: Literal[PrivateDatabaseType.PATH], metadata_path: str)

Bases: MetadataOfDataset

BaseModel for metadata of a dataset with PATH_DB

database_type: Literal[PrivateDatabaseType.PATH]

metadata_path: str

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]] = {'database_type': FieldInfo(annotation=Literal[<PrivateDatabaseType.PATH: 'PATH_DB'>], required=True), 'metadata_path': FieldInfo(annotation=str, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

class lomas_server.utils.collection_models.MetadataOfS3DB(*, database_type: Literal[PrivateDatabaseType.S3], endpoint_url: str, bucket: str, key: str, access_key_id: str | None = None, secret_access_key: str | None = None, credentials_name: str)

Bases: MetadataOfDataset

BaseModel for metadata of a dataset with S3_DB

access_key_id: str | None

bucket: str

credentials_name: str

database_type: Literal[PrivateDatabaseType.S3]

endpoint_url: str

key: str

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]] = {'access_key_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'bucket': FieldInfo(annotation=str, required=True), 'credentials_name': FieldInfo(annotation=str, required=True), 'database_type': FieldInfo(annotation=Literal[<PrivateDatabaseType.S3: 'S3_DB'>], required=True), 'endpoint_url': FieldInfo(annotation=str, required=True), 'key': FieldInfo(annotation=str, required=True), 'secret_access_key': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}

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

This replaces Model.__fields__ from Pydantic V1.

secret_access_key: str | None

class lomas_server.utils.collection_models.User(*, user_name: str, may_query: bool, datasets_list: List[DatasetOfUser])

Bases: BaseModel

BaseModel for a user in a user collection

datasets_list: List[DatasetOfUser]

may_query: bool

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]] = {'datasets_list': FieldInfo(annotation=List[DatasetOfUser], required=True), 'may_query': FieldInfo(annotation=bool, required=True), 'user_name': FieldInfo(annotation=str, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

user_name: str

class lomas_server.utils.collection_models.UserCollection(*, users: List[User])

Bases: BaseModel

BaseModel for users collection

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]] = {'users': FieldInfo(annotation=List[User], required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

users: List[User]

lomas_server.utils.config module

lomas_server.utils.error_handler module

lomas_server.utils.logger module

lomas_server.utils.query_examples module

lomas_server.utils.query_examples.make_dummy(example_query)

Make dummy example dummy query based on example query

lomas_server.utils.query_models module

class lomas_server.utils.query_models.DiffPrivLibModel(*, dataset_name: str, diffprivlib_json: str, feature_columns: list, target_columns: list | None, test_size: Annotated[float, Gt(gt=0.0), Lt(lt=1.0)], test_train_split_seed: int, imputer_strategy: str)

Bases: BaseModel

Model input for a diffprivlib query

dataset_name: str

diffprivlib_json: str

feature_columns: list

imputer_strategy: str

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]] = {'dataset_name': FieldInfo(annotation=str, required=True), 'diffprivlib_json': FieldInfo(annotation=str, required=True), 'feature_columns': FieldInfo(annotation=list, required=True), 'imputer_strategy': FieldInfo(annotation=str, required=True), 'target_columns': FieldInfo(annotation=Union[list, NoneType], required=True), 'test_size': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0.0), Lt(lt=1.0)]), 'test_train_split_seed': FieldInfo(annotation=int, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

target_columns: list | None

test_size: float

test_train_split_seed: int

class lomas_server.utils.query_models.DummyDiffPrivLibModel(*, dataset_name: str, diffprivlib_json: str, feature_columns: list, target_columns: list | None, test_size: Annotated[float, Gt(gt=0.0), Lt(lt=1.0)], test_train_split_seed: int, imputer_strategy: str, dummy_nb_rows: Annotated[int, Gt(gt=0)], dummy_seed: int)

Bases: BaseModel

Model input for a dummy diffprivlib query

dataset_name: str

diffprivlib_json: str

dummy_nb_rows: int

dummy_seed: int

feature_columns: list

imputer_strategy: str

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]] = {'dataset_name': FieldInfo(annotation=str, required=True), 'diffprivlib_json': FieldInfo(annotation=str, required=True), 'dummy_nb_rows': FieldInfo(annotation=int, required=True, metadata=[Gt(gt=0)]), 'dummy_seed': FieldInfo(annotation=int, required=True), 'feature_columns': FieldInfo(annotation=list, required=True), 'imputer_strategy': FieldInfo(annotation=str, required=True), 'target_columns': FieldInfo(annotation=Union[list, NoneType], required=True), 'test_size': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0.0), Lt(lt=1.0)]), 'test_train_split_seed': FieldInfo(annotation=int, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

target_columns: list | None

test_size: float

test_train_split_seed: int

class lomas_server.utils.query_models.DummyOpenDPModel(*, dataset_name: str, opendp_json: str, dummy_nb_rows: Annotated[int, Gt(gt=0)], dummy_seed: int, fixed_delta: float | None = None)

Bases: BaseModel

Model input for a dummy opendp query

dataset_name: str

dummy_nb_rows: int

dummy_seed: int

fixed_delta: float | None

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]] = {'dataset_name': FieldInfo(annotation=str, required=True), 'dummy_nb_rows': FieldInfo(annotation=int, required=True, metadata=[Gt(gt=0)]), 'dummy_seed': FieldInfo(annotation=int, required=True), 'fixed_delta': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'opendp_json': FieldInfo(annotation=str, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

opendp_json: str

class lomas_server.utils.query_models.DummySmartnoiseSQLModel(*, query_str: str, dataset_name: str, dummy_nb_rows: Annotated[int, Gt(gt=0)], dummy_seed: int, epsilon: Annotated[float, Gt(gt=0)], delta: Annotated[float, Gt(gt=0)], mechanisms: dict, postprocess: bool)

Bases: BaseModel

Model input for a smarnoise-sql dummy query

dataset_name: str

delta: float

dummy_nb_rows: int

dummy_seed: int

epsilon: float

mechanisms: dict

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]] = {'dataset_name': FieldInfo(annotation=str, required=True), 'delta': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0)]), 'dummy_nb_rows': FieldInfo(annotation=int, required=True, metadata=[Gt(gt=0)]), 'dummy_seed': FieldInfo(annotation=int, required=True), 'epsilon': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0)]), 'mechanisms': FieldInfo(annotation=dict, required=True), 'postprocess': FieldInfo(annotation=bool, required=True), 'query_str': FieldInfo(annotation=str, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

postprocess: bool

query_str: str

class lomas_server.utils.query_models.DummySmartnoiseSynthQueryModel(*, dataset_name: str, synth_name: SSynthMarginalSynthesizer | SSynthGanSynthesizer, epsilon: Annotated[float, Gt(gt=0), Le(le=5.0)], delta: float | None = None, select_cols: List, synth_params: dict, nullable: bool, constraints: str, return_model: bool, condition: str, nb_samples: int, dummy_nb_rows: Annotated[int, Gt(gt=0)], dummy_seed: int)

Bases: SmartnoiseSynthQueryModel

Dummy Model input for a smarnoise-synth query

dummy_nb_rows: int

dummy_seed: int

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]] = {'condition': FieldInfo(annotation=str, required=True), 'constraints': FieldInfo(annotation=str, required=True), 'dataset_name': FieldInfo(annotation=str, required=True), 'delta': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'dummy_nb_rows': FieldInfo(annotation=int, required=True, metadata=[Gt(gt=0)]), 'dummy_seed': FieldInfo(annotation=int, required=True), 'epsilon': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0), Le(le=5.0)]), 'nb_samples': FieldInfo(annotation=int, required=True), 'nullable': FieldInfo(annotation=bool, required=True), 'return_model': FieldInfo(annotation=bool, required=True), 'select_cols': FieldInfo(annotation=List, required=True), 'synth_name': FieldInfo(annotation=Union[SSynthMarginalSynthesizer, SSynthGanSynthesizer], required=True), 'synth_params': FieldInfo(annotation=dict, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

class lomas_server.utils.query_models.GetDbData(*, dataset_name: str)

Bases: BaseModel

Model input to get information about a dataset

dataset_name: str

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]] = {'dataset_name': FieldInfo(annotation=str, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

class lomas_server.utils.query_models.GetDummyDataset(*, dataset_name: str, dummy_nb_rows: Annotated[int, Gt(gt=0)], dummy_seed: int)

Bases: BaseModel

Model input to get a dummy dataset

dataset_name: str

dummy_nb_rows: int

dummy_seed: int

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]] = {'dataset_name': FieldInfo(annotation=str, required=True), 'dummy_nb_rows': FieldInfo(annotation=int, required=True, metadata=[Gt(gt=0)]), 'dummy_seed': FieldInfo(annotation=int, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

class lomas_server.utils.query_models.OpenDPModel(*, dataset_name: str, opendp_json: str, fixed_delta: float | None = None)

Bases: BaseModel

Model input for an opendp query

dataset_name: str

fixed_delta: float | None

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]] = {'dataset_name': FieldInfo(annotation=str, required=True), 'fixed_delta': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'opendp_json': FieldInfo(annotation=str, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

opendp_json: str

class lomas_server.utils.query_models.SmartnoiseSQLCostModel(*, query_str: str, dataset_name: str, epsilon: Annotated[float, Gt(gt=0)], delta: Annotated[float, Gt(gt=0)], mechanisms: dict)

Bases: BaseModel

Model input for a smarnoise-sql cost query

dataset_name: str

delta: float

epsilon: float

mechanisms: dict

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]] = {'dataset_name': FieldInfo(annotation=str, required=True), 'delta': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0)]), 'epsilon': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0)]), 'mechanisms': FieldInfo(annotation=dict, required=True), 'query_str': FieldInfo(annotation=str, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

query_str: str

class lomas_server.utils.query_models.SmartnoiseSQLModel(*, query_str: str, dataset_name: str, epsilon: Annotated[float, Gt(gt=0), Le(le=5.0)], delta: Annotated[float, Gt(gt=0), Le(le=0.0004)], mechanisms: dict, postprocess: bool)

Bases: BaseModel

Model input for a smarnoise-sql query

dataset_name: str

delta: float

epsilon: float

mechanisms: dict

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]] = {'dataset_name': FieldInfo(annotation=str, required=True), 'delta': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0), Le(le=0.0004)]), 'epsilon': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0), Le(le=5.0)]), 'mechanisms': FieldInfo(annotation=dict, required=True), 'postprocess': FieldInfo(annotation=bool, required=True), 'query_str': FieldInfo(annotation=str, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

postprocess: bool

query_str: str

class lomas_server.utils.query_models.SmartnoiseSynthCostModel(*, dataset_name: str, synth_name: SSynthMarginalSynthesizer | SSynthGanSynthesizer, epsilon: Annotated[float, Gt(gt=0), Le(le=5.0)], delta: float | None = None, select_cols: List, synth_params: dict, nullable: bool, constraints: str)

Bases: BaseModel

Model input for a smarnoise-synth cost

constraints: str

dataset_name: str

delta: float | None

epsilon: float

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]] = {'constraints': FieldInfo(annotation=str, required=True), 'dataset_name': FieldInfo(annotation=str, required=True), 'delta': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'epsilon': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0), Le(le=5.0)]), 'nullable': FieldInfo(annotation=bool, required=True), 'select_cols': FieldInfo(annotation=List, required=True), 'synth_name': FieldInfo(annotation=Union[SSynthMarginalSynthesizer, SSynthGanSynthesizer], required=True), 'synth_params': FieldInfo(annotation=dict, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

nullable: bool

select_cols: List

synth_name: SSynthMarginalSynthesizer | SSynthGanSynthesizer

synth_params: dict

class lomas_server.utils.query_models.SmartnoiseSynthQueryModel(*, dataset_name: str, synth_name: SSynthMarginalSynthesizer | SSynthGanSynthesizer, epsilon: Annotated[float, Gt(gt=0), Le(le=5.0)], delta: float | None = None, select_cols: List, synth_params: dict, nullable: bool, constraints: str, return_model: bool, condition: str, nb_samples: int)

Bases: SmartnoiseSynthCostModel

Model input for a smarnoise-synth query

condition: str

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]] = {'condition': FieldInfo(annotation=str, required=True), 'constraints': FieldInfo(annotation=str, required=True), 'dataset_name': FieldInfo(annotation=str, required=True), 'delta': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'epsilon': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0), Le(le=5.0)]), 'nb_samples': FieldInfo(annotation=int, required=True), 'nullable': FieldInfo(annotation=bool, required=True), 'return_model': FieldInfo(annotation=bool, required=True), 'select_cols': FieldInfo(annotation=List, required=True), 'synth_name': FieldInfo(annotation=Union[SSynthMarginalSynthesizer, SSynthGanSynthesizer], required=True), 'synth_params': FieldInfo(annotation=dict, required=True)}

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

This replaces Model.__fields__ from Pydantic V1.

nb_samples: int

return_model: bool

Module contents