lomas_server.routes package
Submodules
lomas_server.routes.routes_admin module
- lomas_server.routes.routes_admin.get_dataset_metadata(request: Request, query_json: GetDbData = Body({'dataset_name': 'PENGUIN'}), user_name: str = Header(None)) JSONResponse[source]
Retrieves metadata for a given dataset.
- Parameters:
request (Request) – Raw request object
query_json (GetDbData, optional) – A JSON object containing the dataset_name key for indicating the dataset. Defaults to Body(example_get_admin_db_data).
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
- Returns:
- The metadata dictionary for the specified
dataset_name.
- Return type:
JSONResponse
- lomas_server.routes.routes_admin.get_dummy_dataset(request: Request, query_json: GetDummyDataset = Body({'dataset_name': 'PENGUIN', 'dummy_nb_rows': 100, 'dummy_seed': 42}), user_name: str = Header(None)) JSONResponse[source]
Generates and returns a dummy dataset.
- Parameters:
request (Request) – Raw request object
query_json (GetDummyDataset, optional) –
- A JSON object containing the following:
nb_rows (int, optional): The number of rows in the dummy dataset (default: 100).
seed (int, optional): The random seed for generating the dummy dataset (default: 42).
Defaults to Body(example_get_dummy_dataset).
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
- Returns:
- a dict with the dataframe as a dict, the column types
and the list of datetime columns.
- Return type:
JSONResponse
- lomas_server.routes.routes_admin.get_initial_budget(request: Request, query_json: GetDbData = Body({'dataset_name': 'PENGUIN'}), user_name: str = Header(None)) JSONResponse[source]
Returns the initial budget for a user and dataset.
- Parameters:
request (Request) – Raw request object
query_json (GetDbData, optional) –
A JSON object containing: - dataset_name (str): The name of the dataset.
Defaults to Body(example_get_admin_db_data).
user_name (str, optional) – The user name. Defaults to Header(None).
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – The dataset does not exist.
UnauthorizedAccessException – The user does not exist or the user does not have access to the dataset.
- Returns:
- a JSON object with:
initial_epsilon (float): initial epsilon budget.
initial_delta (float): initial delta budget.
- Return type:
JSONResponse
- lomas_server.routes.routes_admin.get_remaining_budget(request: Request, query_json: GetDbData = Body({'dataset_name': 'PENGUIN'}), user_name: str = Header(None)) JSONResponse[source]
Returns the remaining budget for a user and dataset.
- Parameters:
request (Request) – Raw request object
query_json (GetDbData, optional) –
A JSON object containing: - dataset_name (str): The name of the dataset.
Defaults to Body(example_get_admin_db_data).
user_name (str, optional) – The user name. Defaults to Header(None).
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – The dataset does not exist.
UnauthorizedAccessException – The user does not exist or the user does not have access to the dataset.
- Returns:
- a JSON object with:
remaining_epsilon (float): remaining epsilon budget.
remaining_delta (float): remaining delta budget.
- Return type:
JSONResponse
- async lomas_server.routes.routes_admin.get_state(request: Request, user_name: str = Header(None)) JSONResponse[source]
Returns the current state dict of this server instance.
- Parameters:
request (Request) – Raw request object
user_name (str, optional) – The user name. Defaults to Header(None).
- Returns:
The state of the server instance.
- Return type:
JSONResponse
- lomas_server.routes.routes_admin.get_total_spent_budget(request: Request, query_json: GetDbData = Body({'dataset_name': 'PENGUIN'}), user_name: str = Header(None)) JSONResponse[source]
Returns the spent budget for a user and dataset.
- Parameters:
request (Request) – Raw request object
query_json (GetDbData, optional) –
A JSON object containing: - dataset_name (str): The name of the dataset.
Defaults to Body(example_get_admin_db_data).
user_name (str, optional) – The user name. Defaults to Header(None).
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – The dataset does not exist.
UnauthorizedAccessException – The user does not exist or the user does not have access to the dataset.
- Returns:
- a JSON object with:
total_spent_epsilon (float): total spent epsilon budget.
total_spent_delta (float): total spent delta budget.
- Return type:
JSONResponse
- lomas_server.routes.routes_admin.get_user_previous_queries(request: Request, query_json: GetDbData = Body({'dataset_name': 'PENGUIN'}), user_name: str = Header(None)) JSONResponse[source]
Returns the query history of a user on a specific dataset.
- Parameters:
request (Request) – Raw request object
query_json (GetDbData, optional) –
A JSON object containing: - dataset_name (str): The name of the dataset.
Defaults to Body(example_get_admin_db_data).
user_name (str, optional) – The user name. Defaults to Header(None).
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – The dataset does not exist.
UnauthorizedAccessException – The user does not exist or the user does not have access to the dataset.
- Returns:
- A JSON object containing:
previous_queries (list[dict]): a list of dictionaries containing the previous queries.
- Return type:
JSONResponse
lomas_server.routes.routes_dp module
- lomas_server.routes.routes_dp.diffprivlib_query_handler(request: Request, query_json: DiffPrivLibQueryModel = Body({'dataset_name': 'PENGUIN', 'diffprivlib_json': '{"module": "diffprivlib", "version": "0.6.4", "pipeline": [{"type": "_dpl_type:StandardScaler", "name": "scaler", "params": {"with_mean": true, "with_std": true, "copy": true, "epsilon": 0.5, "bounds": {"_tuple": true, "_items": [[30.0, 13.0, 150.0, 2000.0], [65.0, 23.0, 250.0, 7000.0]]}, "random_state": null, "accountant": "_dpl_instance:BudgetAccountant"}}, {"type": "_dpl_type:LogisticRegression", "name": "classifier", "params": {"tol": 0.0001, "C": 1.0, "fit_intercept": true, "random_state": null, "max_iter": 100, "verbose": 0, "warm_start": false, "n_jobs": null, "epsilon": 1.0, "data_norm": 83.69469642643347, "accountant": "_dpl_instance:BudgetAccountant"}}]}', 'feature_columns': ['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g'], 'target_columns': ['species'], 'test_size': 0.2, 'test_train_split_seed': 4, 'imputer_strategy': 'drop'}), user_name: str = Header(None))[source]
Handles queries for the DiffPrivLib Library.
- Parameters:
request (Request) – Raw request object.
query_json (DiffPrivLibQueryModel, optional) –
- A JSON object containing the following:
pipeline: The DiffPrivLib pipeline for the query.
feature_columns: the list of feature column to train
target_columns: the list of target column to predict
test_size: proportion of the test set
test_train_split_seed: seed for the random train test split,
imputer_strategy: imputation strategy
Defaults to Body(example_diffprivlib).
user_name (str, optional) – The user name. Defaults to Header(None).
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – The pipeline does not contain a “measurement”, there is not enough budget or the dataset does not exist.
UnauthorizedAccessException – A query is already ongoing for this user, the user does not exist or does not have access to the dataset.
- Returns:
- A JSON object containing the following:
requested_by (str): The user name.
query_response (pd.DataFrame): A DataFrame containing the query response.
spent_epsilon (float): The amount of epsilon budget spent for the query.
spent_delta (float): The amount of delta budget spent for the query.
- Return type:
JSONResponse
- lomas_server.routes.routes_dp.dummy_diffprivlib_query_handler(request: Request, query_json: DiffPrivLibDummyQueryModel = Body({'dataset_name': 'PENGUIN', 'diffprivlib_json': '{"module": "diffprivlib", "version": "0.6.4", "pipeline": [{"type": "_dpl_type:StandardScaler", "name": "scaler", "params": {"with_mean": true, "with_std": true, "copy": true, "epsilon": 0.5, "bounds": {"_tuple": true, "_items": [[30.0, 13.0, 150.0, 2000.0], [65.0, 23.0, 250.0, 7000.0]]}, "random_state": null, "accountant": "_dpl_instance:BudgetAccountant"}}, {"type": "_dpl_type:LogisticRegression", "name": "classifier", "params": {"tol": 0.0001, "C": 1.0, "fit_intercept": true, "random_state": null, "max_iter": 100, "verbose": 0, "warm_start": false, "n_jobs": null, "epsilon": 1.0, "data_norm": 83.69469642643347, "accountant": "_dpl_instance:BudgetAccountant"}}]}', 'feature_columns': ['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g'], 'target_columns': ['species'], 'test_size': 0.2, 'test_train_split_seed': 4, 'imputer_strategy': 'drop', 'dummy_nb_rows': 100, 'dummy_seed': 42}), user_name: str = Header(None))[source]
Handles queries on dummy datasets for the DiffPrivLib library.
- Parameters:
request (Request) – Raw request object.
query_json (DiffPrivLibDummyQueryModel, optional) –
- A JSON object containing the following:
pipeline: The DiffPrivLib pipeline for the query.
feature_columns: the list of feature column to train
target_columns: the list of target column to predict
test_size: proportion of the test set
test_train_split_seed: seed for the random train test split,
imputer_strategy: imputation strategy
nb_rows (int, optional): The number of rows in the dummy dataset (default: 100).
seed (int, optional): The random seed for generating
the dummy dataset (default: 42).
Defaults to Body(example_dummy_diffprivlib)
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – If there is not enough budget or the dataset does not exist.
- Returns:
- A JSON object containing:
query_response (pd.DataFrame): a DataFrame containing the query response.
- Return type:
JSONResponse
- lomas_server.routes.routes_dp.dummy_opendp_query_handler(request: Request, query_json: OpenDPDummyQueryModel = Body({'dataset_name': 'PENGUIN', 'opendp_json': '{"version": "0.10.0", "ast": {"_type": "partial_chain", "lhs": {"_type": "partial_chain", "lhs": {"_type": "partial_chain", "lhs": {"_type": "partial_chain", "lhs": {"_type": "partial_chain", "lhs": {"_type": "constructor", "func": "make_chain_tt", "module": "combinators", "args": [{"_type": "constructor", "func": "make_select_column", "module": "transformations", "kwargs": {"key": "bill_length_mm", "TOA": "String"}}, {"_type": "constructor", "func": "make_split_dataframe", "module": "transformations", "kwargs": {"separator": ",", "col_names": {"_type": "list", "_items": ["species", "island", "bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g", "sex"]}}}]}, "rhs": {"_type": "constructor", "func": "then_cast_default", "module": "transformations", "kwargs": {"TOA": "f64"}}}, "rhs": {"_type": "constructor", "func": "then_clamp", "module": "transformations", "kwargs": {"bounds": [30.0, 65.0]}}}, "rhs": {"_type": "constructor", "func": "then_resize", "module": "transformations", "kwargs": {"size": 346, "constant": 43.61}}}, "rhs": {"_type": "constructor", "func": "then_variance", "module": "transformations"}}, "rhs": {"_type": "constructor", "func": "then_laplace", "module": "measurements", "kwargs": {"scale": 5.0}}}}', 'fixed_delta': 1e-05, 'dummy_nb_rows': 100, 'dummy_seed': 42}), user_name: str = Header(None)) JSONResponse[source]
Handles queries on dummy datasets for the OpenDP library.
- Parameters:
request (Request) – Raw request object.
query_json (OpenDPDummyQueryModel, optional) –
A JSON object containing the following: - opendp_pipeline: The OpenDP pipeline for the query. - fixed_delta: If the pipeline measurement is of type “ZeroConcentratedDivergence” (e.g. with “make_gaussian”) then
it is converted to “SmoothedMaxDivergence” with “make_zCDP_to_approxDP” (see opendp measurements documentation at https://docs.opendp.org/en/stable/api/python/opendp.combinators.html#opendp.combinators.make_zCDP_to_approxDP). # noqa # pylint: disable=C0301 In that case a “fixed_delta” must be provided by the user.
nb_rows (int, optional): The number of rows in the dummy dataset (default: 100).
seed (int, optional): The random seed for generating the dummy dataset (default: 42).
Defaults to Body(example_dummy_opendp).
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – If there is not enough budget or the dataset does not exist.
- Returns:
- A JSON object containing:
query_response (pd.DataFrame): a DataFrame containing the query response.
- Return type:
JSONResponse
- lomas_server.routes.routes_dp.dummy_smartnoise_sql_handler(request: Request, query_json: SmartnoiseSQLDummyQueryModel = Body({'query_str': 'SELECT COUNT(*) AS NB_ROW FROM df', 'dataset_name': 'PENGUIN', 'epsilon': 0.1, 'delta': 1e-05, 'mechanisms': {'count': 'gaussian'}, 'postprocess': True, 'dummy_nb_rows': 100, 'dummy_seed': 42}), user_name: str = Header(None)) JSONResponse[source]
Handles queries on dummy datasets for the SmartNoiseSQL library.
- Parameters:
request (Request) – Raw request object
query_json (DummySmartnoiseSQLModel, optional) –
A JSON object containing: - query: The SQL query to execute. NOTE: the table name is “df”,
the query must end with “FROM df”.
epsilon (float): Privacy parameter (e.g., 0.1).
delta (float): Privacy parameter (e.g., 1e-5).
mechanisms (dict, optional): Dictionary of mechanisms for the query (default: {}). See Smartnoise-SQL mechanisms documentation https://docs.smartnoise.org/sql/advanced.html#overriding-mechanisms.
postprocess (bool, optional): Whether to postprocess the query results (default: True). See Smartnoise-SQL postprocessing documentation https://docs.smartnoise.org/sql/advanced.html#postprocess.
nb_rows (int, optional): The number of rows in the dummy dataset (default: 100).
seed (int, optional): The random seed for generating the dummy dataset (default: 42).
Defaults to Body(example_dummy_smartnoise_sql).
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – If there is not enough budget or the dataset does not exist.
- Returns:
- A JSON object containing:
query_response (pd.DataFrame): a DataFrame containing the query response.
- Return type:
JSONResponse
- lomas_server.routes.routes_dp.dummy_smartnoise_synth_handler(request: ~starlette.requests.Request, query_json: ~lomas_server.utils.query_models.SmartnoiseSynthDummyQueryModel = Body({'dataset_name': 'PENGUIN', 'synth_name': <SSynthGanSynthesizer.DP_CTGAN: 'dpctgan'>, 'epsilon': 0.1, 'delta': 1e-05, 'select_cols': [], 'synth_params': {'embedding_dim': 128, 'batch_size': 50, 'epochs': 5}, 'nullable': True, 'constraints': '', 'return_model': True, 'condition': '', 'nb_samples': 200, 'dummy_nb_rows': 100, 'dummy_seed': 42}), user_name: str = Header(None)) JSONResponse[source]
Handles queries for the SmartNoise Synth library. :param request: Raw request object :type request: Request :param query_json: A JSON object containing:
synth_name (str): name of the Synthesizer model to use.
epsilon (float): Privacy parameter (e.g., 0.1).
- delta (float): Privacy parameter (e.g., 1e-5).
mechanisms (dict[str, str], optional): Dictionary of mechanisms for the query See Smartnoise-SQL postprocessing documentation.
select_cols (List[str]): List of columns to select.
- synth_params (dict): Keyword arguments to pass to the synthesizer
constructor. See https://docs.smartnoise.org/synth/synthesizers/index.html#, provide all parameters of the model except epsilon and delta.
nullable (bool): True if some data cells may be null
- constraints (dict): Dictionnary for custom table transformer constraints.
Column that are not specified will be inferred based on metadata.
return_model (bool): True to get Synthesizer model, False to get samples
- condition (Optional[str]): sampling condition in model.sample
(only relevant if return_model is False)
- nb_samples (Optional[int]): number of samples to generate.
(only relevant if return_model is False)
nb_rows (int, optional): The number of rows in the dummy dataset (default: 100).
seed (int, optional): The random seed for generating the dummy dataset (default: 42).
Defaults to Body(example_smartnoise_synth).
- Parameters:
user_name (str) – The user name.
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – If there is not enough budget or the dataset does not exist.
UnauthorizedAccessException – A query is already ongoing for this user, the user does not exist or does not have access to the dataset.
- Returns:
- A JSON object containing the following:
requested_by (str): The user name.
query_response (pd.DataFrame): A DataFrame containing the query response.
spent_epsilon (float): The amount of epsilon budget spent for the query.
spent_delta (float): The amount of delta budget spent for the query.
- Return type:
JSONResponse
- lomas_server.routes.routes_dp.estimate_diffprivlib_cost(request: Request, query_json: DiffPrivLibRequestModel = Body({'dataset_name': 'PENGUIN', 'diffprivlib_json': '{"module": "diffprivlib", "version": "0.6.4", "pipeline": [{"type": "_dpl_type:StandardScaler", "name": "scaler", "params": {"with_mean": true, "with_std": true, "copy": true, "epsilon": 0.5, "bounds": {"_tuple": true, "_items": [[30.0, 13.0, 150.0, 2000.0], [65.0, 23.0, 250.0, 7000.0]]}, "random_state": null, "accountant": "_dpl_instance:BudgetAccountant"}}, {"type": "_dpl_type:LogisticRegression", "name": "classifier", "params": {"tol": 0.0001, "C": 1.0, "fit_intercept": true, "random_state": null, "max_iter": 100, "verbose": 0, "warm_start": false, "n_jobs": null, "epsilon": 1.0, "data_norm": 83.69469642643347, "accountant": "_dpl_instance:BudgetAccountant"}}]}', 'feature_columns': ['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g'], 'target_columns': ['species'], 'test_size': 0.2, 'test_train_split_seed': 4, 'imputer_strategy': 'drop'}), user_name: str = Header(None))[source]
Estimates the privacy loss budget cost of an DiffPrivLib query.
- Parameters:
request (Request) – Raw request object
query_json (DiffPrivLibRequestModel, optional)
following (A JSON object containing the) –
pipeline: The DiffPrivLib pipeline for the query.
feature_columns: the list of feature column to train
target_columns: the list of target column to predict
test_size: proportion of the test set
test_train_split_seed: seed for the random train test split,
imputer_strategy: imputation strategy
Defaults to Body(example_dummy_diffprivlib).
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – The dataset does not exist or the pipeline does not contain a measurement.
- Returns:
- A JSON object containing:
epsilon_cost (float): The estimated epsilon cost.
delta_cost (float): The estimated delta cost.
- Return type:
JSONResponse
- lomas_server.routes.routes_dp.estimate_opendp_cost(request: Request, query_json: OpenDPRequestModel = Body({'dataset_name': 'PENGUIN', 'opendp_json': '{"version": "0.10.0", "ast": {"_type": "partial_chain", "lhs": {"_type": "partial_chain", "lhs": {"_type": "partial_chain", "lhs": {"_type": "partial_chain", "lhs": {"_type": "partial_chain", "lhs": {"_type": "constructor", "func": "make_chain_tt", "module": "combinators", "args": [{"_type": "constructor", "func": "make_select_column", "module": "transformations", "kwargs": {"key": "bill_length_mm", "TOA": "String"}}, {"_type": "constructor", "func": "make_split_dataframe", "module": "transformations", "kwargs": {"separator": ",", "col_names": {"_type": "list", "_items": ["species", "island", "bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g", "sex"]}}}]}, "rhs": {"_type": "constructor", "func": "then_cast_default", "module": "transformations", "kwargs": {"TOA": "f64"}}}, "rhs": {"_type": "constructor", "func": "then_clamp", "module": "transformations", "kwargs": {"bounds": [30.0, 65.0]}}}, "rhs": {"_type": "constructor", "func": "then_resize", "module": "transformations", "kwargs": {"size": 346, "constant": 43.61}}}, "rhs": {"_type": "constructor", "func": "then_variance", "module": "transformations"}}, "rhs": {"_type": "constructor", "func": "then_laplace", "module": "measurements", "kwargs": {"scale": 5.0}}}}', 'fixed_delta': 1e-05}), user_name: str = Header(None)) JSONResponse[source]
Estimates the privacy loss budget cost of an OpenDP query.
- Parameters:
request (Request) – Raw request object
query_json (OpenDPRequestModel, optional) –
A JSON object containing the following: - “opendp_pipeline”: The OpenDP pipeline for the query.
Defaults to Body(example_opendp).
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – The dataset does not exist or the pipeline does not contain a measurement.
- Returns:
- A JSON object containing:
epsilon_cost (float): The estimated epsilon cost.
delta_cost (float): The estimated delta cost.
- Return type:
JSONResponse
- lomas_server.routes.routes_dp.estimate_smartnoise_sql_cost(request: Request, query_json: SmartnoiseSQLRequestModel = Body({'query_str': 'SELECT COUNT(*) AS NB_ROW FROM df', 'dataset_name': 'PENGUIN', 'epsilon': 0.1, 'delta': 1e-05, 'mechanisms': {'count': 'gaussian'}}), user_name: str = Header(None)) JSONResponse[source]
Estimates the privacy loss budget cost of a SmartNoiseSQL query.
- Parameters:
request (Request) – Raw request object
query_json (SmartnoiseSQLRequestModel, optional) –
A JSON object containing the following: - query: The SQL query to estimate the cost for.
NOTE: the table name is “df”, the query must end with “FROM df”.
epsilon (float): Privacy parameter (e.g., 0.1).
delta (float): Privacy parameter (e.g., 1e-5).
mechanisms (dict, optional): Dictionary of mechanisms for the query (default: {}). See Smartnoise-SQL mechanisms documentation https://docs.smartnoise.org/sql/advanced.html#overriding-mechanisms.
Defaults to Body(example_smartnoise_sql_cost).
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – The dataset does not exist.
- Returns:
- A JSON object containing:
epsilon_cost (float): The estimated epsilon cost.
delta_cost (float): The estimated delta cost.
- Return type:
JSONResponse
- lomas_server.routes.routes_dp.estimate_smartnoise_synth_cost(request: ~starlette.requests.Request, query_json: ~lomas_server.utils.query_models.SmartnoiseSynthRequestModel = Body({'dataset_name': 'PENGUIN', 'synth_name': <SSynthGanSynthesizer.DP_CTGAN: 'dpctgan'>, 'epsilon': 0.1, 'delta': 1e-05, 'select_cols': [], 'synth_params': {'embedding_dim': 128, 'batch_size': 50, 'epochs': 5}, 'nullable': True, 'constraints': ''}), user_name: str = Header(None)) JSONResponse[source]
Handles queries for the SmartNoise Synth library. :param request: Raw request object :type request: Request :param query_json: A JSON object containing:
synth_name (str): name of the Synthesizer model to use.
epsilon (float): Privacy parameter (e.g., 0.1).
- delta (float): Privacy parameter (e.g., 1e-5).
mechanisms (dict[str, str], optional): Dictionary of mechanisms for the query See Smartnoise-SQL postprocessing documentation.
select_cols (List[str]): List of columns to select.
- synth_params (dict): Keyword arguments to pass to the synthesizer
constructor. See https://docs.smartnoise.org/synth/synthesizers/index.html#, provide all parameters of the model except epsilon and delta.
nullable (bool): True if some data cells may be null
constraints
nb_rows (int, optional): The number of rows in the dummy dataset
- seed (int, optional): The random seed for generating
the dummy dataset (default: 42).
Defaults to Body(example_smartnoise_synth).
- Parameters:
user_name (str) – The user name.
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – If there is not enough budget or the dataset does not exist.
UnauthorizedAccessException – A query is already ongoing for this user, the user does not exist or does not have access to the dataset.
- Returns:
- A JSON object containing:
epsilon_cost (float): The estimated epsilon cost.
delta_cost (float): The estimated delta cost.
- Return type:
JSONResponse
- lomas_server.routes.routes_dp.opendp_query_handler(request: Request, query_json: OpenDPQueryModel = Body({'dataset_name': 'PENGUIN', 'opendp_json': '{"version": "0.10.0", "ast": {"_type": "partial_chain", "lhs": {"_type": "partial_chain", "lhs": {"_type": "partial_chain", "lhs": {"_type": "partial_chain", "lhs": {"_type": "partial_chain", "lhs": {"_type": "constructor", "func": "make_chain_tt", "module": "combinators", "args": [{"_type": "constructor", "func": "make_select_column", "module": "transformations", "kwargs": {"key": "bill_length_mm", "TOA": "String"}}, {"_type": "constructor", "func": "make_split_dataframe", "module": "transformations", "kwargs": {"separator": ",", "col_names": {"_type": "list", "_items": ["species", "island", "bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g", "sex"]}}}]}, "rhs": {"_type": "constructor", "func": "then_cast_default", "module": "transformations", "kwargs": {"TOA": "f64"}}}, "rhs": {"_type": "constructor", "func": "then_clamp", "module": "transformations", "kwargs": {"bounds": [30.0, 65.0]}}}, "rhs": {"_type": "constructor", "func": "then_resize", "module": "transformations", "kwargs": {"size": 346, "constant": 43.61}}}, "rhs": {"_type": "constructor", "func": "then_variance", "module": "transformations"}}, "rhs": {"_type": "constructor", "func": "then_laplace", "module": "measurements", "kwargs": {"scale": 5.0}}}}', 'fixed_delta': 1e-05}), user_name: str = Header(None)) JSONResponse[source]
Handles queries for the OpenDP Library.
- Parameters:
request (Request) – Raw request object.
query_json (OpenDPQueryModel, optional) –
A JSON object containing the following: - opendp_pipeline: The OpenDP pipeline for the query. - fixed_delta: If the pipeline measurement is of type
”ZeroConcentratedDivergence” (e.g. with “make_gaussian”) then it is converted to “SmoothedMaxDivergence” with “make_zCDP_to_approxDP” (see “opendp measurements documentation at https://docs.opendp.org/en/stable/api/python/opendp.combinators.html#opendp.combinators.make_zCDP_to_approxDP). # noqa # pylint: disable=C0301 In that case a “fixed_delta” must be provided by the user.
Defaults to Body(example_opendp).
user_name (str, optional) – The user name. Defaults to Header(None).
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – The pipeline does not contain a “measurement”, there is not enough budget or the dataset does not exist.
UnauthorizedAccessException – A query is already ongoing for this user, the user does not exist or does not have access to the dataset.
- Returns:
- A JSON object containing the following:
requested_by (str): The user name.
query_response (pd.DataFrame): A DataFrame containing the query response.
spent_epsilon (float): The amount of epsilon budget spent for the query.
spent_delta (float): The amount of delta budget spent for the query.
- Return type:
JSONResponse
- lomas_server.routes.routes_dp.smartnoise_sql_handler(request: Request, query_json: SmartnoiseSQLQueryModel = Body({'query_str': 'SELECT COUNT(*) AS NB_ROW FROM df', 'dataset_name': 'PENGUIN', 'epsilon': 0.1, 'delta': 1e-05, 'mechanisms': {'count': 'gaussian'}, 'postprocess': True}), user_name: str = Header(None)) JSONResponse[source]
Handles queries for the SmartNoiseSQL library.
- Parameters:
request (Request) – Raw request object
query_json (SmartnoiseSQLModel) –
A JSON object containing: - query: The SQL query to execute. NOTE: the table name is “df”,
the query must end with “FROM df”.
epsilon (float): Privacy parameter (e.g., 0.1).
delta (float): Privacy parameter (e.g., 1e-5).
mechanisms (dict, optional): Dictionary of mechanisms for the query (default: {}). See “Smartnoise-SQL mechanisms documentation https://docs.smartnoise.org/sql/advanced.html#overriding-mechanisms.
postprocess (bool, optional): Whether to postprocess the query results (default: True). See “Smartnoise-SQL postprocessing documentation https://docs.smartnoise.org/sql/advanced.html#postprocess.
Defaults to Body(example_smartnoise_sql).
user_name (str, optional) – The user name. Defaults to Header(None).
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – If there is not enough budget or the dataset does not exist.
UnauthorizedAccessException – A query is already ongoing for this user, the user does not exist or does not have access to the dataset.
- Returns:
- A JSON object containing the following:
requested_by (str): The user name.
query_response (pd.DataFrame): A DataFrame containing the query response.
spent_epsilon (float): The amount of epsilon budget spent for the query.
spent_delta (float): The amount of delta budget spent for the query.
- Return type:
JSONResponse
- lomas_server.routes.routes_dp.smartnoise_synth_handler(request: ~starlette.requests.Request, query_json: ~lomas_server.utils.query_models.SmartnoiseSynthQueryModel = Body({'dataset_name': 'PENGUIN', 'synth_name': <SSynthGanSynthesizer.DP_CTGAN: 'dpctgan'>, 'epsilon': 0.1, 'delta': 1e-05, 'select_cols': [], 'synth_params': {'embedding_dim': 128, 'batch_size': 50, 'epochs': 5}, 'nullable': True, 'constraints': '', 'return_model': True, 'condition': '', 'nb_samples': 200}), user_name: str = Header(None)) JSONResponse[source]
Handles queries for the SmartNoise Synth library. :param request: Raw request object :type request: Request :param query_json: A JSON object containing:
synth_name (str): name of the Synthesizer model to use.
epsilon (float): Privacy parameter (e.g., 0.1).
- delta (float): Privacy parameter (e.g., 1e-5).
mechanisms (dict[str, str], optional): Dictionary of mechanisms for the query See Smartnoise-SQL postprocessing documentation.
select_cols (List[str]): List of columns to select.
- synth_params (dict): Keyword arguments to pass to the synthesizer
constructor. See https://docs.smartnoise.org/synth/synthesizers/index.html#, provide all parameters of the model except epsilon and delta.
nullable (bool): True if some data cells may be null
- constraints (dict): Dictionnary for custom table transformer constraints.
Column that are not specified will be inferred based on metadata.
return_model (bool): True to get Synthesizer model, False to get samples
- condition (Optional[str]): sampling condition in model.sample
(only relevant if return_model is False)
- nb_samples (Optional[int]): number of samples to generate.
(only relevant if return_model is False)
Defaults to Body(example_smartnoise_synth).
- Parameters:
user_name (str) – The user name.
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – If there is not enough budget or the dataset does not exist.
UnauthorizedAccessException – A query is already ongoing for this user, the user does not exist or does not have access to the dataset.
- Returns:
- A JSON object containing the following:
requested_by (str): The user name.
query_response (pd.DataFrame): A DataFrame containing the query response.
spent_epsilon (float): The amount of epsilon budget spent for the query.
spent_delta (float): The amount of delta budget spent for the query.
- Return type:
JSONResponse
lomas_server.routes.utils module
- lomas_server.routes.utils.handle_cost_query(request: Request, query_json: RequestModel, user_name: str, dp_library: DPLibraries)[source]
Handles cost queries for DP libraries.
- Parameters:
request (Request) – Raw request object
query_json (BaseModel) – A JSON object containing the user request
user_name (str) – The user name
dp_library – Name of the DP library to use for the query
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – The dataset does not exist.
- Returns:
- A JSON object containing:
epsilon_cost (float): The estimated epsilon cost.
delta_cost (float): The estimated delta cost.
- Return type:
JSONResponse
- lomas_server.routes.utils.handle_query_on_dummy_dataset(request: Request, query_json: DummyQueryModel, user_name: str, dp_library: DPLibraries)[source]
Handles queries for the SmartNoiseSQL library.
- Parameters:
request (Request) – Raw request object
query_json (BaseModel) – A JSON object containing the user request
user_name (str) – The user name
dp_library – Name of the DP library to use for the query
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – If there is not enough budget or the dataset does not exist.
- Returns:
A JSON object containing the query response.
- Return type:
JSONResponse
- lomas_server.routes.utils.handle_query_on_private_dataset(request: Request, query_json: QueryModel, user_name: str, dp_library: DPLibraries)[source]
Handles queries for the SmartNoiseSQL library.
- Parameters:
request (Request) – Raw request object
query_json (BaseModel) – A JSON object containing the user request
user_name (str) – The user name
dp_library – Name of the DP library to use for the query
- Raises:
ExternalLibraryException – For exceptions from libraries external to this package.
InternalServerException – For any other unforseen exceptions.
InvalidQueryException – If there is not enough budget or the dataset does not exist.
UnauthorizedAccessException – A query is already ongoing for this user, the user does not exist or does not have access to the dataset.
- Returns:
- A JSON object containing the following:
requested_by (str): The user name.
query_response (pd.DataFrame): A DataFrame containing the query response.
spent_epsilon (float): The amount of epsilon budget spent for the query.
spent_delta (float): The amount of delta budget spent for the query.
- Return type:
JSONResponse