lomas_client package

Submodules

lomas_client.client module

class lomas_client.client.Client(url: str, user_name: str, dataset_name: str)

Bases: object

Client class to send requests to the server Handle all serialisation and deserialisation steps

estimate_opendp_cost(opendp_pipeline: Measurement, fixed_delta: float | None = None) → dict[str, float] | None

This function estimates the cost of executing an OpenDP query.

Parameters:

• opendp_pipeline (dp.Measurement) – The OpenDP pipeline for the query.

• fixed_delta (Optional[float], optional) – 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 Smartnoise-SQL postprocessing documentation.). In that case a fixed_delta must be provided by the user. Defaults to None.

Returns:

A dictionary containing the estimated cost.

Return type:

Optional[dict[str, float]]

estimate_smartnoise_cost(query: str, epsilon: float, delta: float, mechanisms: dict[str, str] = {}) → dict[str, float] | None

This function estimates the cost of executing a SmartNoise query.

Parameters:

• query (str) – 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[str, str], optional): Dictionary of mechanisms for the query See Smartnoise-SQL postprocessing documentation. Defaults to {}.

Returns:

A dictionary containing the estimated cost.

Return type:

Optional[dict[str, float]]

get_dataset_metadata() → Dict[str, int | bool | Dict[str, str | int]] | None

This function retrieves metadata for the dataset.

Returns:

A dictionary containing dataset metadata.

Return type:

Optional[Dict[str, Union[int, bool, Dict[str, Union[str, int]]]]]

get_dummy_dataset(nb_rows: int = 100, seed: int = 42) → DataFrame | None

This function retrieves a dummy dataset with optional parameters.

Parameters:

• nb_rows (int, optional) –

  The number of rows in the dummy dataset.

  Defaults to DUMMY_NB_ROWS.

• seed (int, optional) –

  The random seed for generating the dummy dataset.

  Defaults to DUMMY_SEED.

Returns:

A Pandas DataFrame representing the dummy dataset.

Return type:

Optional[pd.DataFrame]

get_initial_budget() → dict[str, float] | None

This function retrieves the initial budget.

Returns:

A dictionary containing the initial budget.

Return type:

Optional[dict[str, float]]

get_previous_queries() → List[dict] | None

This function retrieves the previous queries of the user.

Raises:

ValueError – If an unknown query type is encountered during deserialization.

Returns:

A list of dictionary containing the different queries on the private dataset.

Return type:

Optional[List[dict]]

get_remaining_budget() → dict[str, float] | None

This function retrieves the remaining budget.

Returns:

A dictionary containing the remaining budget.

Return type:

Optional[dict[str, float]]

get_total_spent_budget() → dict[str, float] | None

This function retrieves the total spent budget.

Returns:

A dictionary containing the total spent budget.

Return type:

Optional[dict[str, float]]

opendp_query(opendp_pipeline: Measurement, fixed_delta: float | None = None, dummy: bool = False, nb_rows: int = 100, seed: int = 42) → dict | None

This function executes an OpenDP query.

Parameters:

• opendp_pipeline (dp.Measurement) – The OpenDP pipeline for the query.

• fixed_delta (Optional[float], optional) – 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 Smartnoise-SQL postprocessing documentation.). In that case a fixed_delta must be provided by the user. Defaults to None.

• dummy (bool, optional) – Whether to use a dummy dataset. Defaults to False.

• nb_rows (int, optional) – The number of rows in the dummy dataset. Defaults to DUMMY_NB_ROWS.

• seed (int, optional) – The random seed for generating the dummy dataset. Defaults to DUMMY_SEED.

Raises:

Exception – If the server returns dataframes

Returns:

A Pandas DataFrame containing the query results.

Return type:

Optional[dict]

smartnoise_query(query: str, epsilon: float, delta: float, mechanisms: dict[str, str] = {}, postprocess: bool = True, dummy: bool = False, nb_rows: int = 100, seed: int = 42) → dict | None

This function executes a SmartNoise query.

Parameters:

• query (str) – 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[str, str], optional) –

  Dictionary of mechanisms for the query See Smartnoise-SQL postprocessing documentation.

  Defaults to {}.

• postprocess (bool, optional) –

  Whether to postprocess the query results. See Smartnoise-SQL postprocessing documentation.

  Defaults to True.

• dummy (bool, optional) –

  Whether to use a dummy dataset.

  Defaults to False.

• nb_rows (int, optional) –

  The number of rows in the dummy dataset.

  Defaults to DUMMY_NB_ROWS.

• seed (int, optional) –

  The random seed for generating the dummy dataset.

  Defaults to DUMMY_SEED.

Returns:

A Pandas DataFrame containing the query results.

Return type:

Optional[dict]

class lomas_client.client.DPLibraries(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: StrEnum

Enum of the DP librairies used in the server WARNING: MUST match those of lomas_server

OPENDP = 'opendp'

SMARTNOISE_SQL = 'smartnoise_sql'

lomas_client.client.error_message(res: Response) → str

Generates an error message based on the HTTP response.

Parameters:

res (requests.Response) – The response object from an HTTP request.

Returns:

A formatted string describing the server error,

including the status code and response text.

Return type:

str

lomas_client.utils module

Module contents