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module concrete.ml.pytest.utils

Common functions or lists for test files, which can't be put in fixtures.

Global Variables

  • MODELS_AND_DATASETS
  • UNIQUE_MODELS_AND_DATASETS

function get_sklearn_linear_models_and_datasets

get_sklearn_linear_models_and_datasets(
    regressor: bool = True,
    classifier: bool = True,
    unique_models: bool = False,
    select: Optional[str, List[str]] = None,
    ignore: Optional[str, List[str]] = None
) → List

Get the pytest parameters to use for testing linear models.

Args:

  • regressor (bool): If regressors should be selected.
  • classifier (bool): If classifiers should be selected.
  • unique_models (bool): If each models should be represented only once.
  • select (Optional[Union[str, List[str]]]): If not None, only return models which names (or a part of it) match the given string or list of strings. Default to None.
  • ignore (Optional[Union[str, List[str]]]): If not None, only return models which names (or a part of it) do not match the given string or list of strings. Default to None.

Returns:

  • List: The pytest parameters to use for testing linear models.

function get_sklearn_tree_models_and_datasets

get_sklearn_tree_models_and_datasets(
    regressor: bool = True,
    classifier: bool = True,
    unique_models: bool = False,
    select: Optional[str, List[str]] = None,
    ignore: Optional[str, List[str]] = None
) → List

Get the pytest parameters to use for testing tree-based models.

Args:

  • regressor (bool): If regressors should be selected.
  • classifier (bool): If classifiers should be selected.
  • unique_models (bool): If each models should be represented only once.
  • select (Optional[Union[str, List[str]]]): If not None, only return models which names (or a part of it) match the given string or list of strings. Default to None.
  • ignore (Optional[Union[str, List[str]]]): If not None, only return models which names (or a part of it) do not match the given string or list of strings. Default to None.

Returns:

  • List: The pytest parameters to use for testing tree-based models.

function get_sklearn_neural_net_models_and_datasets

get_sklearn_neural_net_models_and_datasets(
    regressor: bool = True,
    classifier: bool = True,
    unique_models: bool = False,
    select: Optional[str, List[str]] = None,
    ignore: Optional[str, List[str]] = None
) → List

Get the pytest parameters to use for testing neural network models.

Args:

  • regressor (bool): If regressors should be selected.
  • classifier (bool): If classifiers should be selected.
  • unique_models (bool): If each models should be represented only once.
  • select (Optional[Union[str, List[str]]]): If not None, only return models which names (or a part of it) match the given string or list of strings. Default to None.
  • ignore (Optional[Union[str, List[str]]]): If not None, only return models which names (or a part of it) do not match the given string or list of strings. Default to None.

Returns:

  • List: The pytest parameters to use for testing neural network models.

function get_sklearn_neighbors_models_and_datasets

get_sklearn_neighbors_models_and_datasets(
    regressor: bool = True,
    classifier: bool = True,
    unique_models: bool = False,
    select: Optional[str, List[str]] = None,
    ignore: Optional[str, List[str]] = None
) → List

Get the pytest parameters to use for testing neighbor models.

Args:

  • regressor (bool): If regressors should be selected.
  • classifier (bool): If classifiers should be selected.
  • unique_models (bool): If each models should be represented only once.
  • select (Optional[Union[str, List[str]]]): If not None, only return models which names (or a part of it) match the given string or list of strings. Default to None.
  • ignore (Optional[Union[str, List[str]]]): If not None, only return models which names (or a part of it) do not match the given string or list of strings. Default to None.

Returns:

  • List: The pytest parameters to use for testing neighbor models.

function get_sklearn_all_models_and_datasets

get_sklearn_all_models_and_datasets(
    regressor: bool = True,
    classifier: bool = True,
    unique_models: bool = False,
    select: Optional[str, List[str]] = None,
    ignore: Optional[str, List[str]] = None
) → List

Get the pytest parameters to use for testing all models available in Concrete ML.

Args:

  • regressor (bool): If regressors should be selected.
  • classifier (bool): If classifiers should be selected.
  • unique_models (bool): If each models should be represented only once.
  • select (Optional[Union[str, List[str]]]): If not None, only return models which names (or a part of it) match the given string or list of strings. Default to None.
  • ignore (Optional[Union[str, List[str]]]): If not None, only return models which names (or a part of it) do not match the given string or list of strings. Default to None.

Returns:

  • List: The pytest parameters to use for testing all models available in Concrete ML.

function instantiate_model_generic

instantiate_model_generic(model_class, n_bits, **parameters)

Instantiate any Concrete ML model type.

Args:

  • model_class (class): The type of the model to instantiate.
  • n_bits (int): The number of quantization to use when initializing the model. For QNNs, default parameters are used based on whether n_bits is greater or smaller than 8.
  • parameters (dict): Hyper-parameters for the model instantiation. For QNNs, these parameters will override the matching default ones.

Returns:

  • model_name (str): The type of the model as a string.
  • model (object): The model instance.

function data_calibration_processing

data_calibration_processing(data, n_sample: int, targets=None)

Reduce size of the given data-set.

Args:

  • data: The input container to consider
  • n_sample (int): Number of samples to keep if the given data-set
  • targets: If dataset is a torch.utils.data.Dataset, it typically contains both the data and the corresponding targets. In this case, targets must be set to None. If data is instance of torch.Tensor or 'numpy.ndarray, targets` is expected.

Returns:

  • Tuple[numpy.ndarray, numpy.ndarray]: The input data and the target (respectively x and y).

Raises:

  • TypeError: If the 'data-set' does not match any expected type.

function load_torch_model

load_torch_model(
    model_class: Module,
    state_dict_or_path: Optional[str, Path, Dict[str, Any]],
    params: Dict,
    device: str = 'cpu'
) → Module

Load an object saved with torch.save() from a file or dict.

Args:

  • model_class (torch.nn.Module): A PyTorch or Brevitas network.
  • state_dict_or_path (Optional[Union[str, Path, Dict[str, Any]]]): Path or state_dict
  • params (Dict): Model's parameters
  • device (str): Device type.

Returns:

  • torch.nn.Module: A PyTorch or Brevitas network.

function values_are_equal

values_are_equal(value_1: Any, value_2: Any) → bool

Indicate if two values are equal.

This method takes into account objects of type None, numpy.ndarray, numpy.floating, numpy.integer, numpy.random.RandomState or any instance that provides a __eq__ method.

Args:

  • value_2 (Any): The first value to consider.
  • value_1 (Any): The second value to consider.

Returns:

  • bool: If the two values are equal.

function check_serialization

check_serialization(
    object_to_serialize: Any,
    expected_type: Type,
    equal_method: Optional[Callable] = None,
    check_str: bool = True
)

Check that the given object can properly be serialized.

This function serializes all objects using the dump, dumps, load and loads functions from Concrete ML. If the given object provides a dump and dumps method, they are also serialized using these.

Args:

  • object_to_serialize (Any): The object to serialize.
  • expected_type (Type): The object's expected type.
  • equal_method (Optional[Callable]): The function to use to compare the two loaded objects. Default to values_are_equal.
  • check_str (bool): If the JSON strings should also be checked. Default to True.

function get_random_samples

get_random_samples(x: ndarray, n_sample: int) → ndarray

Select n_sample random elements from a 2D NumPy array.

Args:

  • x (numpy.ndarray): The 2D NumPy array from which random rows will be selected.
  • n_sample (int): The number of rows to randomly select.

Returns:

  • numpy.ndarray: A new 2D NumPy array containing the randomly selected rows.

Raises:

  • AssertionError: If n_sample is not within the range (0, x.shape[0]) or if x is not a 2D array.

function pandas_dataframe_are_equal

pandas_dataframe_are_equal(
    df_1: DataFrame,
    df_2: DataFrame,
    float_rtol: float = 1e-05,
    float_atol: float = 1e-08,
    equal_nan: bool = False
)

Determine if both data-frames are identical.

Args:

  • df_1 (pandas.DataFrame): The first data-frame to consider.
  • df_2 (pandas.DataFrame): The second data-frame to consider.
  • float_rtol (float): Numpy's relative tolerance parameter to use when comparing columns with floating point values. Default to 1.e-5.
  • float_atol (float): Numpy's absolute tolerance parameter to use when comparing columns with floating point values. Default to 1.e-8.
  • equal_nan (bool): Whether to compare NaN values as equal. Default to False.

Returns:

  • Bool: Wether both data-frames are equal.