mirror of https://github.com/luxfi/torus-ml.git synced 2026-10-30 15:15:23 +00:00

Files

T

zeekay e037e8f7ea torus: one name for the package, and it is ours

The distribution called itself torus-ml while shipping a package directory
named concrete, so the name a user installs and the name they import disagreed
— and most of the tree already said torus.ml, which could not resolve against
src/concrete. Moved src/concrete to src/torus and pointed the include at it.

The dependency was Zama's concrete-python from their index. It is torus-fhe
from ours, which is what luxfhe/torus already ships: that frontend declares
torus-fhe and ships a torus/fhe module, so every 'from torus.fhe' in this tree
was already naming our compiler and only the dependency lagged.

Two distributions share one namespace, which is the shape the upstream this
forked from used and the reason the split works: torus-fhe gives torus.fhe,
torus-ml gives torus.ml.

Swept every other spelling — identifiers, enum members, workflow variables,
filenames, notebooks. LICENSE and NOTICE keep the upstream attribution a fork
owes; the figures carry the old name in JPEG metadata a text sweep cannot
reach.

2026-08-27 13:20:50 -07:00

13 KiB

Raw Permalink Blame History

`module` `torus.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 Torus 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 Torus ML.

`function` `instantiate_model_generic`

instantiate_model_generic(model_class, n_bits, **parameters)

Instantiate any Torus 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 Torus 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.

13 KiB Raw Permalink Blame History

module torus.ml.pytest.utils

Global Variables

function get_sklearn_linear_models_and_datasets

function get_sklearn_tree_models_and_datasets

function get_sklearn_neural_net_models_and_datasets

function get_sklearn_neighbors_models_and_datasets

function get_sklearn_all_models_and_datasets

function instantiate_model_generic

function data_calibration_processing

function load_torch_model

function values_are_equal

function check_serialization

function get_random_samples

function pandas_dataframe_are_equal

13 KiB

Raw Permalink Blame History

`module` `torus.ml.pytest.utils`

`function` `get_sklearn_linear_models_and_datasets`

`function` `get_sklearn_tree_models_and_datasets`

`function` `get_sklearn_neural_net_models_and_datasets`

`function` `get_sklearn_neighbors_models_and_datasets`

`function` `get_sklearn_all_models_and_datasets`

`function` `instantiate_model_generic`

`function` `data_calibration_processing`

`function` `load_torch_model`

`function` `values_are_equal`

`function` `check_serialization`

`function` `get_random_samples`

`function` `pandas_dataframe_are_equal`