zeekay
397da37894
Files edited: 150 Brand-hit files before: 163 Brand-hit files after: 28 Substitutions: - Zama Concrete ML -> TorusML - Zama Concrete / Concrete-> Torus - Zama fhEVM / fhEVM -> TorusEVM - Zama TFHE-rs / tfhe-rs -> Lux-FHE (github.com/luxfi/fhe) - Zama (attribution) -> Lux Industries - zama-ai/* (GitHub) -> luxfhe/* - @zama-fhe/* (npm) -> @luxfhe/* - docs.zama.ai -> docs.lux.network - Fhenix / CoFHE -> dropped or rewritten to Lux FHE / TorusEVM coprocessor Text-only changes. No runtime code semantics altered.
4.6 KiB
module concrete.ml.sklearn.neighbors
Implement sklearn neighbors model.
class KNeighborsClassifier
A k-nearest neighbors classifier model with FHE.
Parameters:
n_bits(int): Number of bits to quantize the model. The value will be used for quantizing inputs and X_fit. Default to 3.
For more details on KNeighborsClassifier please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
method __init__
__init__(
n_bits=2,
n_neighbors=3,
weights='uniform',
algorithm='auto',
leaf_size=30,
p=2,
metric='minkowski',
metric_params=None,
n_jobs=None
)
property fhe_circuit
Get the FHE circuit.
The FHE circuit combines computational graph, mlir, client and server into a single object. More information available in Concrete documentation (https://docs.lux.network/torus/get-started/terminology) Is None if the model is not fitted.
Returns:
Circuit: The FHE circuit.
property is_compiled
Indicate if the model is compiled.
Returns:
bool: If the model is compiled.
property is_fitted
Indicate if the model is fitted.
Returns:
bool: If the model is fitted.
property onnx_model
Get the ONNX model.
Is None if the model is not fitted.
Returns:
onnx.ModelProto: The ONNX model.
method dump_dict
dump_dict() → Dict[str, Any]
method kneighbors
kneighbors(X: Union[ndarray, Tensor, ForwardRef('DataFrame'), List]) → ndarray
Return the knearest distances and their respective indices for each query point.
Args:
X(Data): The input values to predict, as a Numpy array, Torch tensor, Pandas DataFrame or List.
Raises:
NotImplementedError: The method is not implemented for now.
classmethod load_dict
load_dict(metadata: Dict)
method predict_proba
predict_proba(
X: Union[ndarray, Tensor, ForwardRef('DataFrame'), List],
fhe: Union[FheMode, str] = <FheMode.DISABLE: 'disable'>
) → ndarray
Predict class probabilities.
Args:
X(Data): The input values to predict, as a Numpy array, Torch tensor, Pandas DataFrame or List.fhe(Union[FheMode, str]): The mode to use for prediction. Can be FheMode.DISABLE for TorusML Python inference, FheMode.SIMULATE for FHE simulation and FheMode.EXECUTE for actual FHE execution. Can also be the string representation of any of these values. Default to FheMode.DISABLE.
Raises:
NotImplementedError: The method is not implemented for now.