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.
13 KiB
module concrete.ml.sklearn.glm
Implement sklearn's Generalized Linear Models (GLM).
class PoissonRegressor
A Poisson regression model with FHE.
Parameters:
n_bits(int, Dict[str, int]): Number of bits to quantize the model. If an int is passed for n_bits, the value will be used for quantizing inputs and weights. If a dict is passed, then it should contain "op_inputs" and "op_weights" as keys with corresponding number of quantization bits so that: - op_inputs : number of bits to quantize the input values - op_weights: number of bits to quantize the learned parameters Default to 8.
For more details on PoissonRegressor please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PoissonRegressor.html
method __init__
__init__(
n_bits: 'Union[int, dict]' = 8,
alpha: 'float' = 1.0,
fit_intercept: 'bool' = True,
solver='lbfgs',
max_iter: 'int' = 100,
tol: 'float' = 0.0001,
warm_start: 'bool' = False,
verbose: 'int' = 0
)
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
method get_sklearn_params
get_sklearn_params(deep: 'bool' = True) → dict
classmethod load_dict
load_dict(metadata: 'Dict')
method post_processing
post_processing(y_preds: 'ndarray') → ndarray
method predict
predict(
X: 'Data',
fhe: 'Union[FheMode, str]' = <FheMode.DISABLE: 'disable'>
) → ndarray
class GammaRegressor
A Gamma regression model with FHE.
Parameters:
n_bits(int, Dict[str, int]): Number of bits to quantize the model. If an int is passed for n_bits, the value will be used for quantizing inputs and weights. If a dict is passed, then it should contain "op_inputs" and "op_weights" as keys with corresponding number of quantization bits so that: - op_inputs : number of bits to quantize the input values - op_weights: number of bits to quantize the learned parameters Default to 8.
For more details on GammaRegressor please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.GammaRegressor.html
method __init__
__init__(
n_bits: 'Union[int, dict]' = 8,
alpha: 'float' = 1.0,
fit_intercept: 'bool' = True,
solver='lbfgs',
max_iter: 'int' = 100,
tol: 'float' = 0.0001,
warm_start: 'bool' = False,
verbose: 'int' = 0
)
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
method get_sklearn_params
get_sklearn_params(deep: 'bool' = True) → dict
classmethod load_dict
load_dict(metadata: 'Dict')
method post_processing
post_processing(y_preds: 'ndarray') → ndarray
method predict
predict(
X: 'Data',
fhe: 'Union[FheMode, str]' = <FheMode.DISABLE: 'disable'>
) → ndarray
class TweedieRegressor
A Tweedie regression model with FHE.
Parameters:
n_bits(int, Dict[str, int]): Number of bits to quantize the model. If an int is passed for n_bits, the value will be used for quantizing inputs and weights. If a dict is passed, then it should contain "op_inputs" and "op_weights" as keys with corresponding number of quantization bits so that: - op_inputs : number of bits to quantize the input values - op_weights: number of bits to quantize the learned parameters Default to 8.
For more details on TweedieRegressor please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.TweedieRegressor.html
method __init__
__init__(
n_bits: 'Union[int, dict]' = 8,
power: 'float' = 0.0,
alpha: 'float' = 1.0,
fit_intercept: 'bool' = True,
link: 'str' = 'auto',
solver='lbfgs',
max_iter: 'int' = 100,
tol: 'float' = 0.0001,
warm_start: 'bool' = False,
verbose: 'int' = 0
)
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
method get_sklearn_params
get_sklearn_params(deep: 'bool' = True) → dict
classmethod load_dict
load_dict(metadata: 'Dict')
method post_processing
post_processing(y_preds: 'ndarray') → ndarray
method predict
predict(
X: 'Data',
fhe: 'Union[FheMode, str]' = <FheMode.DISABLE: 'disable'>
) → ndarray