concrete.ml.sklearn.glm.md

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,
    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.zama.ai/concrete/getting-started/terminology_and_structure) 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

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,
    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.zama.ai/concrete/getting-started/terminology_and_structure) 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

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',
    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.zama.ai/concrete/getting-started/terminology_and_structure) 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

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