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
)

---

method post_processing

post_processing(
    y_preds: 'ndarray',
    already_dequantized: 'bool' = False
) → ndarray

Post-processing the predictions.

Args:

• y_preds (numpy.ndarray): The predictions to post-process.

• already_dequantized (bool): Whether the inputs were already dequantized or not. Default to False.

Returns:

• numpy.ndarray: The post-processed predictions.

---

method predict

predict(X: 'ndarray', execute_in_fhe: 'bool' = False) → ndarray

Predict on user data.

Predict on user data using either the quantized clear model, implemented with tensors, or, if execute_in_fhe is set, using the compiled FHE circuit.

Args:

• X (numpy.ndarray): The input data.

• execute_in_fhe (bool): Whether to execute the inference in FHE. Default to False.

Returns:

• numpy.ndarray: The model's predictions.

---

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
)

---

method post_processing

post_processing(
    y_preds: 'ndarray',
    already_dequantized: 'bool' = False
) → ndarray

Post-processing the predictions.

Args:

• y_preds (numpy.ndarray): The predictions to post-process.

• already_dequantized (bool): Whether the inputs were already dequantized or not. Default to False.

Returns:

• numpy.ndarray: The post-processed predictions.

---

method predict

predict(X: 'ndarray', execute_in_fhe: 'bool' = False) → ndarray

Predict on user data.

Predict on user data using either the quantized clear model, implemented with tensors, or, if execute_in_fhe is set, using the compiled FHE circuit.

Args:

• X (numpy.ndarray): The input data.

• execute_in_fhe (bool): Whether to execute the inference in FHE. Default to False.

Returns:

• numpy.ndarray: The model's predictions.

---

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
)

---

method post_processing

post_processing(
    y_preds: 'ndarray',
    already_dequantized: 'bool' = False
) → ndarray

Post-processing the predictions.

Args:

• y_preds (numpy.ndarray): The predictions to post-process.

• already_dequantized (bool): Whether the inputs were already dequantized or not. Default to False.

Returns:

• numpy.ndarray: The post-processed predictions.

---

method predict

predict(X: 'ndarray', execute_in_fhe: 'bool' = False) → ndarray

Predict on user data.

Predict on user data using either the quantized clear model, implemented with tensors, or, if execute_in_fhe is set, using the compiled FHE circuit.

Args:

• X (numpy.ndarray): The input data.

• execute_in_fhe (bool): Whether to execute the inference in FHE. Default to False.

Returns:

• numpy.ndarray: The model's predictions.