concrete.ml.sklearn.glm.md
module concrete.ml.sklearn.glm
concrete.ml.sklearn.glm
Implement sklearn's Generalized Linear Models (GLM).
class PoissonRegressor
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__
__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
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
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
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__
__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
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
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
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__
__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
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
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.
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