concrete.ml.sklearn.linear_model.md

module concrete.ml.sklearn.linear_model

Implement sklearn linear model.

---

class LinearRegression

A linear 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 LinearRegression please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html

method __init__

__init__(
    n_bits=8,
    fit_intercept=True,
    normalize='deprecated',
    copy_X=True,
    n_jobs=None,
    positive=False
)

---

class ElasticNet

An ElasticNet 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 ElasticNet please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html

method __init__

__init__(
    n_bits=8,
    alpha=1.0,
    l1_ratio=0.5,
    fit_intercept=True,
    normalize='deprecated',
    precompute=False,
    max_iter=1000,
    copy_X=True,
    tol=0.0001,
    warm_start=False,
    positive=False,
    random_state=None,
    selection='cyclic'
)

---

class Lasso

A Lasso 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 Lasso please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html

method __init__

__init__(
    n_bits=8,
    alpha: float = 1.0,
    fit_intercept=True,
    normalize='deprecated',
    precompute=False,
    copy_X=True,
    max_iter=1000,
    tol=0.0001,
    warm_start=False,
    positive=False,
    random_state=None,
    selection='cyclic'
)

---

class Ridge

A Ridge 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 Ridge please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html

method __init__

__init__(
    n_bits=8,
    alpha: float = 1.0,
    fit_intercept=True,
    normalize='deprecated',
    copy_X=True,
    max_iter=None,
    tol=0.001,
    solver='auto',
    positive=False,
    random_state=None
)

---

class LogisticRegression

A logistic 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 LogisticRegression please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html

method __init__

__init__(
    n_bits=8,
    penalty='l2',
    dual=False,
    tol=0.0001,
    C=1.0,
    fit_intercept=True,
    intercept_scaling=1,
    class_weight=None,
    random_state=None,
    solver='lbfgs',
    max_iter=100,
    multi_class='auto',
    verbose=0,
    warm_start=False,
    n_jobs=None,
    l1_ratio=None
)