concrete.ml.sklearn.linear_model

module concrete.ml.sklearn.linear_model

Implement sklearn linear model.

---

class LinearRegression

A linear regression model with FHE.

Arguments:

• n_bits (int): default is 2.

• use_sum_workaround (bool): indicate if the sum workaround should be used or not. This

• feature is experimental and should be used carefully. Important note: it only works for a LinearRegression model with N features, N a power of 2, for now. More information available in the QuantizedReduceSum operator. Default to False.

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=2,
    use_sum_workaround=False,
    fit_intercept=True,
    normalize='deprecated',
    copy_X=True,
    n_jobs=None,
    positive=False
)

---

property fhe_circuit

Get the FHE circuit.

Returns:

• Circuit: the FHE circuit

---

property input_quantizers

Get the input quantizers.

Returns:

• List[QuantizedArray]: the input quantizers

---

property onnx_model

Get the ONNX model.

.. # noqa: DAR201

Returns:

• onnx.ModelProto: the ONNX model

---

property output_quantizers

Get the input quantizers.

Returns:

• List[QuantizedArray]: the input quantizers

---

property quantize_input

Get the input quantization function.

Returns:

• Callable : function that quantizes the input

---

method fit

fit(X, y: ndarray, *args, **kwargs) → Any

Fit the FHE linear model.

Args:

• X : training data By default, you should be able to pass: * numpy arrays * torch tensors * pandas DataFrame or Series

• y (numpy.ndarray): The target data.

• *args: The arguments to pass to the sklearn linear model.

• **kwargs: The keyword arguments to pass to the sklearn linear model.

Returns: Any

---

class ElasticNet

An ElasticNet regression model with FHE.

Arguments:

• n_bits (int): default is 2.

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=2,
    alpha=1.0,
    l1_ratio=0.5,
    fit_intercept=True,
    normalize='deprecated',
    copy_X=True,
    positive=False
)

---

property fhe_circuit

Get the FHE circuit.

Returns:

• Circuit: the FHE circuit

---

property input_quantizers

Get the input quantizers.

Returns:

• List[QuantizedArray]: the input quantizers

---

property onnx_model

Get the ONNX model.

.. # noqa: DAR201

Returns:

• onnx.ModelProto: the ONNX model

---

property output_quantizers

Get the input quantizers.

Returns:

• List[QuantizedArray]: the input quantizers

---

property quantize_input

Get the input quantization function.

Returns:

• Callable : function that quantizes the input

---

class Lasso

A Lasso regression model with FHE.

Arguments:

• n_bits (int): default is 2.

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=2,
    alpha: float = 1.0,
    fit_intercept=True,
    normalize='deprecated',
    copy_X=True,
    positive=False
)

---

property fhe_circuit

Get the FHE circuit.

Returns:

• Circuit: the FHE circuit

---

property input_quantizers

Get the input quantizers.

Returns:

• List[QuantizedArray]: the input quantizers

---

property onnx_model

Get the ONNX model.

.. # noqa: DAR201

Returns:

• onnx.ModelProto: the ONNX model

---

property output_quantizers

Get the input quantizers.

Returns:

• List[QuantizedArray]: the input quantizers

---

property quantize_input

Get the input quantization function.

Returns:

• Callable : function that quantizes the input

---

class Ridge

A Ridge regression model with FHE.

Arguments:

• n_bits (int): default is 2.

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=2,
    alpha: float = 1.0,
    fit_intercept=True,
    normalize='deprecated',
    copy_X=True,
    positive=False
)

---

property fhe_circuit

Get the FHE circuit.

Returns:

• Circuit: the FHE circuit

---

property input_quantizers

Get the input quantizers.

Returns:

• List[QuantizedArray]: the input quantizers

---

property onnx_model

Get the ONNX model.

.. # noqa: DAR201

Returns:

• onnx.ModelProto: the ONNX model

---

property output_quantizers

Get the input quantizers.

Returns:

• List[QuantizedArray]: the input quantizers

---

property quantize_input

Get the input quantization function.

Returns:

• Callable : function that quantizes the input

---

class LogisticRegression

A logistic regression model with FHE.

Arguments:

• n_bits (int): default is 2.

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=2,
    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
)

---

property fhe_circuit

Get the FHE circuit.

Returns:

• Circuit: the FHE circuit

---

property input_quantizers

Get the input quantizers.

Returns:

• List[QuantizedArray]: the input quantizers

---

property onnx_model

Get the ONNX model.

.. # noqa: DAR201

Returns:

• onnx.ModelProto: the ONNX model

---

property output_quantizers

Get the input quantizers.

Returns:

• List[QuantizedArray]: the input quantizers

---

property quantize_input

Get the input quantization function.

Returns:

• Callable : function that quantizes the input