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
)

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[str, Any]

classmethod `load_dict`

load_dict(metadata: Dict)

class `SGDRegressor`

An FHE linear regression model fitted with stochastic gradient descent.

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

method `init`

__init__(
    n_bits=8,
    loss='squared_error',
    penalty='l2',
    alpha=0.0001,
    l1_ratio=0.15,
    fit_intercept=True,
    max_iter=1000,
    tol=0.001,
    shuffle=True,
    verbose=0,
    epsilon=0.1,
    random_state=None,
    learning_rate='invscaling',
    eta0=0.01,
    power_t=0.25,
    early_stopping=False,
    validation_fraction=0.1,
    n_iter_no_change=5,
    warm_start=False,
    average=False
)

property fhe_circuit

Get the FHE circuit.

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[str, Any]

classmethod `load_dict`

load_dict(metadata: Dict)

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'
)

property fhe_circuit

Get the FHE circuit.

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[str, Any]

classmethod `load_dict`

load_dict(metadata: Dict)

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'
)

property fhe_circuit

Get the FHE circuit.

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[str, Any]

classmethod `load_dict`

load_dict(metadata: Dict)

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
)

property fhe_circuit

Get the FHE circuit.

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[str, Any]

classmethod `load_dict`

load_dict(metadata: Dict)

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
)

property fhe_circuit

Get the FHE circuit.

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 n_classes_

Get the model's number of classes.

Using this attribute is deprecated.

Returns:

int: The model's number of classes.

property onnx_model

Get the ONNX model.

Is None if the model is not fitted.

Returns:

onnx.ModelProto: The ONNX model.

property target_classes_

Get the model's classes.

Using this attribute is deprecated.

Returns:

Optional[numpy.ndarray]: The model's classes.

method `dump_dict`

dump_dict() → Dict[str, Any]

classmethod `load_dict`

load_dict(metadata: Dict)

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hashtagmodule concrete.ml.sklearn.linear_model

hashtagclass LinearRegression

hashtagmethod __init__

hashtagmethod dump_dict

hashtagclassmethod load_dict

hashtagclass SGDRegressor

hashtagmethod __init__

hashtagmethod dump_dict

hashtagclassmethod load_dict

hashtagclass ElasticNet

hashtagmethod __init__

hashtagmethod dump_dict

hashtagclassmethod load_dict

hashtagclass Lasso

hashtagmethod __init__

hashtagmethod dump_dict

hashtagclassmethod load_dict

hashtagclass Ridge

hashtagmethod __init__

hashtagmethod dump_dict

hashtagclassmethod load_dict

hashtagclass LogisticRegression

hashtagmethod __init__

hashtagmethod dump_dict

hashtagclassmethod load_dict

module `concrete.ml.sklearn.linear_model`

class `LinearRegression`

method `init`

method `dump_dict`

classmethod `load_dict`

class `SGDRegressor`

method `init`

method `dump_dict`

classmethod `load_dict`

class `ElasticNet`

method `init`

method `dump_dict`

classmethod `load_dict`

class `Lasso`

method `init`

method `dump_dict`

classmethod `load_dict`

class `Ridge`

method `init`

method `dump_dict`

classmethod `load_dict`

class `LogisticRegression`

method `init`

method `dump_dict`

classmethod `load_dict`