concrete.ml.sklearn.svm.md

module concrete.ml.sklearn.svm

Implement Support Vector Machine.

---

class LinearSVR

A Regression Support Vector Machine (SVM).

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

method __init__

__init__(
    n_bits=8,
    epsilon=0.0,
    tol=0.0001,
    C=1.0,
    loss='epsilon_insensitive',
    fit_intercept=True,
    intercept_scaling=1.0,
    dual=True,
    verbose=0,
    random_state=None,
    max_iter=1000
)

---

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 LinearSVC

A Classification Support Vector Machine (SVM).

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

method __init__

__init__(
    n_bits=8,
    penalty='l2',
    loss='squared_hinge',
    dual=True,
    tol=0.0001,
    C=1.0,
    multi_class='ovr',
    fit_intercept=True,
    intercept_scaling=1,
    class_weight=None,
    verbose=0,
    random_state=None,
    max_iter=1000
)

---

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 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)