concrete.ml.sklearn.xgb.md

module concrete.ml.sklearn.xgb

Implements XGBoost models.

---

class XGBClassifier

Implements the XGBoost classifier.

See https://xgboost.readthedocs.io/en/stable/python/python_api.html#module-xgboost.sklearn for more information about the parameters used.

method __init__

__init__(
    n_bits: int = 6,
    max_depth: Optional[int] = 3,
    learning_rate: Optional[float] = None,
    n_estimators: Optional[int] = 20,
    objective: Optional[str] = 'binary:logistic',
    booster: Optional[str] = None,
    tree_method: Optional[str] = None,
    n_jobs: Optional[int] = None,
    gamma: Optional[float] = None,
    min_child_weight: Optional[float] = None,
    max_delta_step: Optional[float] = None,
    subsample: Optional[float] = None,
    colsample_bytree: Optional[float] = None,
    colsample_bylevel: Optional[float] = None,
    colsample_bynode: Optional[float] = None,
    reg_alpha: Optional[float] = None,
    reg_lambda: Optional[float] = None,
    scale_pos_weight: Optional[float] = None,
    base_score: Optional[float] = None,
    missing: float = nan,
    num_parallel_tree: Optional[int] = None,
    monotone_constraints: Optional[Dict[str, int], str] = None,
    interaction_constraints: Optional[str, List[Tuple[str]]] = None,
    importance_type: Optional[str] = None,
    gpu_id: Optional[int] = None,
    validate_parameters: Optional[bool] = None,
    predictor: Optional[str] = None,
    enable_categorical: bool = False,
    use_label_encoder: bool = False,
    random_state: Optional[int] = None,
    verbosity: Optional[int] = None
)

---

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)

---

class XGBRegressor

Implements the XGBoost regressor.

See https://xgboost.readthedocs.io/en/stable/python/python_api.html#module-xgboost.sklearn for more information about the parameters used.

method __init__

__init__(
    n_bits: int = 6,
    max_depth: Optional[int] = 3,
    learning_rate: Optional[float] = None,
    n_estimators: Optional[int] = 20,
    objective: Optional[str] = 'reg:squarederror',
    booster: Optional[str] = None,
    tree_method: Optional[str] = None,
    n_jobs: Optional[int] = None,
    gamma: Optional[float] = None,
    min_child_weight: Optional[float] = None,
    max_delta_step: Optional[float] = None,
    subsample: Optional[float] = None,
    colsample_bytree: Optional[float] = None,
    colsample_bylevel: Optional[float] = None,
    colsample_bynode: Optional[float] = None,
    reg_alpha: Optional[float] = None,
    reg_lambda: Optional[float] = None,
    scale_pos_weight: Optional[float] = None,
    base_score: Optional[float] = None,
    missing: float = nan,
    num_parallel_tree: Optional[int] = None,
    monotone_constraints: Optional[Dict[str, int], str] = None,
    interaction_constraints: Optional[str, List[Tuple[str]]] = None,
    importance_type: Optional[str] = None,
    gpu_id: Optional[int] = None,
    validate_parameters: Optional[bool] = None,
    predictor: Optional[str] = None,
    enable_categorical: bool = False,
    use_label_encoder: bool = False,
    random_state: Optional[int] = None,
    verbosity: Optional[int] = None
)

---

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]

---

method fit

fit(X, y, *args, **kwargs) → Any

---

classmethod load_dict

load_dict(metadata: Dict)

---

method post_processing

post_processing(y_preds: ndarray) → ndarray