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concrete.ml.sklearn.xgb.md

module concrete.ml.sklearn.xgb

Implements XGBoost models.

class XGBClassifier

Implements the XGBoost classifier.

method __init__

__init__(
    n_bits: int = 6,
    max_depth: Optional[int] = 3,
    learning_rate: Optional[float] = 0.1,
    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[RandomState, int] = None,
    verbosity: Optional[int] = None
)

property onnx_model

Get the ONNX model.

.. # noqa: DAR201

Returns:

  • onnx.ModelProto: the ONNX model

method post_processing

post_processing(y_preds: ndarray) → ndarray

Apply post-processing to the predictions.

Args:

  • y_preds (numpy.ndarray): The predictions.

Returns:

  • numpy.ndarray: The post-processed predictions.

class XGBRegressor

Implements the XGBoost regressor.

method __init__

__init__(
    n_bits: int = 6,
    max_depth: Optional[int] = 3,
    learning_rate: Optional[float] = 0.1,
    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[RandomState, int] = None,
    verbosity: Optional[int] = None
)

property onnx_model

Get the ONNX model.

.. # noqa: DAR201

Returns:

  • onnx.ModelProto: the ONNX model

method fit

fit(X, y, **kwargs) → Any

Fit the tree-based estimator.

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.

  • **kwargs: args for super().fit

Returns:

  • Any: The fitted model.

method post_processing

post_processing(y_preds: ndarray) → ndarray

Apply post-processing to the predictions.

Args:

  • y_preds (numpy.ndarray): The predictions.

Returns:

  • numpy.ndarray: The post-processed predictions.

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