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concrete.ml.sklearn.rf.md

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module concrete.ml.sklearn.rf

Implement RandomForest models.

class RandomForestClassifier

Implements the RandomForest classifier.

method __init__

__init__(
    n_bits: Union[int, Dict[str, int]] = 6,
    n_estimators=20,
    criterion='gini',
    max_depth=4,
    min_samples_split=2,
    min_samples_leaf=1,
    min_weight_fraction_leaf=0.0,
    max_features='sqrt',
    max_leaf_nodes=None,
    min_impurity_decrease=0.0,
    bootstrap=True,
    oob_score=False,
    n_jobs=None,
    random_state=None,
    verbose=0,
    warm_start=False,
    class_weight=None,
    ccp_alpha=0.0,
    max_samples=None
)

Initialize the RandomForestClassifier.

noqa: DAR101

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/get-started/terminology) 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)

method post_processing

post_processing(y_preds: ndarray) → ndarray

class RandomForestRegressor

Implements the RandomForest regressor.

method __init__

__init__(
    n_bits: Union[int, Dict[str, int]] = 6,
    n_estimators=20,
    criterion='squared_error',
    max_depth=4,
    min_samples_split=2,
    min_samples_leaf=1,
    min_weight_fraction_leaf=0.0,
    max_features=1.0,
    max_leaf_nodes=None,
    min_impurity_decrease=0.0,
    bootstrap=True,
    oob_score=False,
    n_jobs=None,
    random_state=None,
    verbose=0,
    warm_start=False,
    ccp_alpha=0.0,
    max_samples=None
)

Initialize the RandomForestRegressor.

noqa: DAR101

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/get-started/terminology) 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)