concrete.ml.sklearn.rf.md

module concrete.ml.sklearn.rf

Implement RandomForest models.

---

class RandomForestClassifier

Implements the RandomForest classifier.

method __init__

__init__(
    n_bits: 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/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)

---

method post_processing

post_processing(y_preds: ndarray) → ndarray

---

class RandomForestRegressor

Implements the RandomForest regressor.

method __init__

__init__(
    n_bits: 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='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,
    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/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)