concrete.ml.sklearn.tree_to_numpy.md

module concrete.ml.sklearn.tree_to_numpy

Implements the conversion of a tree model to a numpy function.

Global Variables

• MAX_BITWIDTH_BACKWARD_COMPATIBLE

• OPSET_VERSION_FOR_ONNX_EXPORT

• MSB_TO_KEEP_FOR_TREES

• MIN_CIRCUIT_THRESHOLD_FOR_TREES

---

function get_onnx_model

get_onnx_model(model: Callable, x: ndarray, framework: str) → ModelProto

Create ONNX model with Hummingbird convert method.

Args:

• model (Callable): The tree model to convert.

• x (numpy.ndarray): Dataset used to trace the tree inference and convert the model to ONNX.

• framework (str): The framework from which the ONNX model is generated.

• (options: 'xgboost', 'sklearn')

Returns:

• onnx.ModelProto: The ONNX model.

---

function workaround_squeeze_node_xgboost

workaround_squeeze_node_xgboost(onnx_model: ModelProto)

Workaround to fix torch issue that does not export the proper axis in the ONNX squeeze node.

FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/2778 The squeeze ops does not have the proper dimensions. remove the following workaround when the issue is fixed Add the axis attribute to the Squeeze node

Args:

• onnx_model (onnx.ModelProto): The ONNX model.

---

function assert_add_node_and_constant_in_xgboost_regressor_graph

assert_add_node_and_constant_in_xgboost_regressor_graph(onnx_model: ModelProto)

Assert if an Add node with a specific constant exists in the ONNX graph.

Args:

• onnx_model (onnx.ModelProto): The ONNX model.

---

function add_transpose_after_last_node

add_transpose_after_last_node(onnx_model: ModelProto)

Add transpose after last node.

Args:

• onnx_model (onnx.ModelProto): The ONNX model.

---

function preprocess_tree_predictions

preprocess_tree_predictions(
    init_tensor: ndarray,
    output_n_bits: int
) → QuantizedArray

Apply post-processing from the graph.

Args:

• init_tensor (numpy.ndarray): Model parameters to be pre-processed.

• output_n_bits (int): The number of bits of the output.

Returns:

• QuantizedArray: Quantizer for the tree predictions.

---

function tree_onnx_graph_preprocessing

tree_onnx_graph_preprocessing(
    onnx_model: ModelProto,
    framework: str,
    expected_number_of_outputs: int
)

Apply pre-processing onto the ONNX graph.

Args:

• onnx_model (onnx.ModelProto): The ONNX model.

• framework (str): The framework from which the ONNX model is generated.

• (options: 'xgboost', 'sklearn')

• expected_number_of_outputs (int): The expected number of outputs in the ONNX model.

---

function tree_values_preprocessing

tree_values_preprocessing(
    onnx_model: ModelProto,
    framework: str,
    output_n_bits: int
) → QuantizedArray

Pre-process tree values.

Args:

• onnx_model (onnx.ModelProto): The ONNX model.

• framework (str): The framework from which the ONNX model is generated.

• (options: 'xgboost', 'sklearn')

• output_n_bits (int): The number of bits of the output.

Returns:

• QuantizedArray: Quantizer for the tree predictions.

---

function tree_to_numpy

tree_to_numpy(
    model: Callable,
    x: ndarray,
    framework: str,
    use_rounding: bool = True,
    output_n_bits: int = 8
) → Tuple[Callable, List[UniformQuantizer], ModelProto]

Convert the tree inference to a numpy functions using Hummingbird.

Args:

• model (Callable): The tree model to convert.

• x (numpy.ndarray): The input data.

• use_rounding (bool): This parameter is exclusively used to tree-based models. It determines whether the rounding feature is enabled or disabled.

• framework (str): The framework from which the ONNX model is generated.

• (options: 'xgboost', 'sklearn')

• output_n_bits (int): The number of bits of the output. Default to 8.

Returns:

• Tuple[Callable, List[QuantizedArray], onnx.ModelProto]: A tuple with a function that takes a numpy array and returns a numpy array, QuantizedArray object to quantize and de-quantize the output of the tree, and the ONNX model.