# concrete.ml.sklearn.tree\_to\_numpy.md

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## 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**

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/tree_to_numpy.py#L31)

### function `get_onnx_model`

```python
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.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/tree_to_numpy.py#L63)

### function `workaround_squeeze_node_xgboost`

```python
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.

***

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### function `add_transpose_after_last_node`

```python
add_transpose_after_last_node(onnx_model: ModelProto)
```

Add transpose after last node.

**Args:**

* `onnx_model` (onnx.ModelProto): The ONNX model.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/tree_to_numpy.py#L109)

### function `preprocess_tree_predictions`

```python
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.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/tree_to_numpy.py#L159)

### function `tree_onnx_graph_preprocessing`

```python
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.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/tree_to_numpy.py#L208)

### function `tree_values_preprocessing`

```python
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.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/tree_to_numpy.py#L252)

### function `tree_to_numpy`

```python
tree_to_numpy(
    model: Callable,
    x: ndarray,
    framework: str,
    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.
* `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.


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