> For the complete documentation index, see [llms.txt](https://docs.zama.org/concrete-ml/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zama.org/concrete-ml/0.4/developer-guide/api/concrete.ml.sklearn.protocols.md).

# concrete.ml.sklearn.protocols

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L0)

## module `concrete.ml.sklearn.protocols`

Protocols.

Protocols are used to mix type hinting with duck-typing. Indeed we don't always want to have an abstract parent class between all objects. We are more interested in the behavior of such objects. Implementing a Protocol is a way to specify the behavior of objects.

To read more about Protocol please read: <https://peps.python.org/pep-0544>

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L26)

### class `Quantizer`

Quantizer Protocol.

To use to type hint a quantizer.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L44)

#### method `dequant`

```python
dequant(X: 'ndarray') → ndarray
```

Dequantize some values.

**Args:**

* **`X`** (numpy.ndarray): Values to dequantize

.. # noqa: DAR202

**Returns:**

* **`numpy.ndarray`**: Dequantized values

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L32)

#### method `quant`

```python
quant(values: 'ndarray') → ndarray
```

Quantize some values.

**Args:**

* **`values`** (numpy.ndarray): Values to quantize

.. # noqa: DAR202

**Returns:**

* **`numpy.ndarray`**: The quantized values

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L58)

### class `ConcreteBaseEstimatorProtocol`

A Concrete Estimator Protocol.

***

**property onnx\_model**

onnx\_model.

.. # noqa: DAR202

Results: onnx.ModelProto

***

**property quantize\_input**

Quantize input function.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L97)

#### method `compile`

```python
compile(
    X: 'ndarray',
    configuration: 'Optional[Configuration]',
    compilation_artifacts: 'Optional[DebugArtifacts]',
    show_mlir: 'bool',
    use_virtual_lib: 'bool',
    p_error: 'float'
) → Circuit
```

Compiles a model to a FHE Circuit.

**Args:**

* **`X`** (numpy.ndarray): the dequantized dataset
* **`configuration`** (Optional\[Configuration]): the options for compilation
* **`compilation_artifacts`** (Optional\[DebugArtifacts]): artifacts object to fill during compilation
* **`show_mlir`** (bool): whether or not to show MLIR during the compilation
* **`use_virtual_lib`** (bool): whether to compile using the virtual library that allows higher bitwidths
* **`p_error`** (float): probability of error of a PBS

.. # noqa: DAR202

**Returns:**

* **`Circuit`**: the compiled Circuit.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L128)

#### method `fit`

```python
fit(X: 'ndarray', y: 'ndarray', **fit_params) → ConcreteBaseEstimatorProtocol
```

Initialize and fit the module.

**Args:**

* **`X`** : training data By default, you should be able to pass: \* numpy arrays \* torch tensors \* pandas DataFrame or Series
* **`y`** (numpy.ndarray): labels associated with training data
* **`**fit_params`**: additional parameters that can be used during training

.. # noqa: DAR202

**Returns:**

* **`ConcreteBaseEstimatorProtocol`**: the trained estimator

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L148)

#### method `fit_benchmark`

```python
fit_benchmark(
    X: 'ndarray',
    y: 'ndarray',
    *args,
    **kwargs
) → Tuple[ConcreteBaseEstimatorProtocol, BaseEstimator]
```

Fit the quantized estimator and return reference estimator.

This function returns both the quantized estimator (itself), but also a wrapper around the non-quantized trained NN. This is useful in order to compare performance between the quantized and fp32 versions of the classifier

**Args:**

* **`X`** : training data By default, you should be able to pass: \* numpy arrays \* torch tensors \* pandas DataFrame or Series
* **`y`** (numpy.ndarray): labels associated with training data
* **`*args`**: The arguments to pass to the underlying model.
* **`**kwargs`**: The keyword arguments to pass to the underlying model.

.. # noqa: DAR202

**Returns:**

* **`self`**: self fitted
* **`model`**: underlying estimator

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L85)

#### method `post_processing`

```python
post_processing(y_preds: 'ndarray') → ndarray
```

Post-process models predictions.

**Args:**

* **`y_preds`** (numpy.ndarray): predicted values by model (clear-quantized)

.. # noqa: DAR202

**Returns:**

* **`numpy.ndarray`**: the post-processed predictions

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L175)

### class `ConcreteBaseClassifierProtocol`

Concrete classifier protocol.

***

**property onnx\_model**

onnx\_model.

.. # noqa: DAR202

Results: onnx.ModelProto

***

**property quantize\_input**

Quantize input function.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L97)

#### method `compile`

```python
compile(
    X: 'ndarray',
    configuration: 'Optional[Configuration]',
    compilation_artifacts: 'Optional[DebugArtifacts]',
    show_mlir: 'bool',
    use_virtual_lib: 'bool',
    p_error: 'float'
) → Circuit
```

Compiles a model to a FHE Circuit.

**Args:**

* **`X`** (numpy.ndarray): the dequantized dataset
* **`configuration`** (Optional\[Configuration]): the options for compilation
* **`compilation_artifacts`** (Optional\[DebugArtifacts]): artifacts object to fill during compilation
* **`show_mlir`** (bool): whether or not to show MLIR during the compilation
* **`use_virtual_lib`** (bool): whether to compile using the virtual library that allows higher bitwidths
* **`p_error`** (float): probability of error of a PBS

.. # noqa: DAR202

**Returns:**

* **`Circuit`**: the compiled Circuit.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L128)

#### method `fit`

```python
fit(X: 'ndarray', y: 'ndarray', **fit_params) → ConcreteBaseEstimatorProtocol
```

Initialize and fit the module.

**Args:**

* **`X`** : training data By default, you should be able to pass: \* numpy arrays \* torch tensors \* pandas DataFrame or Series
* **`y`** (numpy.ndarray): labels associated with training data
* **`**fit_params`**: additional parameters that can be used during training

.. # noqa: DAR202

**Returns:**

* **`ConcreteBaseEstimatorProtocol`**: the trained estimator

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L148)

#### method `fit_benchmark`

```python
fit_benchmark(
    X: 'ndarray',
    y: 'ndarray',
    *args,
    **kwargs
) → Tuple[ConcreteBaseEstimatorProtocol, BaseEstimator]
```

Fit the quantized estimator and return reference estimator.

This function returns both the quantized estimator (itself), but also a wrapper around the non-quantized trained NN. This is useful in order to compare performance between the quantized and fp32 versions of the classifier

**Args:**

* **`X`** : training data By default, you should be able to pass: \* numpy arrays \* torch tensors \* pandas DataFrame or Series
* **`y`** (numpy.ndarray): labels associated with training data
* **`*args`**: The arguments to pass to the underlying model.
* **`**kwargs`**: The keyword arguments to pass to the underlying model.

.. # noqa: DAR202

**Returns:**

* **`self`**: self fitted
* **`model`**: underlying estimator

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L85)

#### method `post_processing`

```python
post_processing(y_preds: 'ndarray') → ndarray
```

Post-process models predictions.

**Args:**

* **`y_preds`** (numpy.ndarray): predicted values by model (clear-quantized)

.. # noqa: DAR202

**Returns:**

* **`numpy.ndarray`**: the post-processed predictions

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L178)

#### method `predict`

```python
predict(X: 'ndarray', execute_in_fhe: 'bool') → ndarray
```

Predicts for each sample the class with highest probability.

**Args:**

* **`X`** (numpy.ndarray): Features
* **`execute_in_fhe`** (bool): Whether the inference should be done in fhe or not.

.. # noqa: DAR202

**Returns:** numpy.ndarray

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L192)

#### method `predict_proba`

```python
predict_proba(X: 'ndarray', execute_in_fhe: 'bool') → ndarray
```

Predicts for each sample the probability of each class.

**Args:**

* **`X`** (numpy.ndarray): Features
* **`execute_in_fhe`** (bool): Whether the inference should be done in fhe or not.

.. # noqa: DAR202

**Returns:** numpy.ndarray

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L207)

### class `ConcreteBaseRegressorProtocol`

Concrete regressor protocol.

***

**property onnx\_model**

onnx\_model.

.. # noqa: DAR202

Results: onnx.ModelProto

***

**property quantize\_input**

Quantize input function.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L97)

#### method `compile`

```python
compile(
    X: 'ndarray',
    configuration: 'Optional[Configuration]',
    compilation_artifacts: 'Optional[DebugArtifacts]',
    show_mlir: 'bool',
    use_virtual_lib: 'bool',
    p_error: 'float'
) → Circuit
```

Compiles a model to a FHE Circuit.

**Args:**

* **`X`** (numpy.ndarray): the dequantized dataset
* **`configuration`** (Optional\[Configuration]): the options for compilation
* **`compilation_artifacts`** (Optional\[DebugArtifacts]): artifacts object to fill during compilation
* **`show_mlir`** (bool): whether or not to show MLIR during the compilation
* **`use_virtual_lib`** (bool): whether to compile using the virtual library that allows higher bitwidths
* **`p_error`** (float): probability of error of a PBS

.. # noqa: DAR202

**Returns:**

* **`Circuit`**: the compiled Circuit.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L128)

#### method `fit`

```python
fit(X: 'ndarray', y: 'ndarray', **fit_params) → ConcreteBaseEstimatorProtocol
```

Initialize and fit the module.

**Args:**

* **`X`** : training data By default, you should be able to pass: \* numpy arrays \* torch tensors \* pandas DataFrame or Series
* **`y`** (numpy.ndarray): labels associated with training data
* **`**fit_params`**: additional parameters that can be used during training

.. # noqa: DAR202

**Returns:**

* **`ConcreteBaseEstimatorProtocol`**: the trained estimator

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L148)

#### method `fit_benchmark`

```python
fit_benchmark(
    X: 'ndarray',
    y: 'ndarray',
    *args,
    **kwargs
) → Tuple[ConcreteBaseEstimatorProtocol, BaseEstimator]
```

Fit the quantized estimator and return reference estimator.

This function returns both the quantized estimator (itself), but also a wrapper around the non-quantized trained NN. This is useful in order to compare performance between the quantized and fp32 versions of the classifier

**Args:**

* **`X`** : training data By default, you should be able to pass: \* numpy arrays \* torch tensors \* pandas DataFrame or Series
* **`y`** (numpy.ndarray): labels associated with training data
* **`*args`**: The arguments to pass to the underlying model.
* **`**kwargs`**: The keyword arguments to pass to the underlying model.

.. # noqa: DAR202

**Returns:**

* **`self`**: self fitted
* **`model`**: underlying estimator

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L85)

#### method `post_processing`

```python
post_processing(y_preds: 'ndarray') → ndarray
```

Post-process models predictions.

**Args:**

* **`y_preds`** (numpy.ndarray): predicted values by model (clear-quantized)

.. # noqa: DAR202

**Returns:**

* **`numpy.ndarray`**: the post-processed predictions

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/protocols.py#L210)

#### method `predict`

```python
predict(X: 'ndarray', execute_in_fhe: 'bool') → ndarray
```

Predicts for each sample the expected value.

**Args:**

* **`X`** (numpy.ndarray): Features
* **`execute_in_fhe`** (bool): Whether the inference should be done in fhe or not.

.. # noqa: DAR202

**Returns:** numpy.ndarray


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