> 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/1.0-2/developer-guide/api/concrete.ml.pytest.utils.md).

# concrete.ml.pytest.utils.md

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.0.x/src/concrete/ml/pytest/utils.py#L0)

## module `concrete.ml.pytest.utils`

Common functions or lists for test files, which can't be put in fixtures.

### **Global Variables**

* **sklearn\_models\_and\_datasets**

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.0.x/src/concrete/ml/pytest/utils.py#L123)

### function `get_random_extract_of_sklearn_models_and_datasets`

```python
get_random_extract_of_sklearn_models_and_datasets()
```

Return a random sublist of sklearn\_models\_and\_datasets.

The sublist contains exactly one model of each kind.

**Returns:** the sublist

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.0.x/src/concrete/ml/pytest/utils.py#L150)

### function `instantiate_model_generic`

```python
instantiate_model_generic(model_class, **parameters)
```

Instantiate any Concrete ML model type.

**Args:**

* `model_class` (class): The type of the model to instantiate
* `parameters` (dict): Hyper-parameters for the model instantiation

**Returns:**

* `model_name` (str): The type of the model as a string
* `model` (object): The model instance

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.0.x/src/concrete/ml/pytest/utils.py#L193)

### function `get_torchvision_dataset`

```python
get_torchvision_dataset(param: Dict, train_set: bool)
```

Get train or testing data-set.

**Args:**

* `param` (Dict): Set of hyper-parameters to use based on the selected torchvision data-set.
* `It must contain`: data-set transformations (torchvision.transforms.Compose), and the data-set\_size (Optional\[int]).
* `train_set` (bool): Use train data-set if True, else testing data-set

**Returns:** A torchvision data-sets.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.0.x/src/concrete/ml/pytest/utils.py#L221)

### function `data_calibration_processing`

```python
data_calibration_processing(data, n_sample: int, targets=None)
```

Reduce size of the given data-set.

**Args:**

* `data`: The input container to consider
* `n_sample` (int): Number of samples to keep if the given data-set
* `targets`: If `dataset` is a `torch.utils.data.Dataset`, it typically contains both the data and the corresponding targets. In this case, `targets` must be set to `None`. If `data` is instance of `torch.Tensor` or 'numpy.ndarray`,` targets\` is expected.

**Returns:**

* `Tuple[numpy.ndarray, numpy.ndarray]`: The input data and the target (respectively x and y).

**Raises:**

* `TypeError`: If the 'data-set' does not match any expected type.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.0.x/src/concrete/ml/pytest/utils.py#L273)

### function `load_torch_model`

```python
load_torch_model(
    model_class: Module,
    state_dict_or_path: Optional[str, Path, Dict[str, Any]],
    params: Dict,
    device: str = 'cpu'
) → Module
```

Load an object saved with torch.save() from a file or dict.

**Args:**

* `model_class` (torch.nn.Module): A PyTorch or Brevitas network.
* `state_dict_or_path` (Optional\[Union\[str, Path, Dict\[str, Any]]]): Path or state\_dict
* `params` (Dict): Model's parameters
* `device` (str): Device type.

**Returns:**

* `torch.nn.Module`: A PyTorch or Brevitas network.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.0.x/src/concrete/ml/pytest/utils.py#L303)

### function `values_are_equal`

```python
values_are_equal(value_1: Any, value_2: Any) → bool
```

Indicate if two values are equal.

This method takes into account objects of type None, numpy.ndarray, numpy.floating, numpy.integer, numpy.random.RandomState or any instance that provides a `__eq__` method.

**Args:**

* `value_2` (Any): The first value to consider.
* `value_1` (Any): The second value to consider.

**Returns:**

* `bool`: If the two values are equal.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.0.x/src/concrete/ml/pytest/utils.py#L347)

### function `check_serialization`

```python
check_serialization(
    object_to_serialize: Any,
    expected_type: Type,
    equal_method: Optional[Callable] = None,
    check_str: bool = True
)
```

Check that the given object can properly be serialized.

This function serializes all objects using the `dump`, `dumps`, `load` and `loads` functions from Concrete ML. If the given object provides a `dump` and `dumps` method, they are also serialized using these.

**Args:**

* `object_to_serialize` (Any): The object to serialize.
* `expected_type` (Type): The object's expected type.
* `equal_method` (Optional\[Callable]): The function to use to compare the two loaded objects. Default to `values_are_equal`.
* `check_str` (bool): If the JSON strings should also be checked. Default to True.


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