# ❖ MixedScales

{% hint style="info" %}
❖ **SDV Enterprise Bundle**. This feature is available as part of the **CAG Bundle**, an optional add-on to SDV Enterprise. For more information, please visit the [CAG Bundle](/sdv/explore/sdv-bundles/cag.md) page.
{% endhint %}

The **MixedScales** constraint enforces that the value of a categorical column (or a combination of categorical columns) determines the scale of a numerical column. 

<figure><img src="/files/zDj9VFAQ5mzKNBf2dGdt" alt=""><figcaption><p>For example, the combined value of categorical columns <code>test_type</code> and <code>units</code> will determine the value of numerical column for <code>test_results</code>. So if test type is <code>'height'</code> and units are <code>'inches'</code> then it forces the synthesizer to learn the scale specially for this segment.</p></figcaption></figure>

## Constraint API

Create a `MixedScales` constraint.

**Parameters**

* (required) `segment_column_names`: A list of one or more categorical columns that ultimately segment the data into different groups of rows. Each group will have a different scale.
* (required) `mixed_scale_column_name`: A numerical column whose scale depends on the segment.
* `table_name`: A string with the name of the table to apply this to. Required if you have a multi-table dataset.

```python
from sdv.cag import MixedScales

my_constraint = MixedScales(
    segment_column_names=['test_type', 'units'],
    'mixed_scale_column_name'='test_results'
)
```

## Usage

Apply the constraint to any SDV synthesizer. Then fit and sample as usual.

```python
synthesizer = GaussianCopulaSynthesizer(metadata)
synthesizer.add_constraints([my_constraint])

synthesizer.fit(data)
synthetic_data = synthesizer.sample()
```

For more information about using predefined constraints, please see the [**Constraint-Augmented Generation tutorial**](https://colab.research.google.com/drive/1WCMQujfVKL5giULZXOPPIBoMfzR9Zj68?usp=sharing).

## Auto-Detection

Auto-detection is allowed for this constraint but it is not enabled by default.

If you'd like to auto-detect this constraint specifically, you can supply it in the auto-detection parameters. This will detect all instances of the constraint throughout the dataset.

```python
constraints = synthesizer.detect_constraints(
    data,
    constraint_classes=['MixedScales']
)
```

**Detection Parameters**: None

## FAQs

<details>

<summary>What if I only have one categorical column that determines the scale?</summary>

In this case, please provide a list of just one column name as your `segment_column_names`:

```python
my_constraint = {
    'constraint_class': 'MixeScales',
    'table_name': 'patient_test_results', # for multi table synthesizers
    'constraint_parameters': {
        'segment_column_names': ['test_type'],
        'mixed_scale_column_name': 'test_results'
    }
}
```

</details>

<details>

<summary>Why can't I input the scale min and max for each segment?</summary>

In this constraint, you will only define the column names. SDV synthesizers will use this information to automatically learn the scales (min/max values and distribution) for each different segment of the real data. There is no need to manually input this data.

</details>


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