# OrderedUniformEncoder
**Compatibility:** `categorical` or `boolean` data
The `OrderedUniformEncoder` transforms data that represents ordered categorical values into a uniform distribution in the `[0,1]` interval. It preserves the frequencies of each category with high accuracy.
```python
from rdt.transformers.categorical import OrderedUniformEncoder
transformer = OrderedUniformEncoder(order=['STRONGLY DISAGREE', 'DISAGREE', 'NEUTRAL',
'AGREE', 'STRONGLY AGREE'])
```
## Parameters
(required) **`order`**: Specify an order to the category values
[list <value>]
An ordered list of the categories that appear in the real data
### Examples
```python
from rdt.transformers.categorical import OrderedUniformEncoder
transformer = OrderedUniformEncoder(order=['STRONGLY DISAGREE', 'DISAGREE', 'NEUTRAL',
'AGREE', 'STRONGLY AGREE'])
```
The transformer assigns each category to a unique, non-overlapping subset of the `[0,1]` interval. The order of the intervals is based on your custom order. The length of the interval is based on the category's frequency. For example if category `'AGREE'` occurs with 20% frequency, the subset will have the length `0.2` such as `[0.5, 0.7]`.
## Attributes
After fitting the transformer, you can access the learned values through the attributes.
**`frequencies`**: A dictionary that maps each category value to the observed frequency, as a float between 0 and 1
```python
>>> transformer.frequencies
{
'STRONGLY DISAGREE': 0.1,
'DISAGREE': 0.2,
'NEUTRAL': 0.2,
'AGREE': 0.2,
'STRONGLY AGREE': 0.3
}
```
**`intervals`**: A dictionary that maps each category value to an interval between `[0,1]`. This allows you to determine the exact rules used for transforming and reverse transforming.
```python
>>> transformer.intervals
{
'STRONGLY DISAGREE': [0, 0.1],
'DISAGREE': [0.1, 0.3],
'NEUTRAL': [0.3, 0.5],
'AGREE': [0.5, 0.7],
'STRONGLY AGREE': [0.7, 1.0]
}
```
## FAQs
When should I use this transformer?
The OrderedUniformEncoder is shown to preserve the frequency of each category value with high accuracy. This is especially useful if you have a data imbalance.
What if my categorical column does not have an order?
This transformer is only defined for ordinal categorical data. If there is no order, your data is *nominal*. Use the [**UniformEncoder**](https://docs.sdv.dev/rdt/transformers-glossary/categorical/uniformencoder) instead.
What happens to missing values?
If there are missing values in your data, they should be defined as part of your order. Use the `None` keyword to denote a missing value.
In the example below, the missing value is added as the last item.
```python
OrderedUniformEncoder(order=['STRONGLY DISAGREE', 'DISAGREE',
'NEUTRAL', 'AGREE', 'STRONGLY AGREE',
None])
```
Add the missing value to whatever ordering position makes sense for your data. If you are unsure, consider adding it to the beginning or the end of the list.
