Handle **high-cardinality categorical features** during preprocessing, where categories can have many unique values and naive one-hot encoding explodes feature size. You’ll implement a simple **top-K + “OTHER”** encoder to keep the representation compact.

The encoding is defined as:
$$
\text{enc}(x)=
\begin{cases}
x & \text{if } x \in \text{TopK}(X, K) \\
\text{"OTHER"} & \text{otherwise}
\end{cases}
$$

## Requirements

Implement the function:
```python
def topk_other_encode(values, k):
    \"""
    Encodes a high-cardinality categorical column by keeping only the top-k
    most frequent categories and mapping the rest to "OTHER".

    Args:
        values (np.ndarray): Categorical values (one column).
        k (int): Number of most frequent categories to keep.

    Returns:
        np.ndarray: Encoded values where rare categories are replaced by "OTHER".
    \"""
    # Your solution below
```

**Rules:**
- Count category frequencies in `values` and identify the `k` most frequent categories.
- If there are ties in frequency, break ties by lexicographic order (ascending).
- Replace any category not in the top-k set with the string `"OTHER"`.
- Return the encoded array in the same order as input.
- Use only **NumPy**.

## Example

```python
values = np.array(["red", "blue", "red", "green", "blue", "yellow", "blue"])
k = 2

topk_other_encode(values, k)
```

Output:
```python
array(["red", "blue", "red", "OTHER", "blue", "OTHER", "blue"], dtype='<U6')
```

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High cardinality categoricals

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146. High cardinality categoricals

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Argument	Type
k	int
values	np.ndarray