# Custom Metrics

In order to use the custom metrics functionality, you first need to create a python file that contains
the function used to calculate the custom metric. This function should be named `custom_metric_func`,
and the interface this function is expected to expose is below:

```python
from typing import Optional

import numpy as np
import pandas as pd


def custom_metric_func(
    df: Optional[pd.DataFrame],
    labels: Optional[pd.Series],
    preds: Optional[np.ndarray],
    preds_index: Optional[pd.Index],
    query_ids: Optional[pd.Series],
) -> Optional[float]:
    """Return a custom metric.

    Args:
        df: Optional pd.DataFrame of data, will be the full data.
        labels: Optional pd.Series of labels, if exists will have the same length
            as df.
        preds: Optional np.array of predictions, if exists may be a subset of the
            full df.
        preds_index: Optional pd.Index of predictions, if exists will have the same
            length as `preds`. Can be used to align the predictions with rows in the
            df.
        query_ids: Optional pd.Series of query ids, if exists will have the same
            length as `df`.

    Returns:
        an optional float corresponding to the metric to be tracked.
    """
```

After defining this function, you then need to specify it in the CLIConfig. The place to specify it is
in the `model_profiling_info` section. Specifically, it should look like:

```json
  "model_profiling_info": {
    "custom_metrics": {"NAME_OF_METRIC": "path/to/file.py"}
  },
```

Replace `NAME_OF_METRIC` with the name of the metric (this is how it will show up in the UI). Replace `path/to/file.py`
with the path to the python file you created in the above step.