# Custom metric

In case you don't settle for the default scikit-learn metrics, you can define your own metric.

Here, we expect the organizer to know python. 

### How to define a custom metric

To define a custom metric, change `EVAL_METRIC` in `conf.json` to `custom`. You must also make sure that `EVAL_HIGHER_IS_BETTER` is set to `1` or `0` depending on whether a higher value of the metric is better or not.

The second step is to create a file `metric.py` in the private competition repo.
The file should contain a `compute` function that takes competition params as input.

Here is the part where we check if metric is custom and calculate the metric value:

```python
def compute_metrics(params):
    if params.metric == "custom":
        metric_file = hf_hub_download(
            repo_id=params.competition_id,
            filename="metric.py",
            token=params.token,
            repo_type="dataset",
        )
        sys.path.append(os.path.dirname(metric_file))
        metric = importlib.import_module("metric")
        evaluation = metric.compute(params)
    .
    .
    .
````

You can find the above part in competitions github repo `compute_metrics.py`

`params` is defined as:

```python
class EvalParams(BaseModel):
    competition_id: str
    competition_type: str
    metric: str
    token: str
    team_id: str
    submission_id: str
    submission_id_col: str
    submission_cols: List[str]
    submission_rows: int
    output_path: str
    submission_repo: str
    time_limit: int
    dataset: str  # private test dataset, used only for script competitions
```

You are free to do whatever you want to in the `compute` function. 
In the end it must return a dictionary with the following keys:

```python
{
    "public_score": {
        "metric1": metric_value,
    },,
    "private_score": {
        "metric1": metric_value,
    },,
}
```

public and private scores must be dictionaries! You can also use multiple metrics.
Example for multiple metrics:

```python
{
    "public_score": {
        "metric1": metric_value,
        "metric2": metric_value,
    },
    "private_score": {
        "metric1": metric_value,
        "metric2": metric_value,
    },
}
```

Note: When using multiple metrics, conf.json must have `SCORING_METRIC` specified to rank the participants in the competition.

For example, if I want to use metric2 to rank the participants, I will set `SCORING_METRIC` to `metric2` in `conf.json`.

### Example of a custom metric

```python
import pandas as pd
from huggingface_hub import hf_hub_download


def compute(params):
    solution_file = hf_hub_download(
        repo_id=params.competition_id,
        filename="solution.csv",
        token=params.token,
        repo_type="dataset",
    )

    solution_df = pd.read_csv(solution_file)

    submission_filename = f"submissions/{params.team_id}-{params.submission_id}.csv"
    submission_file = hf_hub_download(
        repo_id=params.competition_id,
        filename=submission_filename,
        token=params.token,
        repo_type="dataset",
    )
    submission_df = pd.read_csv(submission_file)

    public_ids = solution_df[solution_df.split == "public"][params.submission_id_col].values
    private_ids = solution_df[solution_df.split == "private"][params.submission_id_col].values

    public_solution_df = solution_df[solution_df[params.submission_id_col].isin(public_ids)]
    public_submission_df = submission_df[submission_df[params.submission_id_col].isin(public_ids)]

    private_solution_df = solution_df[solution_df[params.submission_id_col].isin(private_ids)]
    private_submission_df = submission_df[submission_df[params.submission_id_col].isin(private_ids)]

    public_solution_df = public_solution_df.sort_values(params.submission_id_col).reset_index(drop=True)
    public_submission_df = public_submission_df.sort_values(params.submission_id_col).reset_index(drop=True)

    private_solution_df = private_solution_df.sort_values(params.submission_id_col).reset_index(drop=True)
    private_submission_df = private_submission_df.sort_values(params.submission_id_col).reset_index(drop=True)

    # CALCULATE METRICS HERE.......
    # _metric = SOME METRIC FUNCTION
    target_cols = [col for col in solution_df.columns if col not in [params.submission_id_col, "split"]]
    public_score = _metric(public_solution_df[target_cols], public_submission_df[target_cols])
    private_score = _metric(private_solution_df[target_cols], private_submission_df[target_cols])

    evaluation = {
        "public_score": {
            "metric1": public_score,
        },
        "private_score": {
            "metric1": public_score,
        }
    }
    return evaluation
```

Take a careful look at the above code. 
You can see that we are downloading the solution file and the submission file from the dataset repo.
We are then calculating the metric on the public and private splits of the solution and submission files. 
Finally, we are returning the metric values in a dictionary.