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Evaluate

evaluate(qrels, run, metrics, return_mean=True, return_std=False, threads=0, save_results_in_run=True, make_comparable=False)

Compute the performance scores for the provided qrels and run for all the specified metrics.

Usage examples:

from ranx import evaluate

Compute score for a single metric

evaluate(qrels, run, "ndcg@5")

0.7861

Compute scores for multiple metrics at once

evaluate(qrels, run, ["map@5", "mrr"])

{"map@5": 0.6416, "mrr": 0.75}

Computed metric scores are saved in the Run object

run.mean_scores

{"ndcg@5": 0.7861, "map@5": 0.6416, "mrr": 0.75}

Access scores for each query

dict(run.scores)

{ ... "ndcg@5": {"q_1": 0.9430, "q_2": 0.6292}, ... "map@5": {"q_1": 0.8333, "q_2": 0.4500}, ... "mrr": {"q_1": 1.0000, "q_2": 0.5000}, ... } Args: qrels (Union[ Qrels, Dict[str, Dict[str, Number]], nb.typed.typedlist.List, np.ndarray, ]): Qrels. run (Union[ Run, Dict[str, Dict[str, Number]], nb.typed.typedlist.List, np.ndarray, ]): Run. metrics (Union[List[str], str]): Metrics or list of metric to compute. return_mean (bool, optional): Whether to return the metric scores averaged over the query set or the scores for individual queries. Defaults to True. threads (int, optional): Number of threads to use, zero means all the available threads. Defaults to 0. save_results_in_run (bool, optional): Save metric scores for each query in the input run. Defaults to True. make_comparable (bool, optional): Adds empty results for queries missing from the run and removes those not appearing in qrels. Defaults to False.

Returns:

Type Description
Union[Dict[str, float], float]

Union[Dict[str, float], float]: Results.
Source code in ranx/meta/evaluate.py 
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def evaluate(
    qrels: Union[
        Qrels,
        Dict[str, Dict[str, Number]],
        nb.typed.typedlist.List,
        np.ndarray,
    ],
    run: Union[
        Run,
        Dict[str, Dict[str, Number]],
        nb.typed.typedlist.List,
        np.ndarray,
    ],
    metrics: Union[List[str], str],
    return_mean: bool = True,
    return_std: bool = False,
    threads: int = 0,
    save_results_in_run: bool = True,
    make_comparable: bool = False,
) -> Union[Dict[str, float], float]:
    """Compute the performance scores for the provided `qrels` and `run` for all the specified metrics.

    Usage examples:

    from ranx import evaluate

    # Compute score for a single metric
    evaluate(qrels, run, "ndcg@5")
    >>> 0.7861

    # Compute scores for multiple metrics at once
    evaluate(qrels, run, ["map@5", "mrr"])
    >>> {"map@5": 0.6416, "mrr": 0.75}

    # Computed metric scores are saved in the Run object
    run.mean_scores
    >>> {"ndcg@5": 0.7861, "map@5": 0.6416, "mrr": 0.75}

    # Access scores for each query
    dict(run.scores)
    >>> {
    ...     "ndcg@5": {"q_1": 0.9430, "q_2": 0.6292},
    ...     "map@5": {"q_1": 0.8333, "q_2": 0.4500},
    ...     "mrr": {"q_1": 1.0000, "q_2": 0.5000},
    ... }
    Args:
        qrels (Union[ Qrels, Dict[str, Dict[str, Number]], nb.typed.typedlist.List, np.ndarray, ]): Qrels.
        run (Union[ Run, Dict[str, Dict[str, Number]], nb.typed.typedlist.List, np.ndarray, ]): Run.
        metrics (Union[List[str], str]): Metrics or list of metric to compute.
        return_mean (bool, optional): Whether to return the metric scores averaged over the query set or the scores for individual queries. Defaults to True.
        threads (int, optional): Number of threads to use, zero means all the available threads. Defaults to 0.
        save_results_in_run (bool, optional): Save metric scores for each query in the input `run`. Defaults to True.
        make_comparable (bool, optional): Adds empty results for queries missing from the run and removes those not appearing in qrels. Defaults to False.

    Returns:
        Union[Dict[str, float], float]: Results.
    """

    if len(qrels) < 10:
        set_num_threads(1)
    elif threads != 0:
        set_num_threads(threads)

    if not return_mean:
        return_std = False

    if make_comparable and type(qrels) == Qrels and type(run) == Run:
        run = run.make_comparable(qrels)

    if type(qrels) in [Qrels, dict] and type(run) in [Run, dict]:
        check_keys(qrels, run)

    _qrels = convert_qrels(qrels)
    _run = convert_run(run)
    metrics = format_metrics(metrics)
    assert all(isinstance(m, str) for m in metrics), "Metrics error"

    # Compute metrics ----------------------------------------------------------
    metric_scores_dict = {}
    for metric in metrics:
        m, k, rel_lvl = extract_metric_and_params(metric)
        metric_scores_dict[metric] = metric_switch(m)(_qrels, _run, k, rel_lvl)

    # Save results in Run ------------------------------------------------------
    if type(run) == Run and save_results_in_run:
        for m, scores in metric_scores_dict.items():
            run.mean_scores[m] = np.mean(scores)
            if return_std:
                run.std_scores[m] = np.std(scores)
            for i, q_id in enumerate(run.get_query_ids()):
                run.scores[m][q_id] = scores[i]

    # Prepare output -----------------------------------------------------------
    if return_mean:
        for m, scores in metric_scores_dict.items():
            if return_std:
                metric_scores_dict[m] = {
                    "mean": np.mean(scores),
                    "std": np.std(scores),
                }

            else:
                metric_scores_dict[m] = np.mean(scores)

    return metric_scores_dict[m] if len(metrics) == 1 else metric_scores_dict