evaluation.py 文件源码

def precision_recall_at_x_proportion(test_labels, test_predictions, x_proportion=0.01,
                                     return_cutoff=False):
    """Compute precision, recall, F1 for a specified fraction of the test set.

    :params list test_labels: true labels on test set
    :params list test_predicted: predicted labels on test set
    :params float x_proportion: proportion of the test set to flag
    :params bool return_cutoff: if True return the cutoff probablility
    :returns float precision: fraction correctly flagged
    :returns float recall: fraction of the positive class recovered
    :returns float f1: 
    """

    cutoff_index = int(len(test_predictions) * x_proportion)
    cutoff_index = min(cutoff_index, len(test_predictions) - 1)

    sorted_by_probability = np.sort(test_predictions)[::-1]
    cutoff_probability = sorted_by_probability[cutoff_index]

    test_predictions_binary = [1 if x > cutoff_probability else 0 for x in test_predictions]

    precision, recall, f1, _ = metrics.precision_recall_fscore_support(
        test_labels, test_predictions_binary)

    # Only interested in metrics for label 1
    precision, recall, f1 = precision[1], recall[1], f1[1]

    if return_cutoff:
        return precision, recall, f1, cutoff_probability
    else:
        return precision, recall, f1