sklearn.py 文件源码

def threshold_weighted_unique_confusion_matrix(y_true, y_pred,
                                               weights, ids, th=0.5):
    """
    Computes a weighted event-wise confusion matrix with a threshold in 
    predictions.

    Takes numpy arrays

    Arguments:
        y_true - labels
        y_pred - predictions
        weights - weights for each waveform
        ids - ids to correlate waveforms with events
        th - probability threshold above which the signal class is
             considered to predict signal (default: 0.5)

    Returns:
        confusion_matrix - a numpy array containing the confusion matrix

    """
    # This statement flattens vectors from one-hot, thresholds predictions
    return weighted_unique_confusion_matrix(y_true.nonzero()[1],
                                            y_pred[:, 1] > th, weights, ids)