def _compute_curve()

in python/mxboard/summary.py [0:0]

• 23 lines of code
• 3 McCabe index (conditional complexity)

def _compute_curve(labels, predictions, num_thresholds, weights=None):
    """This function is another implementation of functions in
    https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/summary.py"""

    if weights is None:
        weights = 1.0

    # Compute bins of true positives and false positives.
    bucket_indices = np.int32(np.floor(predictions * (num_thresholds - 1)))
    float_labels = labels.astype(np.float)
    histogram_range = (0, num_thresholds - 1)
    tp_buckets, _ = np.histogram(
        bucket_indices,
        bins=num_thresholds,
        range=histogram_range,
        weights=float_labels * weights)
    fp_buckets, _ = np.histogram(
        bucket_indices,
        bins=num_thresholds,
        range=histogram_range,
        weights=(1.0 - float_labels) * weights)

    # Obtain the reverse cumulative sum.
    tp = np.cumsum(tp_buckets[::-1])[::-1]
    fp = np.cumsum(fp_buckets[::-1])[::-1]
    tn = fp[0] - fp
    fn = tp[0] - tp
    precision = tp / np.maximum(_MINIMUM_COUNT, tp + fp)
    recall = tp / np.maximum(_MINIMUM_COUNT, tp + fn)
    return np.stack((tp, fp, tn, fn, precision, recall))