easy_rec/python/core/easyrec_metrics/distribute_metrics_impl_pai.py [2476:2565]:
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def recall_at_k(labels,
                predictions,
                k,
                class_id=None,
                weights=None,
                metrics_collections=None,
                updates_collections=None,
                name=None):
  """Computes recall@k of the predictions with respect to sparse labels.

  If `class_id` is specified, we calculate recall by considering only the
      entries in the batch for which `class_id` is in the label, and computing
      the fraction of them for which `class_id` is in the top-k `predictions`.
  If `class_id` is not specified, we'll calculate recall as how often on
      average a class among the labels of a batch entry is in the top-k
      `predictions`.

  `sparse_recall_at_k` creates two local variables,
  `true_positive_at_<k>` and `false_negative_at_<k>`, that are used to compute
  the recall_at_k frequency. This frequency is ultimately returned as
  `recall_at_<k>`: an idempotent operation that simply divides
  `true_positive_at_<k>` by total (`true_positive_at_<k>` +
  `false_negative_at_<k>`).

  For estimation of the metric over a stream of data, the function creates an
  `update_op` operation that updates these variables and returns the
  `recall_at_<k>`. Internally, a `top_k` operation computes a `Tensor`
  indicating the top `k` `predictions`. Set operations applied to `top_k` and
  `labels` calculate the true positives and false negatives weighted by
  `weights`. Then `update_op` increments `true_positive_at_<k>` and
  `false_negative_at_<k>` using these values.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels] or [D1, ... DN], where the latter implies
      num_labels=1. N >= 1 and num_labels is the number of target classes for
      the associated prediction. Commonly, N=1 and `labels` has shape
      [batch_size, num_labels]. [D1, ... DN] must match `predictions`. Values
      should be in range [0, num_classes), where num_classes is the last
      dimension of `predictions`. Values outside this range always count
      towards `false_negative_at_<k>`.
    predictions: Float `Tensor` with shape [D1, ... DN, num_classes] where
      N >= 1. Commonly, N=1 and predictions has shape [batch size, num_classes].
      The final dimension contains the logit values for each class. [D1, ... DN]
      must match `labels`.
    k: Integer, k for @k metric.
    class_id: Integer class ID for which we want binary metrics. This should be
      in range [0, num_classes), where num_classes is the last dimension of
      `predictions`. If class_id is outside this range, the method returns NAN.
    weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
      `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
      dimensions must be either `1`, or the same as the corresponding `labels`
      dimension).
    metrics_collections: An optional list of collections that values should
      be added to.
    updates_collections: An optional list of collections that updates should
      be added to.
    name: Name of new update operation, and namespace for other dependent ops.

  Returns:
    recall: Scalar `float64` `Tensor` with the value of `true_positives` divided
      by the sum of `true_positives` and `false_negatives`.
    update_op: `Operation` that increments `true_positives` and
      `false_negatives` variables appropriately, and whose value matches
      `recall`.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match
    `predictions`, or if either `metrics_collections` or `updates_collections`
    are not a list or tuple.
    RuntimeError: If eager execution is enabled.
  """
  if context.executing_eagerly():
    raise RuntimeError('tf.metrics.recall_at_k is not '
                       'supported when eager execution is enabled.')

  with ops.name_scope(name, _at_k_name('recall', k, class_id=class_id),
                      (predictions, labels, weights)) as scope:
    _, top_k_idx = nn.top_k(predictions, k)
    return recall_at_top_k(
        labels=labels,
        predictions_idx=top_k_idx,
        k=k,
        class_id=class_id,
        weights=weights,
        metrics_collections=metrics_collections,
        updates_collections=updates_collections,
        name=scope)
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easy_rec/python/core/easyrec_metrics/distribute_metrics_impl_tf.py [2488:2577]:
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def recall_at_k(labels,
                predictions,
                k,
                class_id=None,
                weights=None,
                metrics_collections=None,
                updates_collections=None,
                name=None):
  """Computes recall@k of the predictions with respect to sparse labels.

  If `class_id` is specified, we calculate recall by considering only the
      entries in the batch for which `class_id` is in the label, and computing
      the fraction of them for which `class_id` is in the top-k `predictions`.
  If `class_id` is not specified, we'll calculate recall as how often on
      average a class among the labels of a batch entry is in the top-k
      `predictions`.

  `sparse_recall_at_k` creates two local variables,
  `true_positive_at_<k>` and `false_negative_at_<k>`, that are used to compute
  the recall_at_k frequency. This frequency is ultimately returned as
  `recall_at_<k>`: an idempotent operation that simply divides
  `true_positive_at_<k>` by total (`true_positive_at_<k>` +
  `false_negative_at_<k>`).

  For estimation of the metric over a stream of data, the function creates an
  `update_op` operation that updates these variables and returns the
  `recall_at_<k>`. Internally, a `top_k` operation computes a `Tensor`
  indicating the top `k` `predictions`. Set operations applied to `top_k` and
  `labels` calculate the true positives and false negatives weighted by
  `weights`. Then `update_op` increments `true_positive_at_<k>` and
  `false_negative_at_<k>` using these values.

  If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

  Args:
    labels: `int64` `Tensor` or `SparseTensor` with shape
      [D1, ... DN, num_labels] or [D1, ... DN], where the latter implies
      num_labels=1. N >= 1 and num_labels is the number of target classes for
      the associated prediction. Commonly, N=1 and `labels` has shape
      [batch_size, num_labels]. [D1, ... DN] must match `predictions`. Values
      should be in range [0, num_classes), where num_classes is the last
      dimension of `predictions`. Values outside this range always count
      towards `false_negative_at_<k>`.
    predictions: Float `Tensor` with shape [D1, ... DN, num_classes] where
      N >= 1. Commonly, N=1 and predictions has shape [batch size, num_classes].
      The final dimension contains the logit values for each class. [D1, ... DN]
      must match `labels`.
    k: Integer, k for @k metric.
    class_id: Integer class ID for which we want binary metrics. This should be
      in range [0, num_classes), where num_classes is the last dimension of
      `predictions`. If class_id is outside this range, the method returns NAN.
    weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of
      `labels`. If the latter, it must be broadcastable to `labels` (i.e., all
      dimensions must be either `1`, or the same as the corresponding `labels`
      dimension).
    metrics_collections: An optional list of collections that values should
      be added to.
    updates_collections: An optional list of collections that updates should
      be added to.
    name: Name of new update operation, and namespace for other dependent ops.

  Returns:
    recall: Scalar `float64` `Tensor` with the value of `true_positives` divided
      by the sum of `true_positives` and `false_negatives`.
    update_op: `Operation` that increments `true_positives` and
      `false_negatives` variables appropriately, and whose value matches
      `recall`.

  Raises:
    ValueError: If `weights` is not `None` and its shape doesn't match
    `predictions`, or if either `metrics_collections` or `updates_collections`
    are not a list or tuple.
    RuntimeError: If eager execution is enabled.
  """
  if context.executing_eagerly():
    raise RuntimeError('tf.metrics.recall_at_k is not '
                       'supported when eager execution is enabled.')

  with ops.name_scope(name, _at_k_name('recall', k, class_id=class_id),
                      (predictions, labels, weights)) as scope:
    _, top_k_idx = nn.top_k(predictions, k)
    return recall_at_top_k(
        labels=labels,
        predictions_idx=top_k_idx,
        k=k,
        class_id=class_id,
        weights=weights,
        metrics_collections=metrics_collections,
        updates_collections=updates_collections,
        name=scope)
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