### `tf.contrib.metrics.streaming_sparse_average_precision_at_k(predictions, labels, k, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_sparse_average_precision_at_k}

Computes average precision@k of predictions with respect to sparse labels.

See `sparse_average_precision_at_k` for details on formula. `weights` are
applied to the result of `sparse_average_precision_at_k`

`streaming_sparse_average_precision_at_k` creates two local variables,
`average_precision_at_<k>/total` and `average_precision_at_<k>/max`, that
are used to compute the frequency. This frequency is ultimately returned as
`average_precision_at_<k>`: an idempotent operation that simply divides
`average_precision_at_<k>/total` by `average_precision_at_<k>/max`.

For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the
`precision_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 positives weighted by
`weights`. Then `update_op` increments `true_positive_at_<k>` and
`false_positive_at_<k>` using these values.

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

##### Args:


*  <b>`predictions`</b>: 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`.
*  <b>`labels`</b>: `int64` `Tensor` or `SparseTensor` with shape
    [D1, ... DN, num_labels], where 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 are ignored.
*  <b>`k`</b>: Integer, k for @k metric. This will calculate an average precision for
    range `[1,k]`, as documented above.
*  <b>`weights`</b>: An optional `Tensor` whose shape is broadcastable to the the first
    [D1, ... DN] dimensions of `predictions` and `labels`.
*  <b>`metrics_collections`</b>: An optional list of collections that values should
    be added to.
*  <b>`updates_collections`</b>: An optional list of collections that updates should
    be added to.
*  <b>`name`</b>: Name of new update operation, and namespace for other dependent ops.

##### Returns:


*  <b>`mean_average_precision`</b>: Scalar `float64` `Tensor` with the mean average
    precision values.
*  <b>`update`</b>: `Operation` that increments  variables appropriately, and whose
    value matches `metric`.

