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### `tf.contrib.metrics.streaming_sparse_precision_at_top_k(top_k_predictions, labels, class_id=None, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_sparse_precision_at_top_k}
Computes precision@k of top-k predictions with respect to sparse labels.
If `class_id` is specified, we calculate precision by considering only the
entries in the batch for which `class_id` is in the top-k highest
`predictions`, and computing the fraction of them for which `class_id` is
indeed a correct label.
If `class_id` is not specified, we'll calculate precision as how often on
average a class among the top-k classes with the highest predicted values
of a batch entry is correct and can be found in the label for that entry.
`streaming_sparse_precision_at_top_k` creates two local variables,
`true_positive_at_k` and `false_positive_at_k`, that are used to compute
the precision@k frequency. This frequency is ultimately returned as
`precision_at_k`: an idempotent operation that simply divides
`true_positive_at_k` by total (`true_positive_at_k` + `false_positive_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
`precision_at_k`. Internally, set operations applied to `top_k_predictions`
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>`top_k_predictions`</b>: Integer `Tensor` with shape [D1, ... DN, k] where
N >= 1. Commonly, N=1 and top_k_predictions has shape [batch size, k].
The final dimension contains the indices of top-k labels. [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
`top_k_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>`class_id`</b>: 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.
* <b>`weights`</b>: An optional `Tensor` whose shape is broadcastable to 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>`precision`</b>: Scalar `float64` `Tensor` with the value of `true_positives`
divided by the sum of `true_positives` and `false_positives`.
* <b>`update_op`</b>: `Operation` that increments `true_positives` and
`false_positives` variables appropriately, and whose value matches
`precision`.
##### Raises:
* <b>`ValueError`</b>: 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.
* <b>`ValueError`</b>: If `top_k_predictions` has rank < 2.
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