# tf.compat.v1.metrics.precision_at_k

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.10.1/tensorflow/python/ops/metrics_impl.py#L3717-L3808) |

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

    tf.compat.v1.metrics.precision_at_k(
        labels,
        predictions,
        k,
        class_id=None,
        weights=None,
        metrics_collections=None,
        updates_collections=None,
        name=None
    )

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.

`precision_at_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, 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.

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| ## 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 are ignored. |
| `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. |

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| ## Returns ||
|---|---|
| `precision` | Scalar `float64` `Tensor` with the value of `true_positives` divided by the sum of `true_positives` and `false_positives`. |
| `update_op` | `Operation` that increments `true_positives` and `false_positives` variables appropriately, and whose value matches `precision`. |

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| ## Raises ||
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| `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. |

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