tf.contrib.metrics.streaming_sparse_precision_at_k(predictions, labels, k, class_id=None, weights=None, metrics_collections=None, updates_collections=None, name=None)

tf.contrib.metrics.streaming_sparse_precision_at_k(predictions, labels, k, class_id=None, weights=None, metrics_collections=None, updates_collections=None, name=None)

See the guide: Metrics (contrib) > Metric Ops

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

If class_id is not specified, we calculate precision as the ratio of true positives (i.e., correct predictions, items in the top k highest predictions that are found in the corresponding row in labels) to positives (all top k predictions). If class_id is specified, we calculate precision by considering only the rows 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 in the corresponding row in labels.

We expect precision to decrease as k increases.

streaming_sparse_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.

Args:

• 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.
• labels: 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.
• 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:

• 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.

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.

Defined in tensorflow/contrib/metrics/python/ops/metric_ops.py.