NDCG (normalized discounted cumulative gain) metric.

Inherits From: Metric

Calculates NDCG@k for a given set of top_k values calculated from a list of gains (relevance scores) that are sorted based on the associated predictions. The top_k_list can be passed as part of the NDCG metric config or using tfma.MetricsSpec.binarize.top_k_list if configuring multiple top_k metrics. The gain (relevance score) is determined from the value stored in the 'gain_key' feature. The value of NDCG@k returned is a weighted average of NDCG@k over the set of queries using the example weights.

NDCG@k = (DCG@k for the given rank)/(DCG@k DCG@k = sum_{i=1}^k gain_i/log_2(i+1), where gain_i is the gain (relevance score) of the i^th ranked response, indexed from 1.

This is a query/ranking based metric so a query_key must also be provided in the associated tfma.MetricsSpec.

gain_key Key of feature in features dictionary that holds gain values.
top_k_list Values for top k. This can also be set using the tfma.MetricsSpec.binarize.top_k_list associated with the metric.
name Metric name.

compute_confidence_interval Whether to compute confidence intervals for this metric.

Note that this may not completely remove the computational overhead involved in computing a given metric. This is only respected by the jackknife confidence interval method.



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Creates computations associated with metric.


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Returns serializable config.