tfma.metrics.NDCG

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NDCG (normalized discounted cumulative gain) metric.

Inherits From: Metric

tfma.metrics.NDCG(
    gain_key: str,
    top_k_list: Optional[List[int]] = None,
    name: str = NDCG_NAME
)

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.

Args
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.

Attributes
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.

Methods

computations

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computations(
    eval_config: Optional[tfma.EvalConfig] = None,
    schema: Optional[schema_pb2.Schema] = None,
    model_names: Optional[List[str]] = None,
    output_names: Optional[List[str]] = None,
    sub_keys: Optional[List[Optional[SubKey]]] = None,
    aggregation_type: Optional[AggregationType] = None,
    class_weights: Optional[Dict[int, float]] = None,
    example_weighted: bool = False,
    query_key: Optional[str] = None
) -> tfma.metrics.MetricComputations

Creates computations associated with metric.

from_config

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@classmethod
from_config(
    config: Dict[str, Any]
) -> 'Metric'

get_config

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get_config() -> Dict[str, Any]

Returns serializable config.