def normalized_discounted_cumulative_gain(
labels,
predictions,
weights=None,
topn=None,
name=None,
gain_fn=_DEFAULT_GAIN_FN,
rank_discount_fn=_DEFAULT_RANK_DISCOUNT_FN):
"""Computes normalized discounted cumulative gain (NDCG).
Args:
labels: A `Tensor` of the same shape as `predictions`.
predictions: A `Tensor` with shape [batch_size, list_size]. Each value is
the ranking score of the corresponding example.
weights: A `Tensor` of the same shape of predictions or [batch_size, 1]. The
former case is per-example and the latter case is per-list.
topn: A cutoff for how many examples to consider for this metric.
name: A string used as the name for this metric.
gain_fn: (function) Transforms labels.
rank_discount_fn: (function) The rank discount function.
Returns:
A metric for the weighted normalized discounted cumulative gain of the
batch.
"""
metric = metrics_impl.NDCGMetric(name, topn, gain_fn, rank_discount_fn)
with tf.compat.v1.name_scope(metric.name,
'normalized_discounted_cumulative_gain',
(labels, predictions, weights)):
per_list_ndcg, per_list_weights = metric.compute(
labels, predictions, weights)
return tf.compat.v1.metrics.mean(per_list_ndcg, per_list_weights)
def normalized_discounted_cumulative_gain_NEW(
labels,
predictions,
weights=None,
topn=None,
name=None,
gain_fn=_DEFAULT_GAIN_FN,
rank_discount_fn=_DEFAULT_RANK_DISCOUNT_FN):
metric = metrics_impl.NDCGMetric(name, topn, gain_fn, rank_discount_fn)
with tf.compat.v1.name_scope(metric.name,
'normalized_discounted_cumulative_gain_NEW',
(labels, predictions, weights)):
per_list_ndcg, per_list_weights = metric.compute(
labels, predictions, weights)
return tf.compat.v1.metrics.mean(per_list_ndcg, per_list_weights)
def __init__(self,
name=None,
topn=None,
gain_fn=_DEFAULT_GAIN_FN,
rank_discount_fn=_DEFAULT_RANK_DISCOUNT_FN,
dtype=None,
**kwargs):
super(NDCGMetric, self).__init__(name=name, dtype=dtype, **kwargs)
self._topn = topn
self._gain_fn = gain_fn
self._rank_discount_fn = rank_discount_fn
self._metric = metrics_impl.NDCGMetric(
name=name,
topn=topn,
gain_fn=gain_fn,
rank_discount_fn=rank_discount_fn)
def __init__(self,
name=None,
topn=None,
gain_fn=None,
rank_discount_fn=None,
dtype=None,
ragged=False,
**kwargs):
super(NDCGMetric, self).__init__(name=name, dtype=dtype, **kwargs)
self._topn = topn
self._gain_fn = gain_fn or utils.pow_minus_1
self._rank_discount_fn = rank_discount_fn or utils.log2_inverse
self._metric = metrics_impl.NDCGMetric(
name=name,
topn=topn,
gain_fn=self._gain_fn,
rank_discount_fn=self._rank_discount_fn,
ragged=ragged)
def compute_mean(metric_key,
labels,
predictions,
weights=None,
topn=None,
name=None):
"""Returns the mean of the specified metric given the inputs.
Args:
metric_key: A key in `RankingMetricKey`.
labels: A `Tensor` of the same shape as `predictions` representing
relevance.
predictions: A `Tensor` with shape [batch_size, list_size]. Each value is
the ranking score of the corresponding example.
weights: A `Tensor` of the same shape of predictions or [batch_size, 1]. The
former case is per-example and the latter case is per-list.
topn: An `integer` specifying the cutoff of how many items are considered in
the metric.
name: A `string` used as the name for this metric.
Returns:
A scalar as the computed metric.
"""
metric_dict = {
RankingMetricKey.ARP: metrics_impl.ARPMetric(metric_key),
RankingMetricKey.MRR: metrics_impl.MRRMetric(metric_key, topn),
RankingMetricKey.NDCG: metrics_impl.NDCGMetric(name, topn),
RankingMetricKey.DCG: metrics_impl.DCGMetric(name, topn),
RankingMetricKey.PRECISION: metrics_impl.PrecisionMetric(name, topn),
RankingMetricKey.RECALL: metrics_impl.RecallMetric(name, topn),
RankingMetricKey.MAP:
metrics_impl.MeanAveragePrecisionMetric(name, topn),
RankingMetricKey.ORDERED_PAIR_ACCURACY: metrics_impl.OPAMetric(name),
RankingMetricKey.BPREF: metrics_impl.BPrefMetric(name, topn),
RankingMetricKey.HITS: metrics_impl.HitsMetric(metric_key, topn),
}
assert metric_key in metric_dict, ('metric_key %s not supported.' %
metric_key)
# TODO: Add mask argument for metric.compute() call
metric, weight = metric_dict[metric_key].compute(labels, predictions,
weights)
return tf.compat.v1.div_no_nan(tf.reduce_sum(input_tensor=metric * weight),
tf.reduce_sum(input_tensor=weight))