metrax.PrecisionAtK

class metrax.PrecisionAtK(total: Array, count: Array)

Bases: TopKRankingMetric

Computes P@k (precision at k) metrics.

Precision at k (P@k) is a metric that measures the proportion of relevant items found in the top k recommendations. It answers the question: “Out of the K items recommended, how many are actually relevant?”

Given the top \(K\) recommendations, P@K is calculated as:

\[Precision@K = \frac{\text{Number of relevant items in top K}}{K}\]

__init__(total: Array, count: Array) → None

Methods

__init__(total, count)
compute()	Computes final metrics from intermediate values.
compute_value()	Wraps compute() and returns a values.Value.
empty()	Returns an empty instance (i.e. .merge(Metric.empty()) is a no-op).
from_fun(fun)	Calls cls.from_model_output with the return value from fun.
from_model_output(predictions, labels, ks)	Creates a metric instance from model output.
from_output(name)	Calls cls.from_model_output with model output named name.
merge(other)	Returns Metric that is the accumulation of self and other.
reduce()	Reduces the metric along it first axis by calling _reduce_merge().
replace(**updates)	Returns a new object replacing the specified fields with new values.

Attributes

total
count

__init__(total: Array, count: Array) → None

replace(**updates)

Returns a new object replacing the specified fields with new values.