metrax.Recall

class metrax.Recall(true_positives: Array, false_negatives: Array)

Bases: Metric

Computes recall for binary classification given predictions and labels.

It is calculated as:

\[Recall = \frac{TP}{TP + FN}\]

where:

• TP (True Positives): Number of correctly predicted positive cases

• FN (False Negatives): Number of incorrectly predicted negative cases

A threshold parameter (default 0.5) is used to convert probability predictions to binary predictions.

true_positives

The count of true positive instances from the given data, label, and threshold.

Type:

jax.Array

false_negatives

The count of false negative instances from the given data, label, and threshold.

Type:

jax.Array

__init__(true_positives: Array, false_negatives: Array) → None

Methods

__init__(true_positives, false_negatives)
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[, ...])	Updates the metric.
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

true_positives
false_negatives

true_positives: Array

false_negatives: Array

classmethod empty() → Recall

Returns an empty instance (i.e. .merge(Metric.empty()) is a no-op).

classmethod from_model_output(predictions: Array, labels: Array, threshold: float = 0.5) → Recall

Updates the metric.

Parameters:

• predictions – A floating point 1D vector whose values are in the range [0, 1]. The shape should be (batch_size,).

• labels – True value. The value is expected to be 0 or 1. The shape should be (batch_size,).

• threshold – The threshold to use for the binary classification.

Returns:

Updated Recall metric. The shape should be a single scalar.

Raises:

• ValueError – If type of labels is wrong or the shapes of predictions

• and labels are incompatible. –

merge(other: Recall) → Recall

Returns Metric that is the accumulation of self and other.

Parameters:

other – A Metric whose intermediate values should be accumulated onto the values of self. Note that in a distributed setting, other will typically be the output of a jax.lax parallel operator and thus have a dimension added to the dataclass returned by .from_model_output().

Returns:

A new Metric that accumulates the value from both self and other.

compute() → Array

Computes final metrics from intermediate values.

__init__(true_positives: Array, false_negatives: Array) → None

replace(**updates)

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