metrax.MSLE

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

Bases: Average

Computes the mean squared logarithmic error for regression problems given predictions and labels.

The mean squared logarithmic error is defined as:

\[MSLE = \frac{1}{N} \sum_{i=1}^{N} (ln(y_i + 1) - ln(\hat{y}_i + 1))^2\]

where:

• \(y_i\) are true values

• \(\hat{y}_i\) are predictions

• \(N\) is the number of samples

__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[, ...])	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

total
count

classmethod from_model_output(predictions: Array, labels: Array, sample_weights: Array | None = None) → MSLE

Updates the metric.

Parameters:

• predictions – A floating point 1D vector representing the prediction generated from the model. The shape should be (batch_size,).

• labels – True value. The shape should be (batch_size,).

• sample_weights – An optional floating point 1D vector representing the weight of each sample. The shape should be (batch_size,).

Returns:

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

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

replace(**updates)

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