logloss.md
logloss
R Documentation
Logarithmic Loss
Description
A generic S3 function to compute the logarithmic loss score for a
classification model. This function dispatches to S3 methods inlogloss()
and performs no input validation. If you supply NA values or
vectors of unequal length (e.g. length(x) != length(y)
), the
underlying C++
code may trigger undefined behavior and crash your R
session.
Defensive measures
Because logloss()
operates on raw pointers, pointer-level faults (e.g.
from NA or mismatched length) occur before any R
-level error handling.
Wrapping calls in try()
or tryCatch()
will not prevent R
-session
crashes.
To guard against this, wrap logloss()
in a "safe" validator that
checks for NA values and matching length, for example:
safe_logloss <- function(x, y, ...) {
stopifnot(
!anyNA(x), !anyNA(y),
length(x) == length(y)
)
logloss(x, y, ...)
}
Apply the same pattern to any custom metric functions to ensure input
sanity before calling the underlying C++
code.
Usage
## Generic S3 method
## for Logarithmic Loss
logloss(...)
## Generic S3 method
## for weighted Logarithmic Loss
weighted.logloss(...)
Arguments
...
Arguments passed on to logloss.integer
,logloss.factor
, weighted.logloss.integer
,weighted.logloss.factor
actual
A vector length n
, and k
levels. Can be of integer or factor.
response
A n \times k
<double>-matrix of
predicted probabilities. The i
-th row should
sum to 1 (i.e., a valid probability distribution over the k
classes). The first column corresponds to the
first factor level in actual
, the second column to the
second factor level, and so on.
normalize
A <logical>-value (default: TRUE). If TRUE, the mean cross-entropy across all observations is returned; otherwise, the sum of cross-entropies is returned.
w
A <double> vector of sample weights.
Value
A <double>
References
MacKay, David JC. Information theory, inference and learning algorithms. Cambridge university press, 2003.
Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for evolution strategies (2016): 45-53.
Virtanen, Pauli, et al. "SciPy 1.0: f'undamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.
Examples
## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")
## Generate actual
## and predicted response
## probabilities
actual_classes <- factor(
x = sample(x = classes, size = 1e3, replace = TRUE),
levels = c("Kebab", "Falafel")
)
response <- runif(n = 1e3)
## Logloss
SLmetrics::logloss(
actual = actual_classes,
response = cbind(
response,
1 - response
)
)
## Generate observed
## frequencies
actual_frequency <- sample(10L:100L, size = 1e3, replace = TRUE)
## Poisson Logloss
SLmetrics::logloss(
actual = actual_frequency,
response = response
)
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