weighted.pr.curve.factor.md
weighted.pr.curve.factor
R Documentation
Precision Recall Curve
Description
A generic S3 function to compute the precision recall curve score for
a classification model. This function dispatches to S3 methods inpr.curve()
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 pr.curve()
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 preventR
-session crashes.
To guard against this, wrap pr.curve()
in a "safe" validator that
checks for NA values and matching length, for example:
safe_pr.curve <- function(x, y, ...) {
stopifnot(
!anyNA(x), !anyNA(y),
length(x) == length(y)
)
pr.curve(x, y, ...)
}
Apply the same pattern to any custom metric functions to ensure input
sanity before calling the underlying C++
code.
Area under the curve
Use auc.pr.curve for calculating the area under the curve directly.
Efficient multi-metric evaluation
To avoid sorting the same probability matrix multiple times (once per
class or curve), you can precompute a single set of sort indices and
pass it via the indices
argument. This reduces the overall cost from
O(K·N log N) to O(N log N + K·N).
## presort response
## probabilities
indices <- preorder(response, decreasing = TRUE)
## evaluate precision recall curve
pr.curve(actual, response, indices = indices)
Usage
## S3 method for class 'factor'
weighted.pr.curve(actual, response, w, thresholds = NULL, indices = NULL, ...)
Arguments
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.
w
A <double> vector of sample weights.
thresholds
An optional <double> vector of length n
(default: NULL).
indices
An optional n \times k
matrix of <integer> values of sorted response probability indices.
...
Arguments passed into other methods.
Value
A data.frame on the following form,
threshold
<numeric> Thresholds used to determine recall()
and precision()
level
<character> The level of the actual <factor>
label
<character> The levels of the actual <factor>
recall
<numeric> The recall
precision
<numeric> The precision
References
James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.
Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).
Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.
Examples
## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")
## Generate actual classes
## and response probabilities
actual_classes <- factor(
x = sample(
x = classes,
size = 1e2,
replace = TRUE,
prob = c(0.7, 0.3)
)
)
response_probabilities <- ifelse(
actual_classes == "Kebab",
rbeta(sum(actual_classes == "Kebab"), 2, 5),
rbeta(sum(actual_classes == "Falafel"), 5, 2)
)
## Construct response
## matrix
probability_matrix <- cbind(
response_probabilities,
1 - response_probabilities
)
sample_weights <- runif(1e2)
## Visualize
plot(
SLmetrics::weighted.pr.curve(
actual = actual_classes,
response = probability_matrix,
w = sample_weights
)
)
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