# 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 in`pr.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* prevent`R`-session crashes.

To guard against this, wrap `pr.curve()` in a "safe" validator that\
checks for NA values and matching length, for example:

{% code overflow="wrap" lineNumbers="true" %}

```r
safe_pr.curve <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  pr.curve(x, y, ...)
}
```

{% endcode %}

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).

{% code overflow="wrap" lineNumbers="true" %}

```r
## presort response
## probabilities
indices <- preorder(response, decreasing = TRUE)

## evaluate precision recall curve
pr.curve(actual, response, indices = indices)
```

{% endcode %}

#### Usage

```r
## 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

```r
## 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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