# fmi.md

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| fmi | R Documentation |

### Fowlkes Mallows Index

#### Description

A generic S3 function to compute the *fowlkes mallows index* score for a\
classification model. This function dispatches to S3 methods in `fmi()`\
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 `fmi()` 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 `fmi()` in a "safe" validator that checks\
for NA values and matching length, for example:

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

```r
safe_fmi <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  fmi(x, y, ...)
}
```

{% endcode %}

Apply the same pattern to any custom metric functions to ensure input\
sanity before calling the underlying `C++` code.

**Efficient multi-metric evaluation**

For multiple performance evaluations of a classification model, first\
compute the confusion matrix once via `cmatrix()`. All other performance\
metrics can then be derived from this one object via S3 dispatching:

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

```r
## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate fowlkes mallows index
## via S3 dispatching
fmi(confusion_matrix)

## additional performance metrics
## below
```

{% endcode %}

The `fmi.factor()` method calls `cmatrix()` internally, so explicitly\
invoking `fmi.cmatrix()` yourself avoids duplicate computation, yielding\
significant speed and memory effciency gains when you need multiple\
evaluation metrics.

#### Usage

```r
## Generic S3 method
## for Fowlkes Mallows Index
fmi(...)

## Generic S3 method
## for weighted Fowlkes Mallows Index
weighted.fmi(...)
```

#### Arguments

| `...` | <p>Arguments passed on to <code>fmi.factor</code>,<code>weighted.fmi.factor</code>, <code>fmi.cmatrix</code></p><p><code>actual,predicted</code></p><p>A pair of \<integer> or \<factor> vectors of length <code>n</code>, and <code>k</code> levels.</p><p><code>w</code></p><p>A \<double> vector of sample weights.</p><p><code>x</code></p><p>A confusion matrix created <code>cmatrix()</code>.</p> |
| ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

#### Value

A \<double>-value

#### 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
## and predicted classes
actual_classes <- factor(
x = sample(x = classes, size = 1e3, replace = TRUE),
levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
x = sample(x = classes, size = 1e3, replace = TRUE),
levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::fmi(
   actual    = actual_classes, 
   predicted = predicted_classes
)
```

```

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