Struct eval_metrics::classification::MultiConfusionMatrix

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pub struct MultiConfusionMatrix {
    pub dim: usize,
    pub counts: Vec<Vec<usize>>,
    // some fields omitted
}

Expand description

Confusion matrix for multi-class classification, in which rows represent predicted counts and columns represent labeled counts

Fields

dim: usize

output dimension

counts: Vec<Vec<usize>>

count data

Implementations

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impl MultiConfusionMatrix

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pub fn compute<T: Scalar>(
scores: &Vec<Vec<T>>,
labels: &Vec<usize>
) -> Result<MultiConfusionMatrix, EvalError>

Computes a new confusion matrix from the provided scores and labels

Arguments

scores - vector of class scores
labels - vector of class labels (indexed at zero)

An invalid input error will be returned if either scores or labels are empty, or if their lengths do not match. An undefined metric error will be returned if scores contain any value that is not finite.

Examples

use eval_metrics::classification::MultiConfusionMatrix;
let scores = vec![
   vec![0.3, 0.1, 0.6],
   vec![0.5, 0.2, 0.3],
   vec![0.2, 0.7, 0.1],
   vec![0.3, 0.3, 0.4],
   vec![0.5, 0.1, 0.4],
   vec![0.8, 0.1, 0.1],
   vec![0.3, 0.5, 0.2]
];
let labels = vec![2, 1, 1, 2, 0, 2, 0];
let matrix = MultiConfusionMatrix::compute(&scores, &labels)?;

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pub fn from_counts(
counts: Vec<Vec<usize>>
) -> Result<MultiConfusionMatrix, EvalError>

Constructs a multi confusion matrix with the provided counts

Arguments

counts - vector of vector of counts, where each inner vector represents a row in the confusion matrix

Errors

An invalid input error will be returned if the counts are not a square matrix, or if the counts are all zero

Examples

use eval_metrics::classification::MultiConfusionMatrix;
let counts = vec![
    vec![8, 3, 2],
    vec![1, 5, 3],
    vec![2, 1, 9]
];
let matrix = MultiConfusionMatrix::from_counts(counts)?;

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pub fn accuracy(&self) -> Result<f64, EvalError>

Computes accuracy

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pub fn precision(&self, avg: &Averaging) -> Result<f64, EvalError>

Computes precision, which necessarily requires a specified averaging method

Arguments

avg - averaging method, which can be either ‘Macro’ or ‘Weighted’

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pub fn recall(&self, avg: &Averaging) -> Result<f64, EvalError>

Computes recall, which necessarily requires a specified averaging method

Arguments

avg - averaging method, which can be either ‘Macro’ or ‘Weighted’

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pub fn f1(&self, avg: &Averaging) -> Result<f64, EvalError>

Computes F1, which necessarily requires a specified averaging method

Arguments

avg - averaging method, which can be either ‘Macro’ or ‘Weighted’

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pub fn rk(&self) -> Result<f64, EvalError>

Computes Rk, also known as the multi-class Matthews correlation coefficient following the approach of Gorodkin in “Comparing two K-category assignments by a K-category correlation coefficient” (2004)

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