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use std::f32;
use std::cmp::{min, Ordering};
use array::prelude::*;
pub fn dcg_score(y_true: &Array, y_hat: &Array, k: i32) -> f32 {
assert!(y_true.rows() == y_hat.rows());
let mut pairs: Vec<_> = y_hat.data().iter().enumerate().collect();
pairs.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(Ordering::Equal));
let mut out: f32 = 0.0;
let last = min(k as usize, y_true.rows());
for i in 0..last {
let (orig_idx, _) = pairs[i];
let gain = 2f32.powf(y_true.data()[orig_idx]);
let discount = ((i as f32) + 2.0).log2();
out += gain / discount;
}
out
}
pub fn ndcg_score(y_true: &Array, y_hat: &Array, k: i32) -> f32 {
assert!(y_true.rows() == y_hat.rows());
let best = dcg_score(y_true, y_hat, k);
let actual = dcg_score(y_true, y_hat, k);
actual / best
}
fn counts_at_score(y_true: &[f32], y_hat: &[f32]) -> (Vec<f32>, Vec<f32>) {
let mut pairs: Vec<_> = y_hat.iter().cloned().zip(y_true.iter().cloned()).collect();
pairs.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap_or(Ordering::Equal));
let mut score_prev = ::std::f32::NAN;
let (mut tp, mut fp) = (0.0f32, 0.0f32);
let (mut tps, mut fps) = (vec![], vec![]);
for (score, label) in pairs {
if score != score_prev {
tps.push(tp);
fps.push(fp);
score_prev = score;
}
tp += label;
fp += 1.0 - label;
}
tps.push(tp);
fps.push(fp);
(tps, fps)
}
fn rates_at_score(y_true: &[f32], y_hat: &[f32]) -> (Vec<f32>, Vec<f32>) {
let (mut true_positive_count, mut false_positive_count) = counts_at_score(y_true, y_hat);
let true_positives = true_positive_count[true_positive_count.len() - 1];
let false_positives = false_positive_count[false_positive_count.len() - 1];
for (tp, fp) in true_positive_count.iter_mut()
.zip(false_positive_count.iter_mut()) {
*tp /= true_positives;
*fp /= false_positives;
}
(true_positive_count, false_positive_count)
}
fn trapezoidal(x: &[f32], y: &[f32]) -> f32 {
let mut prev_x = *x.first().unwrap();
let mut prev_y = *y.first().unwrap();
let mut integral = 0.0;
for (&x, &y) in x.iter().skip(1).zip(y.iter().skip(1)) {
integral += (x - prev_x) * (prev_y + y) / 2.0;
prev_x = x;
prev_y = y;
}
integral
}
fn check_roc_auc_inputs(y_true: &Array, y_hat: &Array) -> Result<(), &'static str> {
if y_true.cols() != 1 || y_hat.cols() != 1 {
return Err("Input array has more than one column.");
}
if y_true.rows() != y_hat.rows() {
return Err("Unequal number of rows");
}
if y_true.rows() < 1 {
return Err("Inputs are empty.");
}
let mut pos_present = false;
let mut neg_present = false;
for &y in y_true.data() {
match y {
0.0 => {
neg_present = true;
}
1.0 => {
pos_present = true;
}
_ => return Err("Invalid labels: target data is not either 0.0 or 1.0"),
}
}
if !pos_present || !neg_present {
return Err("Both classes must be present.");
}
Ok(())
}
pub fn roc_auc_score(y_true: &Array, y_hat: &Array) -> Result<f32, &'static str> {
try!(check_roc_auc_inputs(y_true, y_hat));
let (tpr, fpr) = rates_at_score(y_true.data(), y_hat.data());
Ok(trapezoidal(&fpr, &tpr))
}
#[cfg(test)]
mod tests {
use prelude::*;
use super::{counts_at_score, roc_auc_score, dcg_score, ndcg_score};
#[test]
fn basic() {
let y_true = vec![1.0, 1.0, 0.0, 0.0];
let y_hat = vec![0.5, 0.2, 0.3, -1.0];
let (x, y) = counts_at_score(&y_true, &y_hat);
let x_expected: Vec<f32> = vec![0.0, 1.0, 1.0, 2.0, 2.0];
let y_expected: Vec<f32> = vec![0.0, 0.0, 1.0, 1.0, 2.0];
assert!(allclose(&Array::from(x), &Array::from(x_expected)));
assert!(allclose(&Array::from(y), &Array::from(y_expected)));
assert!(close(0.75,
roc_auc_score(&Array::from(y_true), &Array::from(y_hat)).unwrap()));
}
#[test]
fn test_dcg_basic() {
let winner_1 = &Array::from(vec![5.0, 3.0, 2.0]);
let winner_2 = &Array::from(vec![4.0, 3.0, 2.0]);
let loser = &Array::from(vec![2.0, 1.0, 0.0]);
let score_1 = dcg_score(winner_1, loser, 10);
let score_2 = dcg_score(winner_2, loser, 10);
assert!(score_1 > score_2);
assert!(dcg_score(winner_1, loser, 2) > dcg_score(winner_2, loser, 2));
}
#[test]
fn test_dcg_sample_order() {
let r1 = &Array::from(vec![5.0, 3.0, 2.0]);
let r2 = &Array::from(vec![2.0, 1.0, 0.0]);
let r3 = &Array::from(vec![2.0, 3.0, 5.0]);
let r4 = &Array::from(vec![0.0, 1.0, 2.0]);
assert!(dcg_score(r1, r2, 10) == dcg_score(r3, r4, 10));
}
#[test]
fn test_ndcg_ideal() {
let r1 = &Array::from(vec![5.0, 3.0, 2.0]);
let r2 = &Array::from(vec![2.0, 1.0, 0.0]);
let r3 = &Array::from(vec![2.0, 3.0, 5.0]);
let r4 = &Array::from(vec![0.0, 1.0, 2.0]);
assert!(close(1.0, ndcg_score(r1, r1, 10)));
assert!(close(1.0, ndcg_score(r3, r4, 10)));
}
#[test]
fn basic_repeated() {
let y_true = vec![1.0, 1.0, 0.0, 0.0];
let y_hat = vec![0.5, 0.5, -1.0, 0.5];
assert!(close(0.75,
roc_auc_score(&Array::from(y_true), &Array::from(y_hat)).unwrap()));
}
}