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Diffstat (limited to 'library/test/src/stats.rs')
| -rw-r--r-- | library/test/src/stats.rs | 319 |
1 files changed, 319 insertions, 0 deletions
diff --git a/library/test/src/stats.rs b/library/test/src/stats.rs new file mode 100644 index 00000000000..077005371c0 --- /dev/null +++ b/library/test/src/stats.rs @@ -0,0 +1,319 @@ +#![allow(missing_docs)] +#![allow(deprecated)] // Float + +use std::cmp::Ordering::{self, Equal, Greater, Less}; +use std::mem; + +#[cfg(test)] +mod tests; + +fn local_cmp(x: f64, y: f64) -> Ordering { + // arbitrarily decide that NaNs are larger than everything. + if y.is_nan() { + Less + } else if x.is_nan() { + Greater + } else if x < y { + Less + } else if x == y { + Equal + } else { + Greater + } +} + +fn local_sort(v: &mut [f64]) { + v.sort_by(|x: &f64, y: &f64| local_cmp(*x, *y)); +} + +/// Trait that provides simple descriptive statistics on a univariate set of numeric samples. +pub trait Stats { + /// Sum of the samples. + /// + /// Note: this method sacrifices performance at the altar of accuracy + /// Depends on IEEE-754 arithmetic guarantees. See proof of correctness at: + /// ["Adaptive Precision Floating-Point Arithmetic and Fast Robust Geometric + /// Predicates"][paper] + /// + /// [paper]: http://www.cs.cmu.edu/~quake-papers/robust-arithmetic.ps + fn sum(&self) -> f64; + + /// Minimum value of the samples. + fn min(&self) -> f64; + + /// Maximum value of the samples. + fn max(&self) -> f64; + + /// Arithmetic mean (average) of the samples: sum divided by sample-count. + /// + /// See: <https://en.wikipedia.org/wiki/Arithmetic_mean> + fn mean(&self) -> f64; + + /// Median of the samples: value separating the lower half of the samples from the higher half. + /// Equal to `self.percentile(50.0)`. + /// + /// See: <https://en.wikipedia.org/wiki/Median> + fn median(&self) -> f64; + + /// Variance of the samples: bias-corrected mean of the squares of the differences of each + /// sample from the sample mean. Note that this calculates the _sample variance_ rather than the + /// population variance, which is assumed to be unknown. It therefore corrects the `(n-1)/n` + /// bias that would appear if we calculated a population variance, by dividing by `(n-1)` rather + /// than `n`. + /// + /// See: <https://en.wikipedia.org/wiki/Variance> + fn var(&self) -> f64; + + /// Standard deviation: the square root of the sample variance. + /// + /// Note: this is not a robust statistic for non-normal distributions. Prefer the + /// `median_abs_dev` for unknown distributions. + /// + /// See: <https://en.wikipedia.org/wiki/Standard_deviation> + fn std_dev(&self) -> f64; + + /// Standard deviation as a percent of the mean value. See `std_dev` and `mean`. + /// + /// Note: this is not a robust statistic for non-normal distributions. Prefer the + /// `median_abs_dev_pct` for unknown distributions. + fn std_dev_pct(&self) -> f64; + + /// Scaled median of the absolute deviations of each sample from the sample median. This is a + /// robust (distribution-agnostic) estimator of sample variability. Use this in preference to + /// `std_dev` if you cannot assume your sample is normally distributed. Note that this is scaled + /// by the constant `1.4826` to allow its use as a consistent estimator for the standard + /// deviation. + /// + /// See: <http://en.wikipedia.org/wiki/Median_absolute_deviation> + fn median_abs_dev(&self) -> f64; + + /// Median absolute deviation as a percent of the median. See `median_abs_dev` and `median`. + fn median_abs_dev_pct(&self) -> f64; + + /// Percentile: the value below which `pct` percent of the values in `self` fall. For example, + /// percentile(95.0) will return the value `v` such that 95% of the samples `s` in `self` + /// satisfy `s <= v`. + /// + /// Calculated by linear interpolation between closest ranks. + /// + /// See: <http://en.wikipedia.org/wiki/Percentile> + fn percentile(&self, pct: f64) -> f64; + + /// Quartiles of the sample: three values that divide the sample into four equal groups, each + /// with 1/4 of the data. The middle value is the median. See `median` and `percentile`. This + /// function may calculate the 3 quartiles more efficiently than 3 calls to `percentile`, but + /// is otherwise equivalent. + /// + /// See also: <https://en.wikipedia.org/wiki/Quartile> + fn quartiles(&self) -> (f64, f64, f64); + + /// Inter-quartile range: the difference between the 25th percentile (1st quartile) and the 75th + /// percentile (3rd quartile). See `quartiles`. + /// + /// See also: <https://en.wikipedia.org/wiki/Interquartile_range> + fn iqr(&self) -> f64; +} + +/// Extracted collection of all the summary statistics of a sample set. +#[derive(Debug, Clone, PartialEq, Copy)] +#[allow(missing_docs)] +pub struct Summary { + pub sum: f64, + pub min: f64, + pub max: f64, + pub mean: f64, + pub median: f64, + pub var: f64, + pub std_dev: f64, + pub std_dev_pct: f64, + pub median_abs_dev: f64, + pub median_abs_dev_pct: f64, + pub quartiles: (f64, f64, f64), + pub iqr: f64, +} + +impl Summary { + /// Construct a new summary of a sample set. + pub fn new(samples: &[f64]) -> Summary { + Summary { + sum: samples.sum(), + min: samples.min(), + max: samples.max(), + mean: samples.mean(), + median: samples.median(), + var: samples.var(), + std_dev: samples.std_dev(), + std_dev_pct: samples.std_dev_pct(), + median_abs_dev: samples.median_abs_dev(), + median_abs_dev_pct: samples.median_abs_dev_pct(), + quartiles: samples.quartiles(), + iqr: samples.iqr(), + } + } +} + +impl Stats for [f64] { + // FIXME #11059 handle NaN, inf and overflow + fn sum(&self) -> f64 { + let mut partials = vec![]; + + for &x in self { + let mut x = x; + let mut j = 0; + // This inner loop applies `hi`/`lo` summation to each + // partial so that the list of partial sums remains exact. + for i in 0..partials.len() { + let mut y: f64 = partials[i]; + if x.abs() < y.abs() { + mem::swap(&mut x, &mut y); + } + // Rounded `x+y` is stored in `hi` with round-off stored in + // `lo`. Together `hi+lo` are exactly equal to `x+y`. + let hi = x + y; + let lo = y - (hi - x); + if lo != 0.0 { + partials[j] = lo; + j += 1; + } + x = hi; + } + if j >= partials.len() { + partials.push(x); + } else { + partials[j] = x; + partials.truncate(j + 1); + } + } + let zero: f64 = 0.0; + partials.iter().fold(zero, |p, q| p + *q) + } + + fn min(&self) -> f64 { + assert!(!self.is_empty()); + self.iter().fold(self[0], |p, q| p.min(*q)) + } + + fn max(&self) -> f64 { + assert!(!self.is_empty()); + self.iter().fold(self[0], |p, q| p.max(*q)) + } + + fn mean(&self) -> f64 { + assert!(!self.is_empty()); + self.sum() / (self.len() as f64) + } + + fn median(&self) -> f64 { + self.percentile(50_f64) + } + + fn var(&self) -> f64 { + if self.len() < 2 { + 0.0 + } else { + let mean = self.mean(); + let mut v: f64 = 0.0; + for s in self { + let x = *s - mean; + v = v + x * x; + } + // N.B., this is _supposed to be_ len-1, not len. If you + // change it back to len, you will be calculating a + // population variance, not a sample variance. + let denom = (self.len() - 1) as f64; + v / denom + } + } + + fn std_dev(&self) -> f64 { + self.var().sqrt() + } + + fn std_dev_pct(&self) -> f64 { + let hundred = 100_f64; + (self.std_dev() / self.mean()) * hundred + } + + fn median_abs_dev(&self) -> f64 { + let med = self.median(); + let abs_devs: Vec<f64> = self.iter().map(|&v| (med - v).abs()).collect(); + // This constant is derived by smarter statistics brains than me, but it is + // consistent with how R and other packages treat the MAD. + let number = 1.4826; + abs_devs.median() * number + } + + fn median_abs_dev_pct(&self) -> f64 { + let hundred = 100_f64; + (self.median_abs_dev() / self.median()) * hundred + } + + fn percentile(&self, pct: f64) -> f64 { + let mut tmp = self.to_vec(); + local_sort(&mut tmp); + percentile_of_sorted(&tmp, pct) + } + + fn quartiles(&self) -> (f64, f64, f64) { + let mut tmp = self.to_vec(); + local_sort(&mut tmp); + let first = 25_f64; + let a = percentile_of_sorted(&tmp, first); + let second = 50_f64; + let b = percentile_of_sorted(&tmp, second); + let third = 75_f64; + let c = percentile_of_sorted(&tmp, third); + (a, b, c) + } + + fn iqr(&self) -> f64 { + let (a, _, c) = self.quartiles(); + c - a + } +} + +// Helper function: extract a value representing the `pct` percentile of a sorted sample-set, using +// linear interpolation. If samples are not sorted, return nonsensical value. +fn percentile_of_sorted(sorted_samples: &[f64], pct: f64) -> f64 { + assert!(!sorted_samples.is_empty()); + if sorted_samples.len() == 1 { + return sorted_samples[0]; + } + let zero: f64 = 0.0; + assert!(zero <= pct); + let hundred = 100_f64; + assert!(pct <= hundred); + if pct == hundred { + return sorted_samples[sorted_samples.len() - 1]; + } + let length = (sorted_samples.len() - 1) as f64; + let rank = (pct / hundred) * length; + let lrank = rank.floor(); + let d = rank - lrank; + let n = lrank as usize; + let lo = sorted_samples[n]; + let hi = sorted_samples[n + 1]; + lo + (hi - lo) * d +} + +/// Winsorize a set of samples, replacing values above the `100-pct` percentile +/// and below the `pct` percentile with those percentiles themselves. This is a +/// way of minimizing the effect of outliers, at the cost of biasing the sample. +/// It differs from trimming in that it does not change the number of samples, +/// just changes the values of those that are outliers. +/// +/// See: <http://en.wikipedia.org/wiki/Winsorising> +pub fn winsorize(samples: &mut [f64], pct: f64) { + let mut tmp = samples.to_vec(); + local_sort(&mut tmp); + let lo = percentile_of_sorted(&tmp, pct); + let hundred = 100_f64; + let hi = percentile_of_sorted(&tmp, hundred - pct); + for samp in samples { + if *samp > hi { + *samp = hi + } else if *samp < lo { + *samp = lo + } + } +} |