# P² quantile estimator rounding issue

Update: the estimator accuracy could be improved using a bunch of patches.

The P² quantile estimator is a sequential estimator that uses $$O(1)$$ memory. Thus, for the given sequence of numbers, it allows estimating quantiles without storing values. I already wrote a blog post about this approach and added its implementation in perfolizer. Recently, I got a bug report that revealed a flaw of the original paper. In this post, I’m going to briefly discuss this issue and the corresponding fix.

### Introduction

I already described the P² quantile estimator algorithm in one of the previous blog posts. This approach includes maintaining of five markers $$\{ n'_0, n'_1, n'_2, n'_3, n'_4 \}$$. If the considered sequence currently contains exactly $$n$$ elements, the marker values are defined as follows:

• $$n'_0 = 0$$
• $$n'_1 = (n - 1) p / 2$$
• $$n'_2 = (n - 1) p$$
• $$n'_3 = (n - 1) (1 + p) / 2$$
• $$n'_4 = (n - 1)$$

After adding another element in the sequence, the marker values should be invalidated. The original paper suggests using the marker increments to “reduce CPU overhead”:

• $$dn'_0 = 0$$
• $$dn'_1 = p / 2$$
• $$dn'_2 = p$$
• $$dn'_3 = (1 + p) / 2$$
• $$dn'_4 = 1$$

@AnthonyLloyd has reported that such approach has rounding issues. Let’s try to fix this problem and see how it affects the approach.

### Experiments

Let’s implement the classic approach with increments (P2QuantileEstimatorOriginal) and the new one without increments (P2QuantileEstimatorPatched):

public class P2QuantileEstimatorOriginal
{
private readonly int[] n = new int[5]; // marker positions
private readonly double[] ns = new double[5]; // desired marker positions
private readonly double[] dns = new double[5];
private readonly double[] q = new double[5]; // marker heights
private int count;

public P2QuantileEstimatorOriginal(double probability)
{
p = probability;
}

{
if (count < 5)
{
q[count++] = x;
if (count == 5)
{
Array.Sort(q);

for (int i = 0; i < 5; i++)
n[i] = i;

ns[0] = 0;
ns[1] = 2 * p;
ns[2] = 4 * p;
ns[3] = 2 + 2 * p;
ns[4] = 4;

dns[0] = 0;
dns[1] = p / 2;
dns[2] = p;
dns[3] = (1 + p) / 2;
dns[4] = 1;
}

return;
}

int k;
if (x < q[0])
{
q[0] = x;
k = 0;
}
else if (x < q[1])
k = 0;
else if (x < q[2])
k = 1;
else if (x < q[3])
k = 2;
else if (x < q[4])
k = 3;
else
{
q[4] = x;
k = 3;
}

for (int i = k + 1; i < 5; i++)
n[i]++;
for (int i = 0; i < 5; i++)
ns[i] += dns[i];

for (int i = 1; i <= 3; i++)
{
double d = ns[i] - n[i];
if (d >= 1 && n[i + 1] - n[i] > 1 || d <= -1 && n[i - 1] - n[i] < -1)
{
int dInt = Math.Sign(d);
double qs = Parabolic(i, dInt);
if (q[i - 1] < qs && qs < q[i + 1])
q[i] = qs;
else
q[i] = Linear(i, dInt);
n[i] += dInt;
}
}

count++;
}

private double Parabolic(int i, double d)
{
return q[i] + d / (n[i + 1] - n[i - 1]) * (
(n[i] - n[i - 1] + d) * (q[i + 1] - q[i]) / (n[i + 1] - n[i]) +
(n[i + 1] - n[i] - d) * (q[i] - q[i - 1]) / (n[i] - n[i - 1])
);
}

private double Linear(int i, int d)
{
return q[i] + d * (q[i + d] - q[i]) / (n[i + d] - n[i]);
}

public double GetQuantile()
{
if (count == 0)
throw new InvalidOperationException("Sequence contains no elements");
if (count <= 5)
{
Array.Sort(q, 0, count);
int index = (int)Math.Round((count - 1) * p);
return q[index];
}

return q[2];
}
}

public class P2QuantileEstimatorPatched
{
private readonly int[] n = new int[5]; // marker positions
private readonly double[] ns = new double[5]; // desired marker positions
private readonly double[] q = new double[5]; // marker heights
private int count;

public P2QuantileEstimatorPatched(double probability)
{
p = probability;
}

{
if (count < 5)
{
q[count++] = x;
if (count == 5)
{
Array.Sort(q);

for (int i = 0; i < 5; i++)
n[i] = i;

ns[0] = 0;
ns[1] = 2 * p;
ns[2] = 4 * p;
ns[3] = 2 + 2 * p;
ns[4] = 4;
}

return;
}

int k;
if (x < q[0])
{
q[0] = x;
k = 0;
}
else if (x < q[1])
k = 0;
else if (x < q[2])
k = 1;
else if (x < q[3])
k = 2;
else if (x < q[4])
k = 3;
else
{
q[4] = x;
k = 3;
}

for (int i = k + 1; i < 5; i++)
n[i]++;
ns[1] = count * p / 2;
ns[2] = count * p;
ns[3] = count * (1 + p) / 2;
ns[4] = count;

for (int i = 1; i <= 3; i++)
{
double d = ns[i] - n[i];
if (d >= 1 && n[i + 1] - n[i] > 1 || d <= -1 && n[i - 1] - n[i] < -1)
{
int dInt = Math.Sign(d);
double qs = Parabolic(i, dInt);
if (q[i - 1] < qs && qs < q[i + 1])
q[i] = qs;
else
q[i] = Linear(i, dInt);
n[i] += dInt;
}
}

count++;
}

private double Parabolic(int i, double d)
{
return q[i] + d / (n[i + 1] - n[i - 1]) * (
(n[i] - n[i - 1] + d) * (q[i + 1] - q[i]) / (n[i + 1] - n[i]) +
(n[i + 1] - n[i] - d) * (q[i] - q[i - 1]) / (n[i] - n[i - 1])
);
}

private double Linear(int i, int d)
{
return q[i] + d * (q[i + d] - q[i]) / (n[i + d] - n[i]);
}

public double GetQuantile()
{
if (count == 0)
throw new InvalidOperationException("Sequence contains no elements");
if (count <= 5)
{
Array.Sort(q, 0, count);
int index = (int)Math.Round((count - 1) * p);
return q[index];
}

return q[2];
}
}


The main change is replacing

for (int i = 0; i < 5; i++)
ns[i] += dns[i];


by

ns[1] = count * p / 2;
ns[2] = count * p;
ns[3] = count * (1 + p) / 2;
ns[4] = count;


Now let’s benchmark it using BenchmarkDotNet:

[LongRunJob]
public class P2Benchmarks
{
[Benchmark]
public void Original()
{
var estimator = new P2QuantileEstimatorOriginal(0.5);
for (int i = 0; i < 1_000_000; i++)
}

[Benchmark]
public void Patched()
{
var estimator = new P2QuantileEstimatorPatched(0.5);
for (int i = 0; i < 1_000_000; i++)
}
}

class Program
{
static void Main()
{
BenchmarkRunner.Run<P2Benchmarks>();
}
}


Here are the benchmark results:

BenchmarkDotNet=v0.13.1, OS=Windows 10.0.19042.1288 (20H2/October2020Update)
Intel Core i7-7700K CPU 4.20GHz (Kaby Lake), 1 CPU, 8 logical and 4 physical cores
.NET SDK=5.0.300
[Host]  : .NET 5.0.6 (5.0.621.22011), X64 RyuJIT
LongRun : .NET 5.0.6 (5.0.621.22011), X64 RyuJIT

|   Method |     Mean |    Error |   StdDev |
|--------- |---------:|---------:|---------:|
| Original | 24.47 ms | 0.028 ms | 0.138 ms |
|  Patched | 21.72 ms | 0.026 ms | 0.133 ms |


As we can see, the Patched version works even faster than the Original. Of course, the difference in performance could be explained by the manual loop unrolling and decreasing the number of operations (we do not update $$n'_0$$ in Patched since $$dn'_0 = 0$$). Of course, further optimizations are possible. The current implementation doesn’t aim to be the fastest one, it aims to be the readable one. The most important thing here is that the patch doesn’t lead to a performance regression. Meanwhile, it also reduces the memory overhead (we shouldn’t keep the dns array with five double elements anymore).

Now it’s time to check the impact on the estimator result. Let’s consider the following code snippet:

var random = new Random(1729);
var original = new P2QuantileEstimatorOriginal(0.6);
var patched = new P2QuantileEstimatorPatched(0.6);
for (int i = 0; i < 100; i++)
{
var x = random.NextDouble();
}

Console.WriteLine("Original : " + original.GetQuantile());
Console.WriteLine("Patched  : " + patched.GetQuantile());


Here is the corresponding output:

Original : 0.6094896389457989
Patched  : 0.6053711159656534


As we can see, the difference is noticeable.

### Conclusion

The suggested change:

• Improves performance keeping the same readability level
• Fixes the internal calculations

Thus, I have decided to update the corresponding implementation in perfolizer. The fix is available in perfolizer v0.3.0-nightly.106+. Here is the updated copy-pastable version of the reference implementation:

public class P2QuantileEstimator
{
private readonly int[] n = new int[5]; // marker positions
private readonly double[] ns = new double[5]; // desired marker positions
private readonly double[] q = new double[5]; // marker heights
private int count;

public P2QuantileEstimator(double probability)
{
p = probability;
}

{
if (count < 5)
{
q[count++] = x;
if (count == 5)
{
Array.Sort(q);

for (int i = 0; i < 5; i++)
n[i] = i;

ns[0] = 0;
ns[1] = 2 * p;
ns[2] = 4 * p;
ns[3] = 2 + 2 * p;
ns[4] = 4;
}

return;
}

int k;
if (x < q[0])
{
q[0] = x;
k = 0;
}
else if (x < q[1])
k = 0;
else if (x < q[2])
k = 1;
else if (x < q[3])
k = 2;
else if (x < q[4])
k = 3;
else
{
q[4] = x;
k = 3;
}

for (int i = k + 1; i < 5; i++)
n[i]++;
ns[1] = count * p / 2;
ns[2] = count * p;
ns[3] = count * (1 + p) / 2;
ns[4] = count;

for (int i = 1; i <= 3; i++)
{
double d = ns[i] - n[i];
if (d >= 1 && n[i + 1] - n[i] > 1 || d <= -1 && n[i - 1] - n[i] < -1)
{
int dInt = Math.Sign(d);
double qs = Parabolic(i, dInt);
if (q[i - 1] < qs && qs < q[i + 1])
q[i] = qs;
else
q[i] = Linear(i, dInt);
n[i] += dInt;
}
}

count++;
}

private double Parabolic(int i, double d)
{
return q[i] + d / (n[i + 1] - n[i - 1]) * (
(n[i] - n[i - 1] + d) * (q[i + 1] - q[i]) / (n[i + 1] - n[i]) +
(n[i + 1] - n[i] - d) * (q[i] - q[i - 1]) / (n[i] - n[i - 1])
);
}

private double Linear(int i, int d)
{
return q[i] + d * (q[i + d] - q[i]) / (n[i + d] - n[i]);
}

public double GetQuantile()
{
if (count == 0)
throw new InvalidOperationException("Sequence contains no elements");
if (count <= 5)
{
Array.Sort(q, 0, count);
int index = (int)Math.Round((count - 1) * p);
return q[index];
}

return q[2];
}
}


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