difference between java implementation and python

[idanmoradarthas]

I have
my_set.zip.

When I'm using the java code:

import com.tdunning.math.stats.TDigest;
import org.apache.commons.csv.CSVFormat;
import org.apache.commons.csv.CSVRecord;

import java.io.*;
import java.util.stream.StreamSupport;

public class TDigestTry {
    public static void main(String[] args) throws IOException {
        ClassLoader classLoader = TDigestTry.class.getClassLoader();
        File file = new File(classLoader.getResource("my_set.csv").getFile());
        Reader in = new FileReader(file);
        Iterable<CSVRecord> records = CSVFormat.EXCEL.withHeader().parse(in);
        TDigest digest = TDigest.createAvlTreeDigest(20);
        StreamSupport.stream(records.spliterator(), false)
                .map(record -> new Double(record.get("change rate")))
                .forEach(digest::add);
        System.out.println(digest.quantile(0.05));
        System.out.println(digest.quantile(0.95));
    }
}

I'm getting the results:

3.0
5.0

But when I'm this code:

from pathlib import Path

import pandas
from tdigest import TDigest

if __name__ == '__main__':
    frame = pandas.read_csv(Path(__file__).parents[0].joinpath("resources").joinpath("my_set.csv"))
    digest = TDigest()
    digest.batch_update(frame["change rate"].values)
    print(f"Quantile 0.05 = {digest.percentile(5)};\t\tQuantile 0.95 = {digest.percentile(95)}")

I'm getting the results:

Quantile 0.05 = 2.6495903059149586;		Quantile 0.95 = 3689686.790917569

How come there's a large difference between between the 0.95 quantiles?

P.S
same results when I use:

for value in frame["change rate"].values:
        digest.update(value)

[CamDavidsonPilon]

Looks strange - are you able to share the data?

[idanmoradarthas]

It attached in the second line

or link:

https://github.com/CamDavidsonPilon/tdigest/files/2613099/my_set.zip

[phucbui95]

Hi, i have a same issue here.

t.batch_update([1649.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0])
t.percentile(25)```
result was
`-523.5`

[rlele5]

Hi, I noticed this too. Thanks for everything you've done! In the cases it does work, it seems to be doing a great job! However, with this simple example posted above I'm now a little worried. Do you know when this error case occurs? Is this something inherent to TDigests? When should we watch out for something like this happening?

[rlele5]

@CamDavidsonPilon , @idanmoradarthas , @phucbui95 - I found where this regression was introduced:

commit 1f76113e3fce0d5b7189fd083085ba64d4129e05 (HEAD)
Author: Cameron Davidson-Pilon <cam.davidson.pilon@gmail.com>
Date:   Tue Dec 26 23:04:33 2017 -0500

    bump to v0.5, drop python2 support

The commit before returns the correct results:

Author: Cameron Davidson-Pilon <cam.davidson.pilon@gmail.com>
Date:   Mon Feb 20 11:22:54 2017 -0500

    test against 3.6

Fix: roll back the implementation of def percentile(self, p) back to the implementation in 9cd2536. @CamDavidsonPilon , are you able to explain this further? I still have to run your test cases to confirm, but this seems promising from some manual tests.

---

Examples:
Code:

from tdigest import TDigest
t = TDigest()
t.batch_update([1649.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0, 69.0])

for percent in [0, 25, 50, 75, 100]:
    value = t.percentile(percent)
    print(f"p{percent} = {value}")

Output with 1f76113 (broken):

p0 = 69.0
p25 = -1906.0
p50 = -1116.0
p75 = -326.0
p100 = 464.0

Output with 9cd2536 (working):

p0 = 69.0
p25 = 69.0
p50 = 69.0
p75 = 69.0
p100 = 1649.0

[rlele5]

Addendum - this may not be as simple as it seems, but may be a step in the right direction. I further compared a test case (bigJump) in the reference implementation (https://github.com/tdunning/t-digest/blob/ff3232bc25a69961fc7bf4911f8de0026bd28c44/core/src/test/java/com/tdunning/math/stats/TDigestTest.java)

Here is the java test - notice the assertions, then compare with the values with the working python implementation (9cd2536):

        TDigest digest = factory(100).create();
        for (int i = 1; i < 20; i++) {
            digest.add(i);
        }
        digest.add(1_000_000);

        assertEquals(18, digest.quantile(0.89999999), 0);
        assertEquals(19, digest.quantile(0.9), 0);
        assertEquals(19, digest.quantile(0.949999999), 0);
        assertEquals(1_000_000, digest.quantile(0.95), 0);

Working python implementation results (9cd2536):
Code:

from tdigest import TDigest

l = [i for i in range(20)]
l.append(1000000)
print(l)
t = TDigest()
t.batch_update(l)

for percent in [89.999999, 90, 94.9999999, 95,]:
    value = t.percentile(percent)
    print(f"p{percent} = {value}")

Output:

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1000000]
p89.999999 = 18.39999979
p90 = 18.400000000000002
p94.9999999 = 225014.93950018956
p95 = 225014.94999999963

Notice that the p94.9999999 and p95 results are still quite different from the reference implementation. This will probably only happen in extreme scenarios, but I thought I'd point it out. Perhaps the reference implementation has some other conditions that handle these extremities. I don't think the python implementation is wrong, but this just may be one of the accuracy tradeoffs made with TDigest (just a guess).

[CamDavidsonPilon]

@rlele5 unfortunately I'm not involved much in this repo anymore - if you suggest a fix, with an appropriate test, I'd be happy to review and merge it.

[rlele5]

@CamDavidsonPilon , sounds good. I'll have to go back and understand why the change was made in the first place and will see if there are any further changes needed.