There's an ongoing effort to introduce quantile() into numpy. You'd use it just like percentile(), but would input your q value in probability space (0.5 for 50%):
https://github.com/numpy/numpy/pull/9213
Since there's a great deal of overlap between these two functions, we'd like to solicit opinions on how to move forward on this.
The current thinking is to tolerate the redundancy and keep both, using one as the engine for the other. I'm partial to having quantile because 1.) I prefer probability space, and 2.) I have a PR waiting on quantile().
Best,
C
I think that there would be a very good reason to have a separate function if we were to introduce weights to the inputs, similarly to the way that we have mean and average. This would have some (positive) repercussions like making weighted histograms with the Freedman-Diaconis binwidth estimator a possibility. I have had this change on the back-burner for a long time, mainly because I was too lazy to figure out how to include it in the C code. However, I will take a closer look.
Regards,
-Joe
On Fri, Jul 21, 2017 at 5:11 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
There's an ongoing effort to introduce quantile() into numpy. You'd use it just like percentile(), but would input your q value in probability space (0.5 for 50%):
https://github.com/numpy/numpy/pull/9213
Since there's a great deal of overlap between these two functions, we'd like to solicit opinions on how to move forward on this.
The current thinking is to tolerate the redundancy and keep both, using one as the engine for the other. I'm partial to having quantile because 1.) I prefer probability space, and 2.) I have a PR waiting on quantile().
Best,
C
NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
Just to provide some context, 9213 actually spawned off of this guy:
https://github.com/numpy/numpy/pull/9211
which might address the weighted inputs issue Joe brought up.
C
On Fri, Jul 21, 2017 at 2:21 PM, Joseph Fox-Rabinovitz < jfoxrabinovitz@gmail.com> wrote:
I think that there would be a very good reason to have a separate function if we were to introduce weights to the inputs, similarly to the way that we have mean and average. This would have some (positive) repercussions like making weighted histograms with the Freedman-Diaconis binwidth estimator a possibility. I have had this change on the back-burner for a long time, mainly because I was too lazy to figure out how to include it in the C code. However, I will take a closer look.
Regards,
-JoeOn Fri, Jul 21, 2017 at 5:11 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
There's an ongoing effort to introduce quantile() into numpy. You'd use it just like percentile(), but would input your q value in probability space (0.5 for 50%):
https://github.com/numpy/numpy/pull/9213
Since there's a great deal of overlap between these two functions, we'd like to solicit opinions on how to move forward on this.
The current thinking is to tolerate the redundancy and keep both, using one as the engine for the other. I'm partial to having quantile because 1.) I prefer probability space, and 2.) I have a PR waiting on quantile().
Best,
C
NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
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While #9211 is a good start, it is pretty inefficient in terms of the fact that it performs an O(nlogn) sort of the array. It is possible to reduce the time to O(n) by using a similar partitioning algorithm to the one in the C code of percentile. I will look into it as soon as I can.
-Joe
On Fri, Jul 21, 2017 at 5:34 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
Just to provide some context, 9213 actually spawned off of this guy:
https://github.com/numpy/numpy/pull/9211
which might address the weighted inputs issue Joe brought up.
C
On Fri, Jul 21, 2017 at 2:21 PM, Joseph Fox-Rabinovitz < jfoxrabinovitz@gmail.com> wrote:
I think that there would be a very good reason to have a separate function if we were to introduce weights to the inputs, similarly to the way that we have mean and average. This would have some (positive) repercussions like making weighted histograms with the Freedman-Diaconis binwidth estimator a possibility. I have had this change on the back-burner for a long time, mainly because I was too lazy to figure out how to include it in the C code. However, I will take a closer look.
Regards,
-JoeOn Fri, Jul 21, 2017 at 5:11 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
There's an ongoing effort to introduce quantile() into numpy. You'd use it just like percentile(), but would input your q value in probability space (0.5 for 50%):
https://github.com/numpy/numpy/pull/9213
Since there's a great deal of overlap between these two functions, we'd like to solicit opinions on how to move forward on this.
The current thinking is to tolerate the redundancy and keep both, using one as the engine for the other. I'm partial to having quantile because 1.) I prefer probability space, and 2.) I have a PR waiting on quantile().
Best,
C
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That would be great. I just used np.argsort because it was familiar to me. Didn't know about the C code.
On Fri, Jul 21, 2017 at 3:43 PM, Joseph Fox-Rabinovitz < jfoxrabinovitz@gmail.com> wrote:
While #9211 is a good start, it is pretty inefficient in terms of the fact that it performs an O(nlogn) sort of the array. It is possible to reduce the time to O(n) by using a similar partitioning algorithm to the one in the C code of percentile. I will look into it as soon as I can.
-JoeOn Fri, Jul 21, 2017 at 5:34 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
Just to provide some context, 9213 actually spawned off of this guy:
https://github.com/numpy/numpy/pull/9211
which might address the weighted inputs issue Joe brought up.
C
On Fri, Jul 21, 2017 at 2:21 PM, Joseph Fox-Rabinovitz < jfoxrabinovitz@gmail.com> wrote:
I think that there would be a very good reason to have a separate function if we were to introduce weights to the inputs, similarly to the way that we have mean and average. This would have some (positive) repercussions like making weighted histograms with the Freedman-Diaconis binwidth estimator a possibility. I have had this change on the back-burner for a long time, mainly because I was too lazy to figure out how to include it in the C code. However, I will take a closer look.
Regards,
-JoeOn Fri, Jul 21, 2017 at 5:11 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
There's an ongoing effort to introduce quantile() into numpy. You'd use it just like percentile(), but would input your q value in probability space (0.5 for 50%):
https://github.com/numpy/numpy/pull/9213
Since there's a great deal of overlap between these two functions, we'd like to solicit opinions on how to move forward on this.
The current thinking is to tolerate the redundancy and keep both, using one as the engine for the other. I'm partial to having quantile because 1.) I prefer probability space, and 2.) I have a PR waiting on quantile().
Best,
C
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Any way I can help expedite this?
On Fri, Jul 21, 2017 at 4:42 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
That would be great. I just used np.argsort because it was familiar to me. Didn't know about the C code.
On Fri, Jul 21, 2017 at 3:43 PM, Joseph Fox-Rabinovitz < jfoxrabinovitz@gmail.com> wrote:
While #9211 is a good start, it is pretty inefficient in terms of the fact that it performs an O(nlogn) sort of the array. It is possible to reduce the time to O(n) by using a similar partitioning algorithm to the one in the C code of percentile. I will look into it as soon as I can.
-JoeOn Fri, Jul 21, 2017 at 5:34 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
Just to provide some context, 9213 actually spawned off of this guy:
https://github.com/numpy/numpy/pull/9211
which might address the weighted inputs issue Joe brought up.
C
On Fri, Jul 21, 2017 at 2:21 PM, Joseph Fox-Rabinovitz < jfoxrabinovitz@gmail.com> wrote:
I think that there would be a very good reason to have a separate function if we were to introduce weights to the inputs, similarly to the way that we have mean and average. This would have some (positive) repercussions like making weighted histograms with the Freedman-Diaconis binwidth estimator a possibility. I have had this change on the back-burner for a long time, mainly because I was too lazy to figure out how to include it in the C code. However, I will take a closer look.
Regards,
-JoeOn Fri, Jul 21, 2017 at 5:11 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
There's an ongoing effort to introduce quantile() into numpy. You'd use it just like percentile(), but would input your q value in probability space (0.5 for 50%):
https://github.com/numpy/numpy/pull/9213
Since there's a great deal of overlap between these two functions, we'd like to solicit opinions on how to move forward on this.
The current thinking is to tolerate the redundancy and keep both, using one as the engine for the other. I'm partial to having quantile because 1.) I prefer probability space, and 2.) I have a PR waiting on quantile().
Best,
C
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Not that I know of. The algorithm is very simple, requiring a relatively small addition to the current introselect algorithm used for `np.partition`. My biggest hurdle is figuring out how the calling machinery really works so that I can figure out which input type permutations I need to generate, and how to get the right backend running for a given function call.
-Joe
On Thu, Aug 3, 2017 at 1:00 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
Any way I can help expedite this?
On Fri, Jul 21, 2017 at 4:42 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
That would be great. I just used np.argsort because it was familiar to me. Didn't know about the C code.
On Fri, Jul 21, 2017 at 3:43 PM, Joseph Fox-Rabinovitz jfoxrabinovitz@gmail.com wrote:
While #9211 is a good start, it is pretty inefficient in terms of the fact that it performs an O(nlogn) sort of the array. It is possible to reduce the time to O(n) by using a similar partitioning algorithm to the one in the C code of percentile. I will look into it as soon as I can.
-JoeOn Fri, Jul 21, 2017 at 5:34 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
Just to provide some context, 9213 actually spawned off of this guy:
https://github.com/numpy/numpy/pull/9211
which might address the weighted inputs issue Joe brought up.
C
On Fri, Jul 21, 2017 at 2:21 PM, Joseph Fox-Rabinovitz jfoxrabinovitz@gmail.com wrote:
I think that there would be a very good reason to have a separate function if we were to introduce weights to the inputs, similarly to the way that we have mean and average. This would have some (positive) repercussions like making weighted histograms with the Freedman-Diaconis binwidth estimator a possibility. I have had this change on the back-burner for a long time, mainly because I was too lazy to figure out how to include it in the C code. However, I will take a closer look.
Regards,
-JoeOn Fri, Jul 21, 2017 at 5:11 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
There's an ongoing effort to introduce quantile() into numpy. You'd use it just like percentile(), but would input your q value in probability space (0.5 for 50%):
https://github.com/numpy/numpy/pull/9213
Since there's a great deal of overlap between these two functions, we'd like to solicit opinions on how to move forward on this.
The current thinking is to tolerate the redundancy and keep both, using one as the engine for the other. I'm partial to having quantile because 1.) I prefer probability space, and 2.) I have a PR waiting on quantile().
Best,
C
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Cool. Just as a heads up, for my algorithm to work, I actually need the indices, which is why argsort() is so important to me. I use it to get both ap_sorted and ws_sorted variables. If your weighted-quantile algo is faster and doesn't require those indices, please by all means change my implementation. Thanks.
On Thu, Aug 3, 2017 at 11:10 AM, Joseph Fox-Rabinovitz < jfoxrabinovitz@gmail.com> wrote:
Not that I know of. The algorithm is very simple, requiring a relatively small addition to the current introselect algorithm used for `np.partition`. My biggest hurdle is figuring out how the calling machinery really works so that I can figure out which input type permutations I need to generate, and how to get the right backend running for a given function call.
-JoeOn Thu, Aug 3, 2017 at 1:00 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
Any way I can help expedite this?
On Fri, Jul 21, 2017 at 4:42 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
That would be great. I just used np.argsort because it was familiar to me. Didn't know about the C code.
On Fri, Jul 21, 2017 at 3:43 PM, Joseph Fox-Rabinovitz jfoxrabinovitz@gmail.com wrote:
While #9211 is a good start, it is pretty inefficient in terms of the fact that it performs an O(nlogn) sort of the array. It is possible to reduce the time to O(n) by using a similar partitioning algorithm to
the one
in the C code of percentile. I will look into it as soon as I can.
-JoeOn Fri, Jul 21, 2017 at 5:34 PM, Chun-Wei Yuan <chunwei.yuan@gmail.com
wrote:
Just to provide some context, 9213 actually spawned off of this guy:
https://github.com/numpy/numpy/pull/9211
which might address the weighted inputs issue Joe brought up.
C
On Fri, Jul 21, 2017 at 2:21 PM, Joseph Fox-Rabinovitz jfoxrabinovitz@gmail.com wrote:
I think that there would be a very good reason to have a separate function if we were to introduce weights to the inputs, similarly to
the way
that we have mean and average. This would have some (positive)
repercussions
like making weighted histograms with the Freedman-Diaconis binwidth estimator a possibility. I have had this change on the back-burner
for a
long time, mainly because I was too lazy to figure out how to
include it in
the C code. However, I will take a closer look.
Regards,
-JoeOn Fri, Jul 21, 2017 at 5:11 PM, Chun-Wei Yuan <
chunwei.yuan@gmail.com>
wrote: > > There's an ongoing effort to introduce quantile() into numpy. You'd > use it just like percentile(), but would input your q value in
probability
> space (0.5 for 50%): > > https://github.com/numpy/numpy/pull/9213 > > Since there's a great deal of overlap between these two functions, > we'd like to solicit opinions on how to move forward on this. > > The current thinking is to tolerate the redundancy and keep both, > using one as the engine for the other. I'm partial to having
quantile
> because 1.) I prefer probability space, and 2.) I have a PR waiting
on
> quantile(). > > Best, > > C > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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I will go over your PR carefully to make sure we can agree on a matching API. After that, we can swap the backend out whenever I get around to it.
Thanks for working on this.
-Joe
On Thu, Aug 3, 2017 at 5:36 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
Cool. Just as a heads up, for my algorithm to work, I actually need the indices, which is why argsort() is so important to me. I use it to get both ap_sorted and ws_sorted variables. If your weighted-quantile algo is faster and doesn't require those indices, please by all means change my implementation. Thanks.
On Thu, Aug 3, 2017 at 11:10 AM, Joseph Fox-Rabinovitz jfoxrabinovitz@gmail.com wrote:
Not that I know of. The algorithm is very simple, requiring a relatively small addition to the current introselect algorithm used for `np.partition`. My biggest hurdle is figuring out how the calling machinery really works so that I can figure out which input type permutations I need to generate, and how to get the right backend running for a given function call.
-JoeOn Thu, Aug 3, 2017 at 1:00 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
Any way I can help expedite this?
On Fri, Jul 21, 2017 at 4:42 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
That would be great. I just used np.argsort because it was familiar to me. Didn't know about the C code.
On Fri, Jul 21, 2017 at 3:43 PM, Joseph Fox-Rabinovitz jfoxrabinovitz@gmail.com wrote:
While #9211 is a good start, it is pretty inefficient in terms of the fact that it performs an O(nlogn) sort of the array. It is possible to reduce the time to O(n) by using a similar partitioning algorithm to the one in the C code of percentile. I will look into it as soon as I can.
-JoeOn Fri, Jul 21, 2017 at 5:34 PM, Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
Just to provide some context, 9213 actually spawned off of this guy:
https://github.com/numpy/numpy/pull/9211
which might address the weighted inputs issue Joe brought up.
C
On Fri, Jul 21, 2017 at 2:21 PM, Joseph Fox-Rabinovitz jfoxrabinovitz@gmail.com wrote: > > I think that there would be a very good reason to have a separate > function if we were to introduce weights to the inputs, similarly to > the way > that we have mean and average. This would have some (positive) > repercussions > like making weighted histograms with the Freedman-Diaconis binwidth > estimator a possibility. I have had this change on the back-burner > for a > long time, mainly because I was too lazy to figure out how to > include it in > the C code. However, I will take a closer look. > > Regards, > > -Joe > > > > On Fri, Jul 21, 2017 at 5:11 PM, Chun-Wei Yuan > chunwei.yuan@gmail.com > wrote: >> >> There's an ongoing effort to introduce quantile() into numpy. >> You'd >> use it just like percentile(), but would input your q value in >> probability >> space (0.5 for 50%): >> >> https://github.com/numpy/numpy/pull/9213 >> >> Since there's a great deal of overlap between these two functions, >> we'd like to solicit opinions on how to move forward on this. >> >> The current thinking is to tolerate the redundancy and keep both, >> using one as the engine for the other. I'm partial to having >> quantile >> because 1.) I prefer probability space, and 2.) I have a PR waiting >> on >> quantile(). >> >> Best, >> >> C >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@python.org >> https://mail.python.org/mailman/listinfo/numpy-discussion >> > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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Let’s try and keep this on topic - most replies to this message has been about #9211, which is an orthogonal issue.
There are two main questions here:
1. Would the community prefer to use np.quantile(x, 0.25) instead of np.percentile(x, 25), if they had the choice 2. Is this desirable enough to justify increasing the API surface?
The general consensus on the github issue answers yes to 1, but is neutral on 2. It would be good to get more opinions.
Eric
On Fri, 21 Jul 2017 at 16:12 Chun-Wei Yuan chunwei.yuan@gmail.com http://mailto:chunwei.yuan@gmail.com wrote:
There's an ongoing effort to introduce quantile() into numpy. You'd use it
just like percentile(), but would input your q value in probability space (0.5 for 50%):
https://github.com/numpy/numpy/pull/9213
Since there's a great deal of overlap between these two functions, we'd like to solicit opinions on how to move forward on this.
The current thinking is to tolerate the redundancy and keep both, using one as the engine for the other. I'm partial to having quantile because 1.) I prefer probability space, and 2.) I have a PR waiting on quantile().
Best,
C _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
I concur with the consensus.
On 10 Aug 2017, 11:10 PM +0200, Eric Wieser wieser.eric+numpy@gmail.com, wrote:
Let’s try and keep this on topic - most replies to this message has been about #9211, which is an orthogonal issue. There are two main questions here:
- Would the community prefer to use np.quantile(x, 0.25) instead of np.percentile(x, 25), if they had the choice
- Is this desirable enough to justify increasing the API surface?
The general consensus on the github issue answers yes to 1, but is neutral on 2. It would be good to get more opinions. Eric On Fri, 21 Jul 2017 at 16:12 Chun-Wei Yuan chunwei.yuan@gmail.com wrote:
There's an ongoing effort to introduce quantile() into numpy. You'd use it just like percentile(), but would input your q value in probability space (0.5 for 50%):
https://github.com/numpy/numpy/pull/9213
Since there's a great deal of overlap between these two functions, we'd like to solicit opinions on how to move forward on this.
The current thinking is to tolerate the redundancy and keep both, using one as the engine for the other. I'm partial to having quantile because 1.) I prefer probability space, and 2.) I have a PR waiting on quantile().
Best,
C _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
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On Thu, Aug 10, 2017 at 3:08 PM, Eric Wieser wieser.eric+numpy@gmail.com wrote:
Let’s try and keep this on topic - most replies to this message has been about #9211, which is an orthogonal issue.
There are two main questions here:
- Would the community prefer to use np.quantile(x, 0.25) instead of np.percentile(x,
25), if they had the choice 2. Is this desirable enough to justify increasing the API surface?
The general consensus on the github issue answers yes to 1, but is neutral on 2. It would be good to get more opinions.
I think a quantile function would be natural and desirable.
<snip>
Chuck
On Sun, Aug 13, 2017 at 9:28 AM, Charles R Harris <charlesr.harris@gmail.com
wrote:
On Thu, Aug 10, 2017 at 3:08 PM, Eric Wieser wieser.eric+numpy@gmail.com wrote:
Let’s try and keep this on topic - most replies to this message has been about #9211, which is an orthogonal issue.
There are two main questions here:
- Would the community prefer to use np.quantile(x, 0.25) instead of np.percentile(x,
25), if they had the choice 2. Is this desirable enough to justify increasing the API surface?
The general consensus on the github issue answers yes to 1, but is neutral on 2. It would be good to get more opinions.
I think a quantile function would be natural and desirable.
I'm in favor of adding it. (moving away from +0) It should be an obvious code completion choice, np.q?
Josef
<snip>
Chuck
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+1 on quantile()
-CHB
On Sun, Aug 13, 2017 at 6:28 AM, Charles R Harris <charlesr.harris@gmail.com
wrote:
On Thu, Aug 10, 2017 at 3:08 PM, Eric Wieser wieser.eric+numpy@gmail.com wrote:
Let’s try and keep this on topic - most replies to this message has been about #9211, which is an orthogonal issue.
There are two main questions here:
- Would the community prefer to use np.quantile(x, 0.25) instead of np.percentile(x,
25), if they had the choice 2. Is this desirable enough to justify increasing the API surface?
The general consensus on the github issue answers yes to 1, but is neutral on 2. It would be good to get more opinions.
I think a quantile function would be natural and desirable.
<snip>
Chuck
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Charles R Harris -
Chris Barker -
Chun-Wei Yuan -
Eric Wieser -
josef.pktd＠gmail.com -
Joseph Fox-Rabinovitz -
Juan Nunez-Iglesias