PERF: Improve performance of Series.isin() on sets #25812

kykosic · 2019-03-21T00:23:25Z

closes pandas.Series.isin() is slow on large sets due to conversion of set to list #25507
tests added / passed
passes git diff upstream/master -u -- "*.py" | flake8 --diff
whatsnew entry

Issue #25507 pointed out that using DataFrame.isin or Series.isin on a set took extremely long due to the set being first converted to a list then back to a hash table to lookup values. This fix eliminates that conversion and simply uses the set to directly test membership.

Sample code from issue:

import pandas as pd

squares = set(a**2 for a in range(10000000))
series = pd.Series(range(100))

def test_isin():
    _ = series.isin(squares)

%timeit test_isin()

Output:

Before:
3.89 s ± 66.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

After:
134 µs ± 11.3 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

There are already several unit tests covering this function on sets with different dtypes. Additionally, I added an additional asv benchmark for sets:

series_methods.IsIn.time_isin
======== =============
dtype
-------- -------------
int64    1.46±0.04ms
uint64    1.76±0.1ms
object    3.53±0.2ms
======== =============

series_methods.IsIn.time_isin_set
======== =============
dtype
-------- -------------
int64    1.58±0.03ms
uint64   1.61±0.06ms
object   3.24±0.04ms
======== =============

If the additional benchmark is overkill I can of course remove it.

jreback · 2019-03-21T00:26:46Z

pandas/core/algorithms.py

@@ -395,6 +395,14 @@ def isin(comps, values):
                        " to isin(), you passed a [{values_type}]"
                        .format(values_type=type(values).__name__))

+    # GH 25507
+    # if `values` is a set, directly use it instead of hashing a list
+    if isinstance(values, set):


i think we have an is_set_like (otherwise pls create one)

I didn't find an existing one so I have added it.

codecov · 2019-03-21T01:03:16Z

Codecov Report

Merging #25812 into master will increase coverage by <.01%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master   #25812      +/-   ##
==========================================
+ Coverage   91.27%   91.27%   +<.01%     
==========================================
  Files         173      173              
  Lines       53002    53007       +5     
==========================================
+ Hits        48375    48381       +6     
+ Misses       4627     4626       -1

Flag	Coverage Δ
#multiple	`89.83% <100%> (ø)`	⬆️
#single	`41.76% <20%> (-0.01%)`	⬇️

Impacted Files	Coverage Δ
pandas/core/algorithms.py	`94.83% <100%> (+0.03%)`	⬆️
pandas/util/testing.py	`89.4% <0%> (+0.1%)`	⬆️

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codecov · 2019-03-21T01:03:17Z

Codecov Report

Merging #25812 into master will increase coverage by 0.14%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master   #25812      +/-   ##
==========================================
+ Coverage    91.3%   91.45%   +0.14%     
==========================================
  Files         173      172       -1     
  Lines       53004    52899     -105     
==========================================
- Hits        48397    48380      -17     
+ Misses       4607     4519      -88

Flag	Coverage Δ
#multiple	`90.02% <100%> (+0.14%)`	⬆️
#single	`41.82% <42.85%> (+0.05%)`	⬆️

Impacted Files	Coverage Δ
pandas/core/dtypes/common.py	`96.78% <ø> (ø)`	⬆️
pandas/core/dtypes/inference.py	`98.46% <100%> (+0.04%)`	⬆️
pandas/core/algorithms.py	`94.83% <100%> (+0.03%)`	⬆️
pandas/io/feather_format.py	`89.47% <0%> (-0.27%)`	⬇️
pandas/core/computation/engines.py	`88.7% <0%> (-0.18%)`	⬇️
pandas/plotting/_tools.py	`78.65% <0%> (-0.12%)`	⬇️
pandas/util/testing.py	`89.3% <0%> (-0.11%)`	⬇️
pandas/io/excel/_xlrd.py	`93.93% <0%> (-0.1%)`	⬇️
pandas/core/tools/datetimes.py	`84.59% <0%> (-0.05%)`	⬇️
pandas/core/computation/align.py	`97.82% <0%> (-0.03%)`	⬇️
... and 14 more

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Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 9e27f12...88752e2. Read the comment docs.

jreback · 2019-03-21T11:21:27Z

pandas/core/algorithms.py

@@ -395,6 +395,14 @@ def isin(comps, values):
                        " to isin(), you passed a [{values_type}]"
                        .format(values_type=type(values).__name__))

+    # GH 25507


I would like you to integrate this with the other impl., meaning skip the set-ifying if its a set, but otherwise dispatch the actual isin operation. I suspect this will work only in some of the tests cases (but we aren't fully testing it here).

I'm not sure what you mean by the other implementation or the actual isin operator. Do you mean let the rest of this function run but without the type cast? Or attempt to call comps.isin? It seems that would fail if comps was an ndarray.

what i mean is you can skip the list-ifying if its a set, but don't actually do the comp in values. as I said I suspect this actually doesn't work (some tests are failing) and if we add sets to a fair number of tests they will fail. The reason is that these all must be the same types exactly e.g. comp is often a int while values maybe be an np.int. we already fully handle this path, so you need to fit in.

The tests that failed are unrelated to my changes (conda connection issues on setup). I will add more tests for set values, but dispatching the current path of isin will destroy the performance gain this change intended to fix.

I can't find any situations which cause this change to fail. The int vs np.int works fine, and I can create tests around such cases.

you are creating a new path which makes the codes more complex ; we already have too many paths here

Okay then feel free to close this pull request; the current path cannot support this performance change.

as I said this needs to integrate with the existing isin tests. this needs to happen before this patch is considered.

So the new patch works well for every test case except NaN types. The current paths ensure that an array containing NaN when compared against a collection with NaN in it returns True. However using Python sets NaN != NaN so it will return False (not sure of the reasoning, but I think current implementation goes against IEEE).

This means that .isin({np.nan}) and .isin([np.nan]) will have different results, and I don't know of a way to resolve this without introducing an O(n) check that destroys the performance fix.

Closing this pull request as I don't think we can implement this performance change without this regression.

jreback · 2019-03-21T11:21:56Z

pandas/core/algorithms.py

+    if is_set_like(values):
+        result = np.empty_like(comps, dtype=np.bool)
+        for i, comp in enumerate(comps):
+            result[i] = comp in values


for each test in test_algorithms for isin, add an arg that also is a set (for as many tests as possible)

jreback · 2019-03-22T11:49:11Z

pandas/core/algorithms.py

@@ -395,6 +395,14 @@ def isin(comps, values):
                        " to isin(), you passed a [{values_type}]"
                        .format(values_type=type(values).__name__))

+    # GH 25507


what i mean is you can skip the list-ifying if its a set, but don't actually do the comp in values. as I said I suspect this actually doesn't work (some tests are failing) and if we add sets to a fair number of tests they will fail. The reason is that these all must be the same types exactly e.g. comp is often a int while values maybe be an np.int. we already fully handle this path, so you need to fit in.

jreback · 2019-03-23T20:25:20Z

pandas/core/algorithms.py

@@ -395,6 +395,14 @@ def isin(comps, values):
                        " to isin(), you passed a [{values_type}]"
                        .format(values_type=type(values).__name__))

+    # GH 25507


as I said this needs to integrate with the existing isin tests. this needs to happen before this patch is considered.

Kyle Kosic added 2 commits March 20, 2019 20:02

isin performance fix

b50b193

update whatsnew

a083384

jreback requested changes Mar 21, 2019

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Kyle Kosic added 2 commits March 20, 2019 21:03

add is_set_like

4722e54

isort fix

e0f40f3

WillAyd added the Performance Memory or execution speed performance label Mar 21, 2019

jreback requested changes Mar 21, 2019

View reviewed changes

jreback added the Reshaping Concat, Merge/Join, Stack/Unstack, Explode label Mar 21, 2019

jreback requested changes Mar 22, 2019

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resolve merge conflict

e29b2e9

jreback requested changes Mar 23, 2019

View reviewed changes

isin tests for sets

88752e2

kykosic closed this Mar 24, 2019

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PERF: Improve performance of Series.isin() on sets #25812

PERF: Improve performance of Series.isin() on sets #25812

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kykosic commented Mar 21, 2019 •

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